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Master 26+ GenAI Libraries and Frameworks
Covering 19+ modules, starting from data science for Python and progressing to advanced topics like building LLMs, stable diffusion, and reinforcement learning.
The journey to the current state of the art in NLP
Building LLM Applications using Tools and Frameworks
Training LLMs from scratch
Embark on an Exciting Journey with Generative AI
Fundamentals of Diffusion Models
Understanding Deepest levels needed for Diffusion Models
Stable Diffusion in Practice, Industrial Methods
Methods, Jobs and Tools of Stable Diffusion
Stable Diffusion Task to Achieve Purpose at hand.
State of Art and Cutting Edge of Stable Diffusion
Way Forward for Stable Diffusion
Mathematical Prerequisites for Reinforcement Learning
Simple Reinforcement Learning
Reinforcement Learning with Function Approximation
RLHF for LLMs
Python for Data Science
Data Exploration and Statistical Inference
Basics of Machine Learning
Fundamentals of Deep Learning
Implement training script to train and evaluate LLMs from scratch -Implement the best practices to train LLMs along with setting up the infrastructure.
Google and Meta
Implement steps involved: Pretraining, Supervised Finetuning and RLHF -Learn and implement the best industry practices to build a LLM like ChatGPT
Wells Fargo and Novartis
Use LLM tools and frameworks like LlamaIndex and LangChain to build a production ready RAG system on your private data
Amazon and Netflix
Fine-tune LLMs for various use cases like sentiment analysis, summarization, machine translation, etc., using PEFT to speed up the process on a single GPU.
Github and Replit