Delve into PyTorch basics: autograd, model classes, datasets, and data loaders. Gain insights into model development while avoiding common pitfalls. Start creating and training your own PyTorch models.

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. Tensors, by definition, may have __ dimensions.


Check all that apply. Tensors, by definition, may have __ dimensions.

After executing the code below, which option reflects the state of variable `x`?

```python
x = torch.ones((2,2))
y = x.view(-1,)
y[-1] = 0
```

After executing the code below, which option reflects the state of variable <code>x</code>?
<pre><code class="language-python">x = torch.ones((2,2))
y = x.view(-1,)
y[-1] = 0
</code></pre>

The statement below is a typical way of determining the device to be used.

```python
device = 'cuda' if torch.cuda.is_available() else 'cpu'
```

The statement below is a typical way of determining the device to be used.
<pre><code class="language-python">device = 'cuda' if torch.cuda.is_available() else 'cpu'
</code></pre>

`torch.randn(1, requires_grad=True, device='cpu')`

`torch.randn(1, requires_grad=False, device='cuda')`

`torch.ones(2, 3, requires_grad=True, device='cpu')`

Check all that apply. Which tensors may be used as trainable parameters? 

Check all that apply. Which tensors may be used as trainable parameters?

`torch.randn(1, requires_grad=True).to('cuda')`

`torch.randn(1, requires_grad=True, device='cuda')`

Which one is the recommended way of creating a GPU tensor for a parameter?

Which one is the recommended way of creating a GPU tensor for a parameter?

```python
x = torch.randn(1, device='cuda')
x.numpy()
```

```python
x = torch.randn(1, device='cuda')
x.cpu().numpy()
```

```python
x = torch.randn(1, device='cpu')
x.cpu().numpy()
```

```python
x = torch.randn(1, device='cpu')
x.numpy()
```

Check all that apply. Which operations are valid, that is, do NOT throw an error? 

Check all that apply. Which operations are valid, that is, do NOT throw an error?

What would be the output of the following code?

```python
import torch

tensor = torch.tensor([
    [1, 2, 3],
    [1, 2, 3],
    [1, 2, 3]
])

print(tensor.size())
```

What would be the output of the following code?
<pre><code class="language-python">import torch

tensor = torch.tensor([
 [1, 2, 3],
 [1, 2, 3],
 [1, 2, 3]
])

print(tensor.size())
</code></pre>

```python
tensor([[1, 2, 3],
        [1, 2, 3],
        [1, 2, 3]])
```

```python
tensor([[1, 2, 3],
        [0, 1, 2],
        [1, 5, 3]])
```

```python
tensor([[1, 2, 3],
        [0, 1, 2],
        [5, 5, 5]])
```

```python
ValueError: setting an array element with a sequence.
```

What would be the output of the following code?

```python
import numpy as np
import torch

dummy_array = np.array([
    [1, 2, 3],
    [1, 2, 3],
    [1, 2, 3]
])
dummy_tensor = torch.as_tensor(dummy_array)

dummy_array[1] = [0,1,2]
dummy_array[2] = [5]
print(dummy_tensor)
```

What would be the output of the following code?
<pre><code class="language-python">import numpy as np
import torch

dummy_array = np.array([
 [1, 2, 3],
 [1, 2, 3],
 [1, 2, 3]
])
dummy_tensor = torch.as_tensor(dummy_array)

dummy_array[1] = [0,1,2]
dummy_array[2] = [5]
print(dummy_tensor)
</code></pre>

Only creates CPU tensors, which will not work if you have a GPU.

The approach is fine, and we end up with gradient-requiring GPU tensors for our parameters: `b` and `w`.

The gradients get lost when working with this approach.

This approach creates nontrainable parameters: `b` and `w`.

Why is the following approach not recommended for creating parameters in PyTorch:

```python
import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.manual_seed(42)
b = torch.randn(1, requires_grad=True, dtype=torch.float).to(device)
w = torch.randn(1, requires_grad=True, dtype=torch.float).to(device)
```

Why is the following approach not recommended for creating parameters in PyTorch:
<pre><code class="language-python">import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.manual_seed(42)
b = torch.randn(1, requires_grad=True, dtype=torch.float).to(device)
w = torch.randn(1, requires_grad=True, dtype=torch.float).to(device)
</code></pre>

It is recommended to always assign tensors to a device at the moment of its creation.

When creating parameters, the outputs are different in both CPU and GPU because PyTorch generates different sequences of random numbers in different devices. 

The `view` method creates an entirely new tensor from the original, and if you modify it, the original tensor will not change. 

You can use the `numpy` method to turn a tensor into a GPU tensor.

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