ISLR with applications in Python is out! 🚀🚀🚀

An Introduction to Statistical Learning (ISLR), by Profs James, Witten, Hastie, and Tibshirani, in my opinion, is one of the best introductory books for machine learning ❤️. The book focuses on the foundations of data science and originally was with R examples. Today, the authors, along with Prof. Taylor, released a Python edition for the 2nd version of the book. The book covers topics such as:
✅ Regression and classification
✅ Linear model selection and regularization
✅ Non-linear regression
✅ Tree-based methods
✅ Support vector machines
✅ Deep learning
✅ Unsupervised learning

Both the R and Python versions of the book are available for free

R version
Python version

Have fun learning!👌

One can put this at the beginning of the notebook to check the packages in active environment.
given "d" is suggested package versions.

Python file

Deep Learning with JAX

Notebooks for the chapters:
1. Intro to JAX
- JAX Speedup
2. Your first program in JAX
- MNIST image classification with MLP in pure JAX
3. Working with tensors
- Image Processing with Tensors
- Working with DeviceArray's
4. Autodiff
- Different ways of getting derivatives
- Working with gradients in TensorFlow, PyTorch, and JAX
- Differentiating in JAX
5. Compiling your code
- JIT compilation and more: JIT, Jaxpr, XLA, AOT
6. Vectorizing your code
- Different ways to vectorize a function, Controlling vmap() behavior, More real-life cases
7. Parallelizing your computations
- Using pmap()
8. Advanced parallelization
- Using xmap()
- Using pjit()
- Tensor sharding
- Multi-host example
9. Random numbers in JAX
- Random augmentations, NumPy and JAX PRNGs
9. Complex structures in JAX/Pytrees
- Pytrees, jax.tree_util functions, custom nodes
11. more to come

Github

How To Build a Neural Network to Recognize
Handwritten Digits with TensorFlow


- measuring loss per epoch
- adding dropout probability
- adding callback function to automatically abort the training based on a condition on changing loss value per epoch.


GitHub notebook

Ops..., it's public now.

Machine Learning refresher.

Subplots in #Matplotlib
Some examples using:
1️⃣ subplot_mosaic
2️⃣ subplot2grid

GitHub

Mastering #Python
Credit: Mousa

Notion is a popular tool that offers a wide range of features for note-taking, task management, document creation, and knowledge management. It provides a versatile and customizable interface that can be tailored to individual needs and workflows.

It is also available on Web, Mac, Linux, Windows, IOS and Android.

https://www.notion.so/

YouTube

Diffrax is a JAX-based library providing numerical differential equation solvers.

Features include:

1️⃣ ODE/SDE/CDE (ordinary/stochastic/controlled) solvers;
2️⃣ lots of different solvers (including Tsit5, Dopri8, symplectic solvers, implicit solvers);
3️⃣ vmappable everything (including the region of integration);
4️⃣ using a PyTree as the state;
5️⃣ dense solutions;
6️⃣ multiple adjoint methods for backpropagation;
7️⃣support for neural differential equations.

#jax
Documentation

Skorch: The Power of PyTorch Combined with The Elegance of Sklearn

Skorch immensely simplifies training neural networks with PyTorch.

Documentation

A freely available short course on neuroscience for people with a machine learning background. Designed by Dan Goodman and Marcus Ghosh.

Link

NVTOP stands for Neat Videocard TOP, a (h)top like task monitor for AMD, Intel and NVIDIA GPUs. It can handle multiple GPUs and print information about them in a htop-familiar way.

sudo apt install nvtop
GitHub

polymorphism in Python

Generative AI course for Everyone, is now available!

Learn how Generative AI works, how to use it in professional or personal settings, and how it will affect jobs, businesses and society. This course is accessible to everyone, and assumes no prior coding or AI experience.

Please access it here.