Deep learning with Pytorch, A beginner's Guide By Daniel Godoy.png

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This course is designed to provide you with an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch. In this course, you’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. You will develop, step-by-step, not only the models themselves but also your understanding of them. You'll be shown both the reasoning behind the code and how to avoid some common pitfalls and errors along the way. By the time you finish this course, you’ll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.

Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals

## Conversions between Numpy and PyTorch

It is time to start converting our Numpy code to PyTorch. We will start with the training data; that is, our `x_train` and `y_train` arrays. 

### The "`as_tensor`" method

# Conversions between Numpy and PyTorch

It is time to start converting our Numpy code to PyTorch. We will start with the training data; that is, our `x_train` and `y_train` arrays. 

## The "`as_tensor`" method

Learn about the different PyTorch built-in functions used in converting Numpy to PyTorch and vice versa, along with exploring GPU tensors.  

Loading Data, Devices, and CUDA

Learn about the different PyTorch built-in functions used in converting Numpy to PyTorch and vice versa, along with exploring GPU tensors.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Loading Data, Devices, and CUDA

Conversions between Numpy and PyTorch

The “`as_tensor`” method

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Loading Data, Devices, and CUDA

Conversions between Numpy and PyTorch

The “as_tensor” method

The “`as_tensor`” method