💥💥💥 How to start with PyTorch (machine learning) ?

PyTorch is a popular open-source machine learning framework that allows you to create and train neural networks with ease. PyTorch has a rich set of tutorials and documentation that can help you get started with the basics of PyTorch, such as tensors, datasets, models, autograd, optimization, and more. Here are some resources that you can use to learn PyTorch:

• Welcome to PyTorch Tutorials: This is the official PyTorch tutorial website that contains a variety of tutorials for different levels and domains of PyTorch users. You can find tutorials on topics such as vision, text, audio, reinforcement learning, production, etc. You can also run the tutorials on Google Colab or Microsoft Learn with a single click.
• Learn the Basics: This is a step-by-step guide to building a complete machine learning workflow with PyTorch. You will learn how to load data, build deep neural networks, train and save your models in this quickstart guide. This tutorial is also available in other languages, such as Korean and Japanese.
• Introduction to PyTorch - YouTube Series: This is a video series on YouTube that introduces the fundamental concepts of PyTorch through self-contained examples. You will learn how to use PyTorch to perform tasks such as image classification, text generation, style transfer, etc.

I hope these resources will help you get started with PyTorch and enjoy the process of learning and creating with machine learning. 

Can you recommend some books for learning PyTorch?

Sure, I can recommend some books for learning PyTorch. PyTorch is a powerful and flexible machine learning framework that allows you to create and train neural networks with ease. Whether you are a beginner or an expert, there are many books that can help you learn PyTorch and apply it to various domains and applications. Here are some of the best PyTorch books that I found on the web:

• Learn PyTorch for Deep Learning: Zero to Mastery book: This is an online book version of the Learn PyTorch for Deep Learning: Zero to Mastery course. This book will teach you the foundations of machine learning and deep learning with PyTorch through self-contained examples and exercises. You will learn how to use PyTorch to perform tasks such as image classification, text generation, style transfer, and more. This book is also available in other languages, such as Korean and Japanese.
• 20 Best PyTorch Books of All Time - BookAuthority: This is a list of 20 best PyTorch books recommended by experts such as Kirk Borne and Santiago. You can find books on topics such as vision, text, audio, reinforcement learning, production, etc. Some of the books in this list are:
  • Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools by Eli Stevens, Luca Antiga, and Thomas Viehmann. This book will teach you how to use PyTorch to build deep learning models from scratch or using pre-trained models. You will also learn how to use tools such as TorchVision, TorchText, and TorchAudio to work with different types of data.
  • [Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications] by Ian Pointer. This book will show you how to use PyTorch to create and deploy deep learning applications on various platforms such as web, mobile, and cloud. You will also learn how to use libraries such as Ignite, Hydra, and FastAPI to simplify your development process.
  • [Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning] by Delip Rao and Brian McMahan. This book will help you understand the concepts and techniques of natural language processing (NLP) using PyTorch. You will learn how to use PyTorch to build NLP applications such as sentiment analysis, machine translation, text summarization, and more.
• 12 Best New PyTorch Books To Read In 2023 - BookAuthority: This is a list of 12 new PyTorch books that you should read in 2023. These books cover the latest developments and trends in PyTorch and machine learning. Some of the books in this list are:
• [MLOps Engineering at Scale: Building Production-Ready Machine Learning Systems with Python] by Luca Bianchi and Francesco Pochetti. This book will teach you how to apply MLOps principles and practices to build scalable and reliable machine learning systems with Python. You will learn how to use tools such as PyTorch Lightning, MLflow, DVC, Kubeflow, and more to automate your machine learning workflow.
• [Scaling Machine Learning with Spark: Distributed Training with Apache Spark MLlib] by Alexey Grigorev. This book will show you how to use Apache Spark MLlib to scale your machine learning models using distributed computing. You will learn how to use Spark MLlib to train models on large datasets, integrate with other frameworks such as TensorFlow and PyTorch, and deploy your models on the cloud or on-premise.
• [PyTorch Computer Vision Cookbook: Over 70 recipes to master the art of computer vision with deep learning using Python] by Michael Avendi. This book will provide you with over 70 recipes to solve various computer vision problems using PyTorch. You will learn how to use PyTorch to work with images, videos, face detection, face recognition, object detection, object tracking, semantic segmentation, instance segmentation, pose estimation, action recognition, and more.

How to install PyTorch ?

There are different ways to install PyTorch on your system, depending on your preferences and requirements. PyTorch is a popular open-source machine learning framework that allows you to create and train neural networks with ease. PyTorch supports various operating systems, such as Windows, Linux, and Mac OS, and various package managers, such as Anaconda and pip. PyTorch also supports different versions of Python and CUDA, which are the main dependencies for PyTorch.

One of the easiest ways to install PyTorch is to use the official PyTorch website¹, which provides a simple interface to select your preferences and generate the install command. You can choose the PyTorch build (stable or preview), your operating system, your package manager (conda or pip), your language (Python or C++/Java), and your compute platform (CPU or CUDA). For example, if you want to install the stable version of PyTorch for Python 3.8 on Windows 10 with pip and CUDA 11.8, you can run the following command in your terminal:

pip3 install torch torchvision torchaudio --index-url [4](https://download.pytorch.org/whl/cu118)

Another way to install PyTorch is to use the Start Locally² page on the PyTorch website, which provides more detailed instructions for different operating systems and package managers. You can find the prerequisites, such as Python and CUDA versions, for each system and the steps to install PyTorch using conda or pip. For example, if you want to install PyTorch on Windows using Anaconda, you will need to open an Anaconda prompt and run the following command:

conda install pytorch torchvision torchaudio cudatoolkit=11.8 -c pytorch

A third way to install PyTorch is to follow a tutorial that guides you through the installation process and shows you how to use PyTorch for a specific task or application. For example, you can check out this tutorial³ by DigitalOcean, which teaches you how to install PyTorch on Ubuntu 20.04 using pip and how to use PyTorch for image classification. This approach can help you learn PyTorch while installing it and also validate your installation by running a short PyTorch program.

I hope these methods will help you install PyTorch successfully and enjoy the process of learning and creating with machine learning. 

How do I check if PyTorch is installed correctly?

There are several ways to check if PyTorch is installed correctly on your system. One of the easiest ways is to use Python code to import the torch library and check the version. You can do this by opening a terminal or command prompt and running the following commands:

python3 import torch; torch.__version__

The output should print the installed PyTorch version along with the CUDA version, if applicable. For example, 1.9.0+cu102 means the PyTorch version is 1.9.0, and the CUDA version is 10.2¹.

Another way to check if PyTorch is installed correctly is to use the package manager that you used to install PyTorch, such as pip or conda. You can use the pip list or conda list commands to see the list of installed packages and their versions. You can also use the pip show or conda info commands to get more detailed information about a specific package, such as PyTorch².

A third way to check if PyTorch is installed correctly is to run a simple PyTorch program that performs a basic operation, such as creating a tensor or performing a matrix multiplication. You can use your favorite Python IDE or code editor to write and run the program, or you can use an online platform such as Google Colab or Microsoft Learn that supports PyTorch. If the program runs without any errors and produces the expected output, then you can be confident that PyTorch is working properly on your system³.

What is a tensor in PyTorch?

In PyTorch, a tensor is a multi-dimensional matrix containing elements of a single data type. Tensors are the central data structures of PyTorch and are used for storing data and performing operations on them. They are similar to arrays in other programming languages but come with additional functionality to support the computation needs of machine learning and deep learning.

Tensors can be created from Python lists or sequences using the torch.tensor() constructor, which always copies data. If you have a numpy array and want to avoid a copy, you can use torch.as_tensor(). Tensors can also be created with specific sizes using various creation operations like torch.zeros(), torch.ones(), or torch.rand() for random initialization.

PyTorch defines several tensor types with CPU and GPU variants, such as 32-bit floating point (torch.float32), 64-bit floating point (torch.float64), 16-bit floating point (torch.float16), 8-bit integer (signed and unsigned), 16-bit integer, 32-bit integer, 64-bit integer, and Boolean tensors. This allows for flexibility in the precision and range of the numerical values stored in the tensors.

For more detailed information about tensors, including their properties and operations, you can refer to the PyTorch documentation or PyTorch tutorials that provide an in-depth introduction to the torch.Tensor class¹².

How do I perform operations on tensors in PyTorch?

PyTorch provides a variety of operations that you can perform on tensors, such as arithmetic, logical, linear algebra, reduction, indexing, slicing, joining, mutating, etc. You can use these operations to manipulate the shape, size, values, and data type of your tensors. PyTorch also supports automatic differentiation, which allows you to compute gradients of tensor operations with respect to some parameters.

To perform operations on tensors in PyTorch, you can use either the functional API or the method API. The functional API is a collection of functions that are attached to the torch module and take one or more tensors as input and return one or more tensors as output. For example, you can use the torch.add() function to add two tensors element-wise:

>>> a = torch.tensor( [ [1, 2], [3, 4]]) >>> b = torch.tensor( [ [5, 6], [7, 8]]) >>> c = torch.add(a, b) >>> c tensor ( [ [ 6, 8], [10, 12]])

The method API is a collection of methods that are attached to the tensor objects and modify the tensor in-place or return a new tensor. For example, you can use the add() method to add another tensor to the current tensor:

>>> a = torch.tensor( [ [1, 2], [3, 4]]) >>> b = torch.tensor( [ [5, 6], [7, 8]]) >>> a.add(b) tensor ( [ [ 6, 8], [10, 12]]) >>> a.add_(b) tensor ( [ [ 6, 8], [10, 12]]) >>> a tensor ( [ [ 6, 8], [10, 12]])

Note that methods that end with an underscore (_) modify the tensor in-place and do not return anything. Methods that do not end with an underscore return a new tensor and do not modify the original tensor.

For more information about the operations on tensors in PyTorch, you can refer to the following resources:

• The official PyTorch documentation¹ provides a comprehensive list of all the available tensor operations and their descriptions.
• The official PyTorch tutorials² provide an in-depth introduction to the basic concepts and features of PyTorch, such as tensors, autograd, models, etc.
• The Introduction to PyTorch Tensors³ tutorial provides a detailed explanation of how to create and manipulate tensors using various examples and exercises.
• The torch.Tensor page provides an overview of the torch.Tensor class and its properties and methods.

How do I compute gradients of tensor operations in PyTorch?

To compute gradients of tensor operations in PyTorch, you can use the torch.autograd module, which provides automatic differentiation for all operations on tensors. The torch.autograd module tracks the history of all operations involving tensors that have requires_grad=True and creates a directed acyclic graph (DAG) of the computations. When you call the backward() method on a tensor, the torch.autograd module traverses the DAG from that tensor and computes the gradients of all the tensors that have requires_grad=True using the chain rule.

For example, suppose you have two tensors x and y that have requires_grad=True, and you want to compute the gradient of the function z = x * y + x ** 2 with respect to both x and y. You can do this as follows:

import torch

x = torch.tensor(2.0, requires_grad=True) y = torch.tensor(3.0, requires_grad=True)

z = x * y + x ** 2 z.backward()

print(x.grad) # prints tensor(7.) print(y.grad) # prints tensor(2.)

The backward() method computes the gradient of z with respect to its inputs and stores them in the grad attribute of the tensors. You can access the gradients using the grad attribute or use them for further computations.

For more information about how to compute gradients in PyTorch, you can refer to the following resources:

• The official PyTorch documentation¹ provides a comprehensive overview of the torch.autograd module and its features.
• The official PyTorch tutorials² provide an in-depth introduction to the basic concepts and features of PyTorch, such as tensors, autograd, models, etc.
• The Introduction to PyTorch Tensors³ tutorial provides a detailed explanation of how to create and manipulate tensors using various examples and exercises.
• The PyTorch: Tensors and autograd⁴ tutorial provides a simple example of how to use PyTorch tensors and autograd to implement a polynomial function.

How do I use gradients for optimization in PyTorch?

To use gradients for optimization in PyTorch, you need to follow these steps:

• Create an optimizer object that will hold the current state and update the parameters based on the computed gradients. You can choose from various optimization algorithms that are available in the torch.optim module, such as Stochastic Gradient Descent (SGD), Adam, RMSprop, etc. You can also specify different options for each optimizer, such as the learning rate, weight decay, momentum, etc. For example, to create an SGD optimizer with a learning rate of 0.01 and a momentum of 0.9, you can use the following code:

optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

• In each iteration of your training loop, you need to perform the following substeps:
  • Clear the gradients of all optimized tensors by calling optimizer.zero_grad(). This is necessary because by default, gradients are accumulated in buffers (i.e., not overwritten) whenever .backward() is called.
  • Compute the loss using your model and the data. The loss is a scalar value that measures how well your model fits the data.
  • Call loss.backward() to compute the gradients of the loss with respect to the model parameters. This will populate the .grad attributes of the tensors that have requires_grad=True.
  • Call optimizer.step() to update the model parameters using the gradients. This will apply the optimization algorithm (e.g., SGD) to modify the values of the parameters.

For example, suppose you have a model called model, a loss function called loss_fn, and a dataset called dataset. You can use the following code to train your model using gradients and optimization:

for input, target in dataset: optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) loss.backward() optimizer.step()

For more information about how to use gradients and optimization in PyTorch, you can refer to the following resources:

• The official PyTorch documentation¹ provides a comprehensive overview of the torch.optim module and its features.
• The official PyTorch tutorials² provide an in-depth introduction to the basic concepts and features of PyTorch, such as tensors, autograd, models, etc.
• The Optimizing Model Parameters³ tutorial provides a simple example of how to train a neural network using PyTorch tensors, autograd, and optimizers.

How do I choose the right optimization algorithm in PyTorch?

Choosing the right optimization algorithm in PyTorch depends on several factors, such as the type and size of your model, the complexity and noise of your data, the speed and accuracy of your convergence, and the computational resources available. There is no definitive answer to this question, as different optimization algorithms may have different advantages and disadvantages for different scenarios. However, here are some general guidelines that may help you decide:

• If you have a simple and small model that can fit in memory, you can use the classic Stochastic Gradient Descent (SGD) algorithm, which updates the model parameters using a fixed learning rate and a mini-batch of data. SGD is easy to implement and understand, and can achieve good results with proper tuning of the learning rate and the batch size. However, SGD can be slow to converge and sensitive to the choice of the learning rate, which may require manual adjustment or annealing strategies. You can use the torch.optim.SGD class to implement SGD in PyTorch¹.
• If you have a complex and large model that requires adaptive learning rates for different parameters, you can use the Adam algorithm, which combines the ideas of momentum and adaptive gradient descent. Adam computes individual learning rates for each parameter based on the estimates of the first and second moments of the gradients. Adam can handle sparse gradients, noisy data, and high-dimensional problems well, and often converges faster than SGD. However, Adam may also suffer from some drawbacks, such as losing generalization ability, diverging in some cases, and requiring more memory. You can use the torch.optim.Adam class to implement Adam in PyTorch¹.
• If you have a non-linear least-squares problem that involves minimizing a sum of squared error terms, you can use the Gauss-Newton or Levenberg-Marquardt algorithms, which are based on linearizing the objective function around the current parameter values and solving a linear system to update the parameters. These algorithms can exploit the structure of the problem and achieve fast and accurate convergence. However, they may also face some challenges, such as requiring the computation of Jacobians or Hessians, being sensitive to outliers or ill-conditioning, and having difficulty with constraints or regularization. You can use the torchimize package to implement these algorithms in PyTorch².

These are some of the most common optimization algorithms that you can use in PyTorch, but there are also many other options that you can explore, such as RMSprop, Adagrad, Adadelta, Nesterov momentum, etc. You can find more information about these algorithms and their implementations in PyTorch in the following resources:

• The official PyTorch documentation¹ provides a comprehensive overview of the torch.optim module and its features.
• The official PyTorch tutorials³ provide an in-depth introduction to the basic concepts and features of PyTorch, such as tensors, autograd, models, etc.
• The torchimize package² provides implementations of the Gradient Descent, Gauss-Newton and Levenberg-Marquardt optimization algorithms using PyTorch.
• The Using Optimizers from PyTorch⁴ article provides a simple example of how to use PyTorch optimizers for linear regression.
• The torch.optimize · PyPI⁵ page provides information about how to install and use the torchimize package.

I hope these guidelines will help you choose the right optimization algorithm for your PyTorch model and improve your performance and results.

How do I tune the hyperparameters of an optimization algorithm in PyTorch?

To tune the hyperparameters of an optimization algorithm in PyTorch, you can use the Ray Tune library, which is an industry standard tool for distributed hyperparameter tuning. Ray Tune integrates with PyTorch and provides various features, such as:

• The latest hyperparameter search algorithms, such as random search, grid search, Bayesian optimization, population-based training, etc.
• Integration with TensorBoard and other analysis libraries for visualization and debugging.
• Native support for distributed training through Ray’s distributed machine learning engine.
• Checkpointing and fault tolerance for long-running experiments.

To use Ray Tune with PyTorch, you need to follow these steps:

• Install the ray[tune] and torchvision packages using pip or conda.
• Wrap your data loading and training code in functions that take a config dictionary as input. The config dictionary contains the hyperparameters that you want to tune, such as the learning rate, the batch size, the layer sizes, etc. You can also use the tune.report() function to report the metrics that you want to optimize, such as the loss or the accuracy.
• Define the search space for your hyperparameters using the tune.grid_search(), tune.uniform(), tune.loguniform(), or other sampling functions. You can also specify the number of trials, the stopping criteria, and the scheduler for your experiment.
• Create a tune.run() function call that passes your data loading and training functions, your search space, and other options. This will start the hyperparameter tuning process and return the best trial and its results.

For more details and examples on how to use Ray Tune with PyTorch, you can refer to the following resources:

• Hyperparameter tuning with Ray Tune: This is an official PyTorch tutorial that shows you how to integrate Ray Tune into your PyTorch training workflow using a CIFAR10 image classifier example.
• Ray Tune Documentation: This is the official documentation of Ray Tune that provides a comprehensive overview of its features and usage.
• Ray Tune Examples: This is a GitHub repository that contains various examples of using Ray Tune with different frameworks and applications, including PyTorch.

What is the difference between hyperparameters and parameters in PyTorch?

Hyperparameters and parameters are two types of variables that are involved in the design and training of a neural network in PyTorch. The main difference between them is that hyperparameters are set manually by the user before the training process, while parameters are learned automatically by the model during the training process.

Hyperparameters are the variables that control the behavior and performance of the model, such as the number and size of the layers, the learning rate, the batch size, the activation functions, the regularization methods, etc. Hyperparameters are not directly related to the data or the model structure, but they affect how the model learns from the data and how well it generalizes to new data. Hyperparameters are usually tuned by using various search methods, such as grid search, random search, or Bayesian optimization, to find the optimal values that minimize the loss function or maximize some evaluation metric.

Parameters are the variables that represent the weights and biases of the neural network, such as the values of the matrices and vectors that connect the layers. Parameters are directly related to the data and the model structure, and they determine how the model transforms the input into the output. Parameters are initialized randomly or with some predefined values, and then updated iteratively by using an optimization algorithm, such as stochastic gradient descent (SGD), Adam, or RMSprop, to reduce the error between the model output and the target output.

For more information about hyperparameters and parameters in PyTorch, you can refer to these resources:

• How to Grid Search Hyperparameters for PyTorch Models: This is a tutorial that shows you how to use grid search to find the best hyperparameters for a PyTorch model using a simple example.
• Optimizing Model Parameters: This is an official PyTorch tutorial that shows you how to use PyTorch optimizers to update the model parameters using a CIFAR10 image classifier example.
• What is the Difference Between a Parameter and a Hyperparameter?: This is an article that explains the difference between a parameter and a hyperparameter in machine learning with some examples.

💥💥💥 How to improve orthogonal quality in Ansys Workbench?

Orthogonal quality is a mesh metric that measures how well the mesh elements are aligned with the flow direction. A high orthogonal quality means that the mesh faces are perpendicular to the flow, which is desirable for accuracy and stability. A low orthogonal quality means that the mesh faces are skewed or distorted, which can lead to numerical errors and poor convergence.

There are several ways to improve orthogonal quality in Ansys Workbench, depending on the type and complexity of your geometry and mesh. Here are some general tips:

• Use a structured mesh if possible, especially for simple geometries that can be swept or extruded. Structured meshes have higher orthogonal quality than unstructured meshes, because they have regular and aligned elements.
• Use inflation layers near the walls or boundaries, to capture the boundary layer effects and reduce the aspect ratio of the elements. Inflation layers can improve the orthogonal quality by creating smooth transitions from fine to coarse mesh regions.
• Use mesh controls to refine or coarsen the mesh in specific areas, such as regions of high curvature, sharp corners, or complex features. Mesh controls can help you optimize the mesh size and distribution, and avoid creating small or distorted elements that can lower the orthogonal quality.
• Use mesh smoothing or optimization techniques, such as Laplacian smoothing, Winslow smoothing, or Robust Mesh Optimization. These techniques can improve the mesh quality by adjusting the node positions or element shapes, while preserving the geometry fidelity and element volume.

You can check the orthogonal quality of your mesh in Ansys Workbench by using the Element Quality Plots feature¹, which shows you the distribution and location of the elements with different quality values. You can also use the Question Answering feature² to get more information about orthogonal quality from web sources.

What is the difference between structured and unstructured mesh?

The difference between structured and unstructured mesh is that structured mesh has a regular and predictable pattern of elements and nodes, while unstructured mesh has an arbitrary and irregular pattern of elements and nodes. Structured mesh is easier to generate, store, and solve, but it may not capture complex geometries accurately. Unstructured mesh can conform to any geometry shape, but it requires more memory, computation time, and data structures. You can find more information about the types of mesh in the web search results I have provided¹²³⁴.

How do I choose between structured and unstructured mesh?

Choosing between structured and unstructured mesh depends on various factors related to your specific simulation needs. Here are some considerations to help you decide:

• Complexity of Geometry: If you have a complex geometry where the shape is critical to the design, an unstructured mesh is advantageous because it can conform to any geometry with localized resolutions¹.
• Localized Regions of High Resolution: For models that require high-resolution mesh in localized regions, an unstructured mesh allows for such targeted refinement¹.
• Ease of Generation: Structured meshes are generally easier and faster to generate due to their regular pattern, which can be beneficial for simpler geometries or when computation time is a concern¹.
• Memory Requirements: Structured meshes are more memory-efficient because they require less data to define connectivity between elements¹.
• Computation Time: Structured meshes can lead to faster numerical solutions due to their regularity, which simplifies the computation process¹.
• Numerical Accuracy: For simple geometries, structured meshes may provide more accurate solutions and reduce errors caused by distorted cells².

In summary, if your geometry is not overly complex and does not require high-resolution meshing in specific areas, a structured mesh will likely suffice and save on computation time and memory. However, if you’re dealing with intricate shapes or need precise local detail, an unstructured mesh would be the better choice despite the greater memory requirement and potentially slower solve times¹. Always consider the trade-offs between these factors when making your decision.

What is the difference between tetrahedral and hexahedral elements?

The difference between tetrahedral and hexahedral elements is that tetrahedral elements have four triangular faces, while hexahedral elements have six quadrilateral faces. Tetrahedral elements are more suitable for representing complex geometries, but they are less accurate and more prone to distortion than hexahedral elements. Hexahedral elements can provide more accurate and stable solutions, but they are more difficult and time-consuming to generate for arbitrary shapes.

You can find more information about the types of mesh and their advantages and disadvantages in the web search results I have provided¹²³⁴⁵. 

What is the difference between triangular and quadrilateral elements?

The difference between triangular and quadrilateral elements is that triangular elements have three sides and three vertices, while quadrilateral elements have four sides and four vertices. Triangular elements can be used to mesh any shape of domain, but they may not be very accurate or efficient for some problems. Quadrilateral elements can provide better accuracy and efficiency for some problems, but they may not be able to mesh complex shapes of domains.

You can find more information about the types of elements and their advantages and disadvantages in the web search results I have provided¹²³⁴⁵. 

💥💥💥 What is loosely coupled conjugate heat transfer in Ansys Fluent?

 Loosely coupled conjugate heat transfer (CHT) is a new feature in Ansys Fluent 2021R1 that allows you to simulate heat transfer between fluid and solid domains without requiring a conformal mesh or a coupled solver. With this approach, the coupled fluid/solid interface is replaced by boundary conditions: the temperature boundary condition from the solid zones is assigned to the fluid domain, while the heat flux boundary condition from the fluid zones are assigned to the solid domain. This way, you can use different time steps and solvers for the fluid and solid regions, and exchange information between them at a specified coupling frequency. This can reduce the computational cost and complexity of CHT simulations, especially for transient problems with large temperature gradients and deformations. You can learn more about this feature from the following sources:

• How does the “Loosely Coupled Conjugate Heat Transfer” function work? - Ansys Learning Forum
• Fluent Loosely Coupled Conjugate Heat Transfer - Ansys Learning Forum
• CHT Analysis in Ansys Discovery | Ansys Courses
• Ansys Fluent Heat Transfer Modeling | Ansys Training
• Fast Conjugate Heat Transfer Simulation of Long Transient Flexible …

What is the difference between loosely coupled and tightly coupled CHT?

The difference between loosely coupled and tightly coupled CHT is the degree of dependency between the fluid and solid domains in the simulation. In loosely coupled CHT, the fluid and solid domains are solved separately using different meshes, time steps, and solvers, and the information is exchanged between them at a specified frequency. This reduces the computational cost and complexity of the simulation, but may introduce some errors due to interpolation and synchronization. In tightly coupled CHT, the fluid and solid domains are solved simultaneously using a conformal mesh and a coupled solver. This ensures accuracy and stability of the solution, but may increase the computational time and memory requirements. You can find more details about these two approaches from the web search results I found for you¹²³⁴⁵.

How do I choose between loosely coupled and tightly coupled CHT for my simulation?

The choice between loosely coupled and tightly coupled CHT depends on the characteristics of your simulation problem and the trade-off between accuracy and efficiency. Some factors that may influence your decision are:

• The size and complexity of your model: loosely coupled CHT can handle larger and more complex models with less computational resources, while tightly coupled CHT may require more memory and CPU time.
• The type and quality of your mesh: loosely coupled CHT can work with non-conformal meshes that have gaps or overlaps between the fluid and solid domains, while tightly coupled CHT requires a conformal mesh that matches the interface exactly.
• The magnitude and variation of the temperature and heat flux at the interface: loosely coupled CHT may introduce some errors due to interpolation and synchronization of the boundary conditions, while tightly coupled CHT ensures accuracy and stability of the solution.
• The transient behavior and deformation of the fluid and solid domains: loosely coupled CHT can handle large deformations and long transient simulations with different time steps for the fluid and solid domains, while tightly coupled CHT may require smaller time steps and more iterations to converge.

You can find more information about these factors from the web search results I found for you . You can also try both approaches and compare the results to see which one suits your needs better.

How to define loosely coupled conjugate heat transfer model in Ansys Fluent ?

To define a loosely coupled conjugate heat transfer model in Ansys Fluent, you need to follow these steps:

• Create a non-conformal mesh for the fluid and solid domains, or import an existing mesh from another software. You can use different element types and sizes for the fluid and solid regions, as long as they are compatible with Fluent.
• In the Fluent interface, go to the Setup panel and enable the Loosely Coupled Conjugate Heat Transfer option under Models > Energy.
• Specify the fluid time step size and the solid time step size under Solution > Methods. The fluid time step size should be smaller than the solid time step size to ensure accuracy and stability.
• Specify the coupling frequency under Solution > Loosely Coupled CHT. The coupling frequency determines how often the information is exchanged between the fluid and solid domains. You can choose to couple every N fluid time steps, or every N seconds of physical time.
• Define the material properties and boundary conditions for the fluid and solid zones under Materials and Boundary Conditions. For the fluid/solid interface, you need to select the Loosely Coupled CHT boundary condition type and assign the appropriate zones as source and target. Fluent will automatically interpolate the temperature and heat flux values from the adjacent zones using a conservative scheme.
• Initialize the solution and run the calculation under Solution > Initialization and Solution > Run Calculation. You can monitor the convergence and results under Solution > Monitors and Results.

You can find more details and examples about this feature from the web search results I found for you¹²³⁴. I hope this helps you with your simulation. 😊

How do I post-process my CHT simulation results in Fluent?

To post-process your CHT simulation results in Fluent, you can use the tools available in the Results tab of Fluent. You can create various surfaces and graphics objects to visualize the temperature and heat flux distributions in the fluid and solid domains. You can also extract numerical results using surface integrals, fluxes, and XY plots. You can find some tutorials and examples on how to do post-processing in Fluent from the web search results I found for you¹²³⁴⁵. Here are some steps you can follow to post-process your CHT simulation results in Fluent:

• In the Results tab, go to Surfaces and create a plane surface that cuts through the fluid/solid interface. You can use the Define Plane by 3 Points option and select three points on the interface to define the plane.
• In the Graphics and Animations panel, go to Contours and select Temperature as the variable to display. Select the plane surface you created as the surface to display on. Click Display to see the temperature distribution on the plane.
• In the Graphics and Animations panel, go to Vectors and select Velocity as the variable to display. Select the plane surface you created as the surface to display on. Click Display to see the velocity vectors on the plane.
• In the Graphics and Animations panel, go to Pathlines and select Temperature as the variable to color by. Select a point or a line on the inlet or outlet of the fluid domain as the starting location for the pathlines. Click Display to see the pathlines of fluid particles colored by temperature.
• In the Reports panel, go to Surface Integrals and select Heat Flux as the variable to report. Select a surface or a group of surfaces on the fluid/solid interface as the surface to report on. Click Compute to see the total heat flux across the interface.
• In the Reports panel, go to Fluxes and select Mass Flow Rate as the variable to report. Select a surface or a group of surfaces on the inlet or outlet of the fluid domain as the surface to report on. Click Compute to see the mass flow rate through the surface.
• In the Reports panel, go to XY Plot and select Temperature as the Y variable and X Coordinate as the X variable. Select a line or a point on the fluid/solid interface as the surface to plot on. Click Plot to see a graph of temperature versus x coordinate along the interface.

You can also create scenes to combine multiple graphics objects in a single display, or export images or data files for further analysis. I hope this helps you with your post-processing. 😊

💥💥💥 What is Azure Machine Learning ?

Azure Machine Learning is a cloud-based service that allows data scientists and developers to create, deploy, and manage high-quality machine learning models faster and with confidence. It supports the end-to-end machine learning lifecycle, from data preparation and labeling to model building, validation, deployment, and monitoring. It also offers features such as automated machine learning, responsible AI, MLOps, and AI workflow orchestration. You can learn more about Azure Machine Learning from the following web search results:

• Azure Machine Learning — uczenie maszynowe jako usługa | Microsoft Azure: This is the official product page of Azure Machine Learning in Polish. It provides an overview of the features, security, pricing, and customer stories of Azure Machine Learning.
• Azure Machine Learning - ML as a Service | Microsoft Azure: This is the official product page of Azure Machine Learning in English. It provides similar information as the previous result, but in more detail and with some differences in terminology.
• Azure AI | Machine Learning Studio: This is the web portal for accessing Azure Machine Learning Studio, a GUI-based integrated development environment for constructing and operationalizing machine learning workflows on Azure.

What is the difference between Azure Machine Learning and Azure Databricks?

Azure Machine Learning and Azure Databricks are both cloud-based services that can be used for machine learning and data analytics, but they have some differences in their features, use cases, and pricing. Here are some of the main differences between them:

• Azure Machine Learning is focused on the end-to-end machine learning lifecycle, from data preparation and labeling to model building, validation, deployment, and monitoring. It supports MLOps, open-source interoperability, and integrated tools for model development and management. It also offers features for responsible AI, such as model fairness, explainability, error analysis, causal analysis, and data drift detection¹.
• Azure Databricks is based on Apache Spark, a distributed computing framework that can handle large-scale data processing and analytics. It supports real-time data streaming and processing for IoT applications, ETL workflows and data integration for data warehouses and data lakes, and collaborative data science and engineering for team-based data analysis and experimentation. It also provides a data plane and a control plane for back-end services that deliver instant compute and storage²³.
• Azure Machine Learning can be used for various machine learning and AI-based applications, such as predictive maintenance, customer churn analysis, sentiment analysis, healthcare diagnosis, fraud detection, and credit scoring¹. Azure Databricks can be used for big data processing and analytics for enterprise applications, as well as machine learning and AI-based applications for predictive analytics and recommendation engines³.
• Azure Machine Learning has a pay-as-you-go pricing model based on the type and number of resources used for training and inference. It also offers a free tier with limited capabilities¹. Azure Databricks has a consumption-based pricing model based on the number of virtual machines (VMs) used per hour or per minute. It also offers a community edition with limited resources³.

I hope this helps you understand the difference between Azure Machine Learning and Azure Databricks. If you want to learn more about these services, you can check out the following web search results: Azure ML vs. Databricks: Machine Learning Comparison, microsoft azure databricks vs microsoft azure machine learning: Which Tool is Better for Your Next Project?, Comparing Azure Databricks and Azure Machine Learning, machine learning - When should I use Azure ML Notebooks VS Azure Databricks?, Azure Machine Learning vs Databricks: A Comprehensive Comparison.

Can I use both Azure Machine Learning and Databricks together?

Yes, you can use both Azure Machine Learning and Databricks together for your machine learning projects. Azure Machine Learning and Databricks are both cloud-based services that can be used for machine learning and data analytics, but they have some differences in their features, use cases, and pricing. By integrating them, you can leverage the best of both worlds: the end-to-end machine learning lifecycle management of Azure Machine Learning and the scalable data processing and deep learning capabilities of Databricks.

There are three ways to use Databricks as a compute environment with Azure Machine Learning:

• Running Databricks notebooks in your pipeline steps: You can create Azure Machine Learning pipelines that execute Databricks notebooks for specific machine learning tasks, such as data preparation, model training, or inference. You can also pass parameters and data between the pipeline steps and the notebooks. This way, you can reuse your existing Databricks code and take advantage of the Spark engine for distributed computing¹.
• Using automated machine learning with Databricks: You can use the automated machine learning feature of Azure Machine Learning to automatically train and tune machine learning models on your Databricks cluster. You can also use the AutoML UI or the AutoML SDK to configure and run your experiments. This way, you can save time and effort in finding the best model for your data and scenario².
• Using Databricks as a compute target from an Azure Machine Learning pipeline: You can use Databricks as a compute target for running Python scripts or Jupyter notebooks from an Azure Machine Learning pipeline. You can also install additional libraries or dependencies on your Databricks cluster as needed. This way, you can run custom code on a scalable and flexible compute environment³.

What are some use cases for integrating Azure Machine Learning and Databricks?

Some use cases for integrating Azure Machine Learning and Databricks are:

• Real-time data streaming and processing for IoT applications: You can use Databricks to ingest, process, and analyze data from various sources such as sensors, devices, or web applications in real-time. You can then use Azure Machine Learning to train, deploy, and monitor machine learning models that can provide insights, predictions, or recommendations based on the streaming data. For example, you can build a smart home system that can adjust the temperature, lighting, or security based on the user’s preferences and behavior¹.
• ETL workflows and data integration for data warehouses and data lakes: You can use Databricks to perform extract, transform, and load (ETL) operations on large-scale data from various sources such as databases, files, or APIs. You can then use Azure Machine Learning to access, explore, and prepare the data for machine learning tasks. For example, you can build a data pipeline that can cleanse, enrich, and aggregate data from multiple sources and store it in a data lake or a data warehouse for further analysis².
• Collaborative data science and engineering for team-based data analysis and experimentation: You can use Databricks to create notebooks that can run Python, R, Scala, or SQL code on a distributed cluster. You can then use Azure Machine Learning to track, manage, and share your experiments and models with your team members. For example, you can build a machine learning project that can leverage the power of Databricks notebooks for data exploration, feature engineering, model training, and evaluation, and use Azure Machine Learning for model tracking, comparison, deployment, and serving³.

How can I monitor my machine learning models in Databricks and Azure Machine Learning?

There are several ways to monitor your machine learning models in Databricks and Azure Machine Learning. Here are some of the options you can consider:

• You can use MLflow, an open-source platform for managing the end-to-end machine learning lifecycle, to track, compare, and evaluate your model performance and metrics. MLflow is integrated with both Databricks and Azure Machine Learning, so you can easily log and access your model information from either service. You can also use MLflow to register, version, and deploy your models to various environments¹².
• You can use Lakehouse Monitoring, a feature of Databricks that allows you to monitor the quality and drift of your data and models. Lakehouse Monitoring leverages Delta Lake, a storage layer that enables reliable and performant data pipelines, to provide data validation, anomaly detection, and alerting capabilities. You can use Lakehouse Monitoring to track the distribution, statistics, and schema of your data and model inputs and outputs, and to identify any issues or changes that may affect your model performance³.
• You can use Azure Machine Learning Monitor, a service that helps you collect data from your deployed models and analyze it for model performance and fairness. Azure Machine Learning Monitor supports both online and batch inference scenarios, and provides tools for data exploration, visualization, and debugging. You can use Azure Machine Learning Monitor to monitor the inputs, outputs, predictions, errors, and latency of your models, as well as the fairness metrics such as demographic parity and equalized odds⁴.

What are some common issues that can affect the performance of machine learning models?

Some common issues that can affect the performance of machine learning models are:

• Data quality: The quality of the data used for training and testing the models is crucial for achieving good performance. Data quality issues include missing values, outliers, noise, inconsistency, imbalance, and redundancy. These issues can reduce the accuracy, reliability, and generalizability of the models. To improve data quality, various techniques such as data cleaning, preprocessing, transformation, normalization, feature selection, and augmentation can be applied¹².
• Algorithm selection: The choice of the algorithm or the model architecture for a given machine learning task is also important for achieving good performance. Different algorithms have different strengths and weaknesses, and may perform better or worse on different types of data sets. For example, some algorithms may be more suitable for linearly separable data, while others may be more suitable for nonlinearly separable data. Some algorithms may be more robust to noise and outliers, while others may be more prone to overfitting or underfitting. To select the best algorithm for a given task, various techniques such as cross-validation, grid search, random search, and Bayesian optimization can be used¹³.
• Hyperparameter tuning: Hyperparameters are the parameters that are not learned by the model during training, but are set by the user before the training process. Hyperparameters control various aspects of the model such as the learning rate, the number of epochs, the regularization strength, the number of hidden layers, and the activation function. Hyperparameters can have a significant impact on the performance of the model, and finding the optimal values for them can be challenging and time-consuming. To tune hyperparameters effectively, various techniques such as grid search, random search, Bayesian optimization, and evolutionary algorithms can be used¹⁴.
• Model evaluation: Model evaluation is the process of measuring how well the model performs on unseen data. Model evaluation is essential for assessing the performance of the model, comparing different models or algorithms, and selecting the best model for deployment. Model evaluation can also reveal potential issues such as overfitting or underfitting, which can affect the generalization ability of the model. To evaluate models accurately and reliably, various techniques such as train-test split, cross-validation, bootstrapping, and nested cross-validation can be used¹⁵.
• Model deployment and monitoring: Model deployment is the process of making the model available for use in a production environment. Model monitoring is the process of tracking and analyzing the performance and behavior of the model in a production environment. Model deployment and monitoring are important for ensuring that the model meets the expectations and requirements of the users and stakeholders. Model deployment and monitoring can also help identify and resolve any issues that may arise due to changes in data distribution, user feedback, or system configuration. To deploy and monitor models effectively, various techniques such as continuous integration, continuous delivery, logging, alerting, debugging, and updating can be used .

💥💥💥 What is the easiest machine learning software to use ?

 Selecting the easiest software for modeling machine learning depends on your specific needs and level of expertise. Here are some options that are considered user-friendly and suitable for various purposes:

1. TensorFlow: TensorFlow is an end-to-end open source platform that makes it easy for both beginners and experts to create machine learning models¹.

2. Anaconda: Anaconda is recommended for small businesses and freelancers. It’s known for its ease of use and comprehensive data science toolkit.

3. Google Cloud AI Platform: This platform offers low- and no-code machine learning frameworks, making it accessible for those with limited coding background.

4. Azure Machine Learning: Azure Machine Learning provides pre-trained models and is available for those with limited coding background. It simplifies remote working and has features that enable data scientists to pull data from a wide range of sources.

5. IBM Machine Learning: IBM Machine Learning suite combines various products and is known for its free, open-source machine learning software.

Each of these platforms has its own strengths, so I recommend exploring them further to see which one aligns best with your requirements.

What is TensorFlow?

TensorFlow is an end-to-end open source platform that makes it easy for both beginners and experts to create machine learning models¹. It offers a comprehensive ecosystem of tools, libraries, and community resources that help you build, train, deploy, and manage ML applications. TensorFlow supports various types of models, such as deep learning, computer vision, natural language processing, recommender systems, and more. TensorFlow also enables you to run your models on different devices and platforms, such as CPUs, GPUs, TPUs, mobile devices, web browsers, servers, and cloud services. TensorFlow is widely used by researchers, developers, and enterprises for various purposes. You can learn more about TensorFlow from its official website or its introduction page.

What is the difference between TensorFlow and Keras?

TensorFlow and Keras are both popular frameworks for deep learning, but they have some key differences. Here are some of the main points of comparison:

• TensorFlow is a low-level framework that offers more flexibility and control over the details of the model architecture and optimization. Keras is a high-level framework that provides a simpler and more user-friendly interface for building and training models.
• TensorFlow supports various types of models, such as deep learning, computer vision, natural language processing, recommender systems, and more. Keras mainly focuses on neural networks and offers various pre-built layers and models for common tasks.
• TensorFlow can run on different devices and platforms, such as CPUs, GPUs, TPUs, mobile devices, web browsers, servers, and cloud services. Keras relies on TensorFlow or other backends for its computation and deployment.
• TensorFlow has a larger and more active community of developers and users, as well as more documentation and tutorials. Keras has a smaller but still significant community and is easier to learn for beginners.

You can learn more about the differences between TensorFlow and Keras from these sources: Difference between TensorFlow and Keras - GeeksforGeeks, PyTorch vs Tensorflow vs Keras - DataCamp, TensorFlow vs Keras: Key Difference Between Them - Guru99, Keras vs TensorFlow: What’s The Difference? - Springboard.

Which one is better for beginners?

There is no definitive answer to which framework is better for beginners, as it depends on your personal preference, learning style, and goals. However, some general factors that you may consider are:

• Ease of use: Keras is designed to be more user-friendly and intuitive than TensorFlow, as it offers a simpler and more consistent interface for building and training models. Keras also has fewer lines of code and less boilerplate than TensorFlow, making it easier to read and debug.
• Flexibility: TensorFlow gives you more control and customization over the details of the model architecture and optimization. TensorFlow also supports various types of models, such as deep learning, computer vision, natural language processing, recommender systems, and more. Keras mainly focuses on neural networks and offers various pre-built layers and models for common tasks.
• Performance: TensorFlow can run on different devices and platforms, such as CPUs, GPUs, TPUs, mobile devices, web browsers, servers, and cloud services. TensorFlow also has a faster execution speed and a larger memory capacity than Keras. Keras relies on TensorFlow or other backends for its computation and deployment.
• Community: TensorFlow has a larger and more active community of developers and users, as well as more documentation and tutorials. TensorFlow also has more updates and improvements than Keras. Keras has a smaller but still significant community and is easier to learn for beginners.

Based on these factors, you may choose the framework that suits your needs and preferences best. You can also try both frameworks and compare them yourself. 

What are the first steps to model in TensorFlow ?

To model in TensorFlow, you need to follow some basic steps:

1. Define your model architecture using tf.keras.Sequential, tf.keras.Model, or tf.Module. You can use predefined layers from tf.keras.layers or create your own custom layers. You can also specify the input shape, activation functions, regularization, and other parameters for your model.
2. Compile your model using tf.keras.Model.compile or tf.Module.call. You need to specify the optimizer, loss function, metrics, and other options for your model training.
3. Train your model using tf.keras.Model.fit or a custom training loop. You need to provide the training data, batch size, number of epochs, validation data, callbacks, and other options for your model training.
4. Evaluate your model using tf.keras.Model.evaluate or a custom evaluation loop. You need to provide the test data, batch size, metrics, and other options for your model evaluation.
5. Save and load your model using tf.keras.Model.save, tf.keras.Model.load_model, tf.saved_model.save, or tf.saved_model.load. You can save your model as a whole or as individual components, such as weights, architecture, or functions.

You can find more details and examples of how to model in TensorFlow from these sources: TensorFlow 2 quickstart for beginners, Introduction to modules, layers, and models, Training models.

Can you show me an example of how to save and load a TensorFlow model?

Sure, I can show you an example of how to save and load a TensorFlow model. Here is a simple code snippet that demonstrates the steps:

Import TensorFlow and Keras

import tensorflow as tf from tensorflow import keras

Define a simple sequential model

def create_model(): model = tf.keras.Sequential([ keras.layers.Dense(512, activation=‘relu’, input_shape=(784,)), keras.layers.Dropout(0.2), keras.layers.Dense(10) ])

model.compile(optimizer=‘adam’, loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[‘accuracy’])

return model

Create and train a model instance

model = create_model() model.fit(train_images, train_labels, epochs=5)

Save the entire model as a SavedModel

model.save(‘my_model’)

Load the model back

loaded_model = tf.keras.models.load_model(‘my_model’)

Check that the state is preserved

loaded_model.evaluate(test_images, test_labels)

This example is based on the Save and load models tutorial from TensorFlow. You can find more details and explanations there.

How do I use the loaded model for prediction?

To use the loaded model for prediction, you need to provide the input data that matches the model’s expected input shape, type, and features. You can use different methods to feed the input data to the model, such as:

• Using the model.predict () function on a TensorFlow Dataset created with pd_dataframe_to_tf_dataset. This function converts a pandas DataFrame into a TensorFlow Dataset that can be used for prediction. You can find an example of this method in Making predictions with a TensorFlow model - Stack Overflow.
• Using the model.predict () function on a TensorFlow Dataset created manually. This function allows you to create your own TensorFlow Dataset from various sources, such as numpy arrays, tensors, or generators. You can find an example of this method in Making predictions | TensorFlow Decision Forests.
• Using the model.predict () function on numpy arrays. This function accepts numpy arrays as input and returns numpy arrays as output. You can find an example of this method in How to make prediction based on model Tensorflow lite?.
• Using the TensorFlow Lite inference API. This API allows you to run inference on mobile devices or embedded systems using TensorFlow Lite models. You can find an example of this method in TensorFlow Lite inference.

How do I preprocess the input data for prediction?

Preprocessing the input data for prediction is an important step in machine learning, as it can improve the performance and accuracy of the model. There are different ways to preprocess the input data, depending on the type, format, and quality of the data. Here are some common steps that you may follow to preprocess the input data for prediction:

1. Acquire the dataset: You need to obtain the dataset that contains the input data for prediction. You can use various sources, such as files, databases, APIs, web scraping, etc. You can also use existing datasets from online platforms, such as Kaggle, UCI Machine Learning Repository, TensorFlow Datasets, etc.
2. Import all the crucial libraries: You need to import the libraries that will help you preprocess the input data, such as NumPy, Pandas, Scikit-learn, TensorFlow, etc. These libraries provide various functions and methods for data manipulation, analysis, and transformation.
3. Import the dataset: You need to load the dataset into your Python environment using the appropriate functions. For example, you can use pd.read_csv() to read a CSV file into a Pandas DataFrame, or tf.data.Dataset.from_tensor_slices() to create a TensorFlow Dataset from a tensor.
4. Identify and handle the missing values: You need to check if there are any missing values in the input data, such as NaNs (not a number) or empty strings. You can use methods like df.isnull() or df.info() to inspect the dataset for missing values. You can then choose to either drop the rows or columns with missing values using df.dropna(), or fill them up with some imputed values using df.fillna() or sklearn.impute.SimpleImputer().
5. Encode the categorical data: You need to convert the categorical data into numerical data, as most machine learning models only accept numerical inputs. Categorical data are data that have a finite number of possible values, such as gender, color, or country. You can use various methods to encode the categorical data, such as sklearn.preprocessing.LabelEncoder(), sklearn.preprocessing.OneHotEncoder(), pd.get_dummies(), or tf.feature_column.categorical_column_with_vocabulary_list().
6. Normalize or standardize the numerical data: You need to scale the numerical data to a common range or distribution, as this can help reduce the variance and improve the convergence of the model. Normalization rescales the data to have values between 0 and 1, while standardization rescales the data to have a mean of 0 and a standard deviation of 1. You can use methods like sklearn.preprocessing.MinMaxScaler(), sklearn.preprocessing.StandardScaler(), tf.keras.utils.normalize(), or tf.keras.layers.experimental.preprocessing.Normalization().
7. Split the dataset into training and test sets: You need to divide the dataset into two subsets: one for training the model and one for evaluating the model. This can help prevent overfitting and measure the generalization ability of the model. You can use methods like sklearn.model_selection.train_test_split(), tf.data.Dataset.take(), or tf.data.Dataset.skip().

These are some of the basic steps that you may follow to preprocess the input data for prediction. However, you may also need to apply other techniques depending on your specific problem and dataset, such as feature engineering, feature selection, dimensionality reduction, outlier detection, etc. You can find more details and examples of how to preprocess the input data for prediction from these sources: How to Preprocess Data in Python | Built In, Preprocessing data with TensorFlow Transform | TFX, Data Preprocessing in Machine Learning: 7 Easy Steps To Follow.