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

It has a probability of 50% of being in the positive class.

It has a probability of 0% of being in the positive class.

It has a probability of 50% of being in the negative class.

Check all that apply. If a given data point has a logit value equals zero:

Check all that apply. If a given data point has a logit value equals zero:

The binary cross-entropy loss is given by averaging the logarithm of the predicted probability of each point being in the:

The binary cross-entropy loss is given by averaging the logarithm of the predicted probability of each point being in the:

The more dimensions, the ____ separable the data points are.

The more dimensions, the ____ separable the data points are.

The model `nn.Sequential(nn.Linear(2, 1))` is used in a binary classification task. Which loss function should be used with it?

The model <code>nn.Sequential(nn.Linear(2, 1))</code> is used in a binary classification task. Which loss function should be used with it?

Is the code snippet below correct?

```python
loss_fn = nn.BCELoss(reduction='mean')
loss = loss_fn(labels, predicted_probabilities)
```

Is the code snippet below correct?
<pre><code class="language-python">loss_fn = nn.BCELoss(reduction='mean')
loss = loss_fn(labels, predicted_probabilities)
</code></pre>

The “accuracy” is always going to be a good metric to evaluate a classification model.

The “accuracy” is always going to be a good metric to evaluate a classification model.

What is the shape of the decision boundary of logistic regression with two features?

What is the shape of the decision boundary of logistic regression with two features?

Check all that apply. There is a trade-off between these two metrics:

Check all that apply. There is a trade-off between these two metrics:

The model `nn.Sequential(nn.Linear(2, 1), nn.Sigmoid())` is used in a binary classification task. Which loss function should be used with it?

The model <code>nn.Sequential(nn.Linear(2, 1), nn.Sigmoid())</code> is used in a binary classification task. Which loss function should be used with it?

The values in a confusion matrix depend on the probability threshold used to classify the data points:

The values in a confusion matrix depend on the probability threshold used to classify the data points:

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