The Full Applied GenAI Curriculum

Python Crash Course I

ยท Python fundamentals

ยท Experience writing simple Python programs

Python Crash Course II

ยท Jupyter Notebooks and Google Colab

ยท Python Libraries for Machine Learning

ยท Hands-on with Large Language Models

ยท Accessing popular foundation models via their APIs

ยท Working with open-source models via Huggingface Transformers

ยท Working with Stable Diffusion, LoRA

Gen AI Background

ยท Overview of Generative AI

ยท Applications of Generative AI

ยท Recent History of Generative AI

ยท Methods for Evaluating Generative AI Models

ยท Ethical Considerations in Generative AI

Neural Networks Background

ยท Architecture of feedforward neural networks

ยท Activation functions

ยท Training neural networks

ยท Recurrent Neural Networks (RNNs)

ยท Convolutional Neural Networks (CNNs)

Deep Dive into LLMs

ยท Probabilistic language models

ยท The Transformer Architecture

ยท Scaling Laws

Building Applications with LLMs

ยท Retrieval-augmented generation (RAG)

ยท Agents

ยท LangChain as a toolkit for LLM applications

Training LLMs

ยท Pretraining LLMs

ยท Fine-tuning LLMs and LoRA

ยท Instruction Tuning

ยท Learning from Preferences (RLHF and DPO)

GenAI for Images

ยท Diffusion Process (DDPM) and Stable Diffusion

ยท Latent Diffusion

ยท Fine-tuning diffusion models and LoRA (Low-Rank Adaptation of Large Language Models)

ยท Visual Transformers and applications

GenAI for Audio

ยท Overview of Text-to-Speech Synthesis: Waveform Generation techniques

ยท Creating New Music and Sounds with WaveNet and GANs

ยท Voice Models and Architecture: Vocoders, Tacotron, etc.

ยท Integrating Voice with LLMs

Hands-On Project

ยท Create LLM-based applications

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