Genify

A Comprehensive Course on Artificial Intelligence & Chip Design

Genify is a structured, end-to-end learning program that bridges Artificial Intelligence and Computer Architecture, with a strong focus on Machine Learning, Deep Learning, RISC-V, and Hardware Design using HDLs.

---

📘 Course Curriculum

Lecture 01 – Foundations

Topics Covered:

• Genify Overview
• Python Basics
• Digital Logic Design

---

Lecture 02 – Programming & Architecture

Topics Covered:

• Object-Oriented Programming (OOP) in Python
• RISC-V Instruction Set Architecture (ISA)

---

Lecture 03 – Machine Learning Basics

Topics Covered:

• Data Cleaning
• Feature Engineering
• Data Preprocessing
• Types of Machine Learning

---

Lecture 04 – Supervised Learning

Topics Covered:

• Machine Learning
• Supervised Machine Learning

---

Lecture 05 – Unsupervised & Reinforcement Learning

Topics Covered:

• Unsupervised Machine Learning
• Reinforcement Learning

---

Lecture 06 – Neural Networks & Architecture Review

Topics Covered:

• Artificial Neural Networks (ANNs)
• RISC-V Revision

---

Lecture 07 – RISC-V Core Design

Topics Covered:

• RISC-V Single-Cycle Core Deep Dive

---

Lecture 08 – Artificial Neural Networks

Topics Covered:

• Artificial Neural Networks (ANNs)

---

Lecture 09 – Convolutional Neural Networks (CNNs)

Topics Covered:

• Convolutional Neural Networks (CNNs)

---

Lecture 10 – CNNs Continued

Topics Covered:

• Advanced CNN Concepts

---

Lecture 11 – Hardware Description Languages

Topics Covered:

• Introduction to HDLs
• CHISEL HDL
• Verilog HDL

---

Lecture 12 – Recurrent Neural Networks

Topics Covered:

• Recurrent Neural Networks (RNNs)

---

Lecture 13 – Advanced RNNs & HDLs

Topics Covered:

• Long Short-Term Memory (LSTM) Networks
• Introduction to HDLs
  • CHISEL
  • SystemVerilog

---

Lecture 14 – Transformers (Part I)

Topics Covered:

• Transformers Overview
• Self-Attention Mechanism

---

Lecture 15 – Transformers (Part II)

Topics Covered:

• Deep Dive into Self-Attention

---

Lecture 16 – Transformer Architecture

Topics Covered:

• Multi-Head Attention
• Positional Encoding
• Encoder Architecture

---

Lecture 17 – Transformers (Decoder)

Topics Covered:

• Transformers Recap
• Decoder Architecture

---

Lecture 18 – Large Language Models

Topics Covered:

• LLM Inferencing
• Prompt Engineering
• Vector Databases

---

Lecture 19 – LLM Ecosystem

Topics Covered:

• Tokenization
• Vector Databases
• Semantic Search
• LLM Ecosystem Overview
• LangChain

---

Lecture 20 – Retrieval Augmented Generation

Topics Covered:

• Retrieval Augmented Generation (RAG)

---

Lecture Extra – AI Agents

Topics Covered:

• AI Agents
• AI Agent Frameworks

---

🛠 Technologies Covered

• Python
• Machine Learning & Deep Learning
• RISC-V Architecture
• Verilog & SystemVerilog
• CHISEL HDL
• Transformers & LLMs
• Vector Databases & RAG

---

📌 License

This project is intended for educational purposes.