This repository is dedicated to my AI/ML learning journey and practice.
Here, I’ll be pushing code, experiments, notes, and resources related to Artificial Intelligence, Deep Learning, and Large Language Models (LLMs).

• 🔬 Practice code for AI/ML/Deep Learning
• 📚 Notes on Transformer architecture & modern NLP techniques
• 🧩 Implementations of key AI concepts (Tokenization, Embeddings, Attention, etc.)
• 🏗️ Experiments with state-of-the-art LLMs

Here are some of the most important LLMs you can explore:

1. GPT-4 (OpenAI)
2. Claude 3 (Anthropic)
3. Gemini (Google DeepMind)
4. LLaMA 3 (Meta AI)
5. Mistral (Mistral AI)
6. Falcon (TII)
7. Cohere Command R
8. Jurassic-2 (AI21 Labs)
9. Mixtral (Mistral AI, MoE)
10. DeepSeek LLM

The field of LLMs is rapidly evolving. Some of the latest research and engineering trends include:

1. Reasoning (RLVR, GRPO)

   • RLVR (Reinforcement Learning via Verifiable Rewards): ensures models get rewarded for provably correct reasoning steps.
   • GRPO (Generalized Reinforcement Policy Optimization): an advanced RL method for improving structured reasoning.

2. MoEs (Mixture of Experts)

   • Models with multiple expert subnetworks, where only a subset activates per query.
   • Enables efficiency and scalability.
   • Example: Mixtral of Experts.

3. Tool Use

   • LLMs can call external tools (APIs, calculators, databases, code execution).
   • Moves LLMs from static text generators to active AI agents.

4. Multi-Head Latent Attention

   • Extends multi-head attention to focus on latent (hidden) representations.
   • Enhances reasoning depth and compression of long contexts.

• Attention Is All You Need (Vaswani et al., 2017)
• The Illustrated Transformer – Jay Alammar

Hareesh Bhittam
Full-stack developer exploring AI & LLMs.