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

jupyterp-retest-challenge-copy-copy

jupyterp-retest-challenge-copy-copy-copy

jupyterp-retest-challenge-copy-copy-copy-copy

jupyterp-putting1

jupyterp-putting2

jupyterp-putting3

jupyterp-putting4

jupyterp-tensorboard

jupyterp-tensorboard-graphs

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

Check all that apply. Which methods should be implemented in a custom dataset?

Check all that apply. Which methods should be implemented in a custom dataset?

It is best practice to always load the full dataset at once.

It is best practice to always load the full dataset at once.

It is best practice to send the dataset to the GPU right away.

It is best practice to send the dataset to the GPU right away.

Features of the `i`th data point

Tuple of (features, label) of the `i`th point

The `i`th feature of the dataset

What should be returned by <code>dataset[i]</code>?

It is best practice to set `shuffle=True` for the data loader of the validation/test set.

It is best practice to set <code>shuffle=True</code> for the data loader of the validation/test set.

The data loader behaves like a(n):

What would be the output of the following code:

```python
def calculate_result(num):
    def equation1(x):

        def equation2_1(y):
            return  (y ** x)

        def equation2_2(y):
            eq2_1 = equation2_1(y)
            return ((x + num) / (y - num)) * eq2_1

        return equation2_2

    return equation1

res = calculate_result(2)
res2 = res(4)
print(res2(5))
```

What would be the output of the following code:
<pre><code class="language-python">def calculate_result(num):
 def equation1(x):

 def equation2_1(y):
 return (y ** x)

 def equation2_2(y):
 eq2_1 = equation2_1(y)
 return ((x + num) / (y - num)) * eq2_1

 return equation2_2

 return equation1

res = calculate_result(2)
res2 = res(4)
print(res2(5))
</code></pre>

A mini-batch size of 16 was chosen for our example.

If your system has many GPUs to train your model on, it is best to assign the batches of your dataset to different GPUs during training.

When creating our training tensors out of Numpy arrays, we used CPU tensors instead of GPU tensors.

Higher-order functions are functions that return another function as its result.

Check all that apply. Which of the following statements are correct?

Check all that apply. Which of the following statements are correct?

Test your understanding of the concepts explained in this chapter with this quiz!

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Quiz