Machine Learning Courses

Deploying our Fine Tuned LLM to Sagemaker

Fine Tuning LLMs in Sagemaker(PeFT, Mixed Precision, Quantization)

Fine Tune a Mixtral Model on Sagemaker Course(PeFT, LoRA, Quantization, Mixed Precision Training)

Mixed Precision Training: Bfloat16 vsFloat32

Implementing Mixed Precision Training in Sagemaker

Testing our Fine Tuned Sagemaker LLM with Streamlit, through API Gateway, and AWS Lambda

Understanding LoRA with Python Part 1

Understanding Double Quantization for LLMs

LoRA Numerical Example

LoRA Cost Saving Example

Understanding LoRA with Python Part 2

Introduction To Low Rank Adapters(LoRA)

LLM Chunking Part 5: Creating Our Chunking Function

LLM Chunking Part 4: Slicing and Chunking

LLM Chunking Part 3: Understanding Batching for Chunks

LLM Chunking Part 2: Chain Iterator and List Constructor Example ForTokenIds and Attention Mask

LLM Chunking Part 1: Understand Star Unpacking

Creating Lambda Function Action Group For the Restaurant Agent

Creating and Testing the Supervisor agent in AWS Bedrock

Testing the Accommodation Agent for Hotels and Airbnbs

Testing Our AWS Bedrock Multi Agentic Workflow with Postman

Deploying our Multi Agentic Workflow with AWS API Gateway

Multi Agentic Workflow on AWS Bedrock Demo

Architecture Diagram of a Multi-agentic workflow on AWS Bedrock

Calculate the Derivative of the Sigmoid Function for Backpropagation

Mathematics Behind Backpropagation | Theory and Python Code

Understanding Partial Derivatives for Backpropagation

Understanding the Chain Rule for Backpropagation

Visualizing the MSE Loss Function and Understanding Gradients

Using the Chain rule to Calculate the Gradient of w2