A Complete Learning Journey Through 42 Hands-On Implementations

https://github.com/ShaliniAnandaPhD/PIXEL-PIONEERS-TUTORIALS

What You'll Find Here

Welcome to a comprehensive collection of 42 machine learning tutorials built with JAX! Whether you're just starting your ML journey or looking to master advanced techniques, these tutorials will guide you step-by-step through real-world implementations.

From simple linear regression to cutting-edge transformers, each tutorial is designed to teach you practical skills while building something meaningful. You'll learn by doing, with plenty of examples, common pitfalls to avoid, and tips to help you succeed.

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🗂️ JAX Tutorial Collection - Quick Find Index

Find exactly what you need to learn with this organized guide

🎯 I Want To Learn...

🚀 JAX Fundamentals

Start here if you're new to JAX

Basic JAX Operations:

• Linear Regression (jax_linear_regression.py) - ⭐⭐☆☆☆ | 2-3 hours
  • Learn: jax.numpy, jax.grad, @jax.jit
  • Perfect first JAX tutorial

JAX Data Processing:

• K-Means Clustering (jax_kmeans_customer_segmentation.py) - ⭐⭐☆☆☆ | 2-3 hours
  • Learn: jax.vmap, vectorization, array operations

JAX vs Other Frameworks:

• Basic vs Flax vs Functional - Compare 3 nutrition prediction implementations
  • See how the same problem looks in different JAX styles

🧠 JAX for Deep Learning

Getting Started with Neural Networks

Your First Neural Network:

• Nutrition Prediction - Basic (jax_nutritional_content_prediction.py) - ⭐⭐☆☆☆ | 2-3 hours
  • Learn: Basic neural networks, multi-output regression
  • Great introduction to JAX for ML

Modern JAX with Flax:

• Nutrition Prediction - Flax (jax_nutritional_content_prediction_flax.py) - ⭐⭐⭐☆☆ | 3-4 hours
  • Learn: Flax modules, modern JAX patterns, clean code

Intermediate Deep Learning

Autoencoders:

• Anomaly Detection (jax_autoencoder_anomaly_detection.py) - ⭐⭐⭐☆☆ | 3-4 hours
• Denoising Images (jax_denoising_autoencoder.py) - ⭐⭐⭐☆☆ | 4-5 hours

Advanced Architectures:

• VAE Face Generation (jax_vae_face_generation.py) - ⭐⭐⭐⭐☆ | 5-6 hours
• Transfer Learning (jax_transfer_learning.py) - ⭐⭐⭐☆☆ | 4-5 hours

🖼️ JAX for Computer Vision

Start Here for Vision

Image Classification:

• CIFAR-10 CNN (jax_cifar10_cnn_classification.py) - ⭐⭐⭐☆☆ | 3-4 hours
  • Your first computer vision project in JAX

Intermediate Vision Tasks

Image Enhancement:

• Super Resolution (jax_srcnn_image_super_resolution.py) - ⭐⭐⭐☆☆ | 4-5 hours
• Image Inpainting (jax_image_inpainting.py) - ⭐⭐⭐⭐☆ | 6-7 hours

Image Understanding:

• U-Net Segmentation (jax_unet_image_segmentation.py) - ⭐⭐⭐☆☆ | 4-5 hours
• Image Captioning (jax_image_captioning_cnn_rnn.py) - ⭐⭐⭐⭐☆ | 5-6 hours

Advanced Vision

Object Detection & Tracking:

• YOLO Object Detection (jax_yolo_object_detection.py) - ⭐⭐⭐⭐⭐ | 8-10 hours
• Siamese Object Tracking (jax_siamese_object_tracking.py) - ⭐⭐⭐⭐☆ | 6-8 hours

Generative Vision:

• GAN Image Generation (jax_gan_image_generation.py) - ⭐⭐⭐☆☆ | 5-6 hours
• VAE Face Generation (jax_vae_face_generation.py) - ⭐⭐⭐⭐☆ | 5-6 hours

💬 JAX for Natural Language Processing

Text Classification

Sentiment Analysis:

• BERT Fine-tuning (jax_bert_sentiment_analysis.py) - ⭐⭐⭐☆☆ | 4-5 hours
  • Learn transformer fine-tuning in JAX

Text Generation

Language Models:

• GPT-2 Text Generation (jax_gpt2_text_generation_simulated.py) - ⭐⭐⭐☆☆ | 3-4 hours
• Transformer Translation (jax_transformer_translation.py) - ⭐⭐⭐⭐☆ | 6-8 hours

Speech Processing

Audio to Text:

• Deep Speech Recognition (jax_deep_speech_recognition.py) - ⭐⭐⭐⭐⭐ | 8-10 hours

🎮 JAX for Reinforcement Learning

Start Here for RL

Basic Q-Learning:

• DQN CartPole (jax_dqn_cartpole.py) - ⭐⭐⭐☆☆ | 4-5 hours
  • Perfect introduction to RL with JAX

Intermediate RL

Improved DQN:

• Enhanced DQN (jax_dqn_reinforcement_learning.py) - ⭐⭐⭐☆☆ | 4-5 hours
  • Experience replay, target networks

Advanced RL

Policy Gradient Methods:

• A3C Actor-Critic (jax_a3c_reinforcement_learning.py) - ⭐⭐⭐⭐☆ | 6-7 hours

🔧 JAX Advanced Techniques

Performance Optimization

Making JAX Fast:

• Parallel Processing (jax_nutritional_content_prediction_parallel.py) - ⭐⭐⭐⭐⭐ | 5-8 hours

Custom Implementations:

• Explicit Differentiation (jax_nutritional_content_prediction_explicit_diff.py) - ⭐⭐⭐⭐☆ | 4-5 hours </aside>

🟢 Foundation Level Implementations (8 implementations)

1. Linear Regression Implementation

File: jax_linear_regression.py

Difficulty: Beginner (⭐⭐☆☆☆) | Development Time: 2-3 hours

Problem Domain: Supervised regression for housing price prediction using California housing dataset (20,640 samples, 8 features).

JAX Features Implemented:

• jax.numpy for array operations
• jax.grad for automatic differentiation
• @jax.jit compilation for performance optimization
• Custom training loop with gradient descent