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

What will be the output of the following code?

```python
class Scores:
    def __init__(self, s1, s2):
      self.scores = s1 + s2
    def display(self, scores):
      print(scores)
 
x = Scores(5,10)
setattr(x, 'scores', x.scores+1)
x.display(x.scores)
```

What will be the output of the following code?
<pre><code class="language-python">class Scores:
 def __init__(self, s1, s2):
 self.scores = s1 + s2
 def display(self, scores):
 print(scores)
 
x = Scores(5,10)
setattr(x, 'scores', x.scores+1)
x.display(x.scores)
</code></pre>

Methods that start with a single underscore (`_make_train_step()`) are known as private methods. 

Methods that start with a single underscore (<code>_make_train_step()</code>) are known as private methods.

Marcus Jones

`AttributeError: 'Students' object has no attribute '__studentrollno'`

`AttributeError: 'Students' object has no attribute '_studentname'`

What will be the output of the following?

```python
class Students(object):
  def __init__(self):
    self._studentname = "Marcus Jones"
    self.__studentrollno = 202111700

std = Students()
print(std._studentname)
print(std.__studentrollno)
```

What will be the output of the following?
<pre><code class="language-python">class Students(object):
 def __init__(self):
 self._studentname = &quot;Marcus Jones&quot;
 self.__studentrollno = 202111700

std = Students()
print(std._studentname)
print(std.__studentrollno)
</code></pre>

The placeholder attribute of a class is also known by "delayed arguments."

The placeholder attribute of a class is also known by “delayed arguments.”

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