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

jupyter_final.tar.gz

codesnip1

tensorboard

jupyterp-autograd

jupyterp-computation

jupyterp-rethinking

jupyterp-minibatch

jupyterp-dataloader

jupyterp-evaluation

jupyterp-saveload

jupyterp-predictions

jupyterp-training-predictions

jupyterp-checkpointing

jupyterp-classy

jupyterp-classification

jupyterp-retest

jupyterp-retest-copy

jupyterp-retest-copy-copy

jupyterp-retest-copy-copy-copy

jupyterp-retest-copy-copy-copy-copy

jupyterp-retest-question

jupyterp-retest-question-copy

jupyterp-retest-question-copy-copy

jupyterp-retest-question-copy-copy-copy

jupyterp-retest-question-copy-copy-copy-copy

jupyterp-retest-challenge

jupyterp-retest-challenge-copy

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jupyterp-putting1

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jupyterp-tensorboard

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jupyterp-tensorboard-scalers

jupyterp-tensorboard-training

GUI-Tensorboard

GUI-Tensorboard-graphs

GUI-Tensorboard-scalers

GUI-Tensorboard-training

GUI-Chapter04

GUI-No-Jupyter-Tensorboard

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

## Loss surface

We have just computed the loss (2.74) corresponding to our randomly initialized parameters (`b = 0.49` and `w = -0.13`). What if we did the same for all possible values of `b` and `w`? Well, not all possible values but all combinations of evenly spaced values in a given range like the example shown in the code below:

# Loss surface

We have just computed the loss (2.74) corresponding to our randomly initialized parameters (`b = 0.49` and `w = -0.13`). What if we did the same for all possible values of `b` and `w`? Well, not all possible values but all combinations of evenly spaced values in a given range like the example shown in the code below:

Learn how the loss can be computed for all possible values in a given range as well as how it can be visually represented.  

Step 2b - Computing the Loss Surface

Learn how the loss can be computed for all possible values in a given range as well as how it can be visually represented.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Step 2b - Computing the Loss Surface

Loss surface