Gain insights into JAX and Flax's features for deep learning. Learn about optimizers, functions, data loading, and model training. Explore hands-on projects for practical experience.

edujax_1.tar.gz

jaxbook

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This course comprehensively introduces JAX and Flax, two open-source libraries that have gained prominence for their efficiency, flexibility, and scalability in deep learning applications. 

In this course, you’ll explore deep learning principles and understand the unique features of JAX and Flax. You will learn the basics of JAX, optimizers using JAX and Flax, and loss and activation functions. You’ll also learn how to load datasets, perform classification using distributed learning, and use ResNet and LSTM models. In the end, you will complete a project for hands-on experience using JAX and Flax for transfer learning. 

By the end of the course, you’ll be proficient in implementing and customizing neural network models using JAX and Flax, equipped with hands-on skills in advanced optimization and distributed training.

Deep Learning with JAX and Flax

**(Fill in the blank.)** The ________ package provides pretrained models for JAX and Flax.


(Fill in the blank.) The ________ package provides pretrained models for JAX and Flax.

**(Select all that apply.)** Consider the following code:
```python
def custom_collate_fn(batch):
    transposed_data = list(zip(*batch))
    labels = np.array(transposed_data[1])
    imgs = np.stack(transposed_data[0])
    return imgs, labels
```
What does the function above do?

(Select all that apply.) Consider the following code:
<pre><code class="language-python">def custom_collate_fn(batch):
 transposed_data = list(zip(*batch))
 labels = np.array(transposed_data[1])
 imgs = np.stack(transposed_data[0])
 return imgs, labels
</code></pre>
What does the function above do?

**(Select all that apply.)** Which options are instances of the ResNet block?

(Select all that apply.) Which options are instances of the ResNet block?

Which function aggregates the metrics from all the devices?


Which function aggregates the metrics from all the devices?

**(Fill in the blank.)** For transfer learning, we use the ResNet as the ________ and define a custom classification layer.


(Fill in the blank.) For transfer learning, we use the ResNet as the ________ and define a custom classification layer.

Take a short quiz to gauge your learning.

Course Introduction

Basics of JAX

Optimizers in JAX and Flax

Loss and Activation Functions

Load Datasets in JAX

Image Classification and Distributed Training

TensorBoard and State Handling

LSTM in JAX and Flax

Flax vs. TensorFlow

Using ResNet Model in Flax

Transfer Learning in JAX and Flax

Conclusion

Appendix

Quiz: Using ResNet Model in Flax