Discover the power of JAX in deep learning. Gain insights into its ecosystem and learn about linear algebra, pseudo-random number generation, and optimization algorithms for cleaner, structured coding.

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JuPyter

JAX is a Python library designed for high-performance ML research. It is a powerful numerical computing library, just like Numpy, but with some key improvements.

In this course, you will learn all about JAX and its ecosystem of libraries (Haiku, Jraph, Chex, Flax, Optax). Addressing a wide range of audiences, you will cover several topics including linear algebra, random variables theory, pseudo-random number generation, and optimization algorithms.

By the end of this course, you will have a new set of skills that will make deep learning programming more intuitive, structured, and clean.

Introduction to JAX and Deep Learning

As a reference, we'll provide a glossary of some common technical terms used in the course. 

## JAX

Some common JAX terms are presented here. These terms may or may not match with the common terminologies.

* **Asynchronous Dispatch**: The phenomenon in which "jitted" function doesn't await the complete calculation and passes it on to the next calculation. 

* **Device**: A generic term for either CPU, GPU or TPU.
* **DeviceArray** : JAX's analog of the `numpy.ndarray`.
* **`jaxpr`**: JAX Expressions (or jaxpr) are the intermediate representations of a computation graph.
* **JIT**: Just In Time compilation. It is performed in JAX using XLA.

* **Pytrees**: A tree-like structure built out of container-like Python objects.
* **`static`**: Compile-time computation in a JIT compilation and hence not traced.
* **TPU**: Tensor Processing Unit, 
* **Tracer**: An object determining the sequence of operations performed by a Python function.
* **Transformation**: A higher-order function that takes functions as inputs and returning a transformed function like `jit()`, `grad()`, `pmap()` and so on.
* **VJP**: Vector Jacobian Product is opposite of JVP and is used for reverse-mode auto differentiation.
* **XLA**: Accelerated Linear Algebra, XLA is a domain-specific compiler for linear algebra. It is used for JAX JIT compilation.
* **Weak type**: A JAX data type having the same _type promotion_ semantics as Python scalars.

---

## Theory

* **Adam**: A highly used algorithm for stochastic optimization.

* **Auto-differentiation**: A technique for calculating derivatives using the chain rule.

* **Batch Normalization**: A technique enabling faster training of deep neural networks by rescaling (and centering) them to have zero mean and a variance of one. $\mathcal N(0,1)$.

* **Convolution**: A mathematical operation expressing how the shape of a signal $f$ is modified by another signal $g$.

* **Cumulative Distribution Function (CDF)**: The value a distribution will take for values less than or equal to a given value $x$.

* **Gaussian (Normal) distribution**: The famous bell-shaped curve probability distribution used to model a lot of real-world situations.

* **Gradient Clipping**: A technique for faster convergence of deep neural networks by dividing the gradients beyond a threshold by its norm.

* **Hessian**: The matrix of 2nd order derivatives for a given vector.

* **Jacobian**: First-order derivatives matrix for a vector-valued function.

* **JVP**: Jacobian Vector Product is used to implement forward-mode auto differentiation.

* **Kullback-Leibler (KL) Divergence**: A commonly-used, asymmetric divergence measure.

* **Poisson distribution**: A discrete probability distribution, used to model quite unlikely events.

* **PRNG**: Pseudo-Random Number Generator is a key component of JAX and other numerical computation libraries as well. 

* **Probability Density Function (PDF)**: Derivative of CDF at a given point. Its value can be intuitively treated as a measure of probability around that point.

* **Probability Mass Function (PMF)**: Discrete counterpart of PDF.

* **Transposed Convolution**: The opposite of convolution resulting in upsampled output.

* **Wasserstein GAN (WGAN)**: A type of GAN using Wasserstein loss.

* **Wasserstein Loss**: A loss function based on the **optimal transport** problem (hence also known as earth-mover distance).

---

As a reference, we'll provide a glossary of some common technical terms used in the course. 

# JAX

Some common JAX terms are presented here. These terms may or may not match with the common terminologies.

* **Asynchronous Dispatch**: The phenomenon in which "jitted" function doesn't await the complete calculation and passes it on to the next calculation. 

* **Device**: A generic term for either CPU, GPU or TPU.
* **DeviceArray** : JAX's analog of the `numpy.ndarray`.
* **`jaxpr`**: JAX Expressions (or jaxpr) are the intermediate representations of a computation graph.
* **JIT**: Just In Time compilation. It is performed in JAX using XLA.

* **Pytrees**: A tree-like structure built out of container-like Python objects.
* **`static`**: Compile-time computation in a JIT compilation and hence not traced.
* **TPU**: Tensor Processing Unit, 
* **Tracer**: An object determining the sequence of operations performed by a Python function.
* **Transformation**: A higher-order function that takes functions as inputs and returning a transformed function like `jit()`, `grad()`, `pmap()` and so on.
* **VJP**: Vector Jacobian Product is opposite of JVP and is used for reverse-mode auto differentiation.
* **XLA**: Accelerated Linear Algebra, XLA is a domain-specific compiler for linear algebra. It is used for JAX JIT compilation.
* **Weak type**: A JAX data type having the same _type promotion_ semantics as Python scalars.

---

# Theory

* **Adam**: A highly used algorithm for stochastic optimization.

* **Auto-differentiation**: A technique for calculating derivatives using the chain rule.

* **Batch Normalization**: A technique enabling faster training of deep neural networks by rescaling (and centering) them to have zero mean and a variance of one. $\mathcal N(0,1)$.

* **Convolution**: A mathematical operation expressing how the shape of a signal $f$ is modified by another signal $g$.

* **Cumulative Distribution Function (CDF)**: The value a distribution will take for values less than or equal to a given value $x$.

* **Gaussian (Normal) distribution**: The famous bell-shaped curve probability distribution used to model a lot of real-world situations.

* **Gradient Clipping**: A technique for faster convergence of deep neural networks by dividing the gradients beyond a threshold by its norm.

* **Hessian**: The matrix of 2nd order derivatives for a given vector.

* **Jacobian**: First-order derivatives matrix for a vector-valued function.

* **JVP**: Jacobian Vector Product is used to implement forward-mode auto differentiation.

* **Kullback-Leibler (KL) Divergence**: A commonly-used, asymmetric divergence measure.

* **Poisson distribution**: A discrete probability distribution, used to model quite unlikely events.

* **PRNG**: Pseudo-Random Number Generator is a key component of JAX and other numerical computation libraries as well. 

* **Probability Density Function (PDF)**: Derivative of CDF at a given point. Its value can be intuitively treated as a measure of probability around that point.

* **Probability Mass Function (PMF)**: Discrete counterpart of PDF.

* **Transposed Convolution**: The opposite of convolution resulting in upsampled output.

* **Wasserstein GAN (WGAN)**: A type of GAN using Wasserstein loss.

* **Wasserstein Loss**: A loss function based on the **optimal transport** problem (hence also known as earth-mover distance).

---

Some common terms used throughout the course

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Glossary

JAX