Week 6
Week-1
NumPy and Matplotlib
Vectors
Plotting Simple Curves
Matrices
Sampling from Distributions, Bar Plots, Histograms and Scatter plots
NumPy Arrays
Pandas
Apply and Map
Compare
Introduction to Pandas
Pandas Introduction
PivotTable
Week-2
Structured Approach to ML Projects
Week-3
Numpy array to Tensor
3 CNN using Pytorch
4 RNN using pytorch
DL workshop on PyTorch
Mini batch gradient descent
Pytorch
Gradient accumulation and clearing the gradients
Week-4
Loading Images
A Practical Guide to Image Manipulation with Pillow and OpenCV
— Data Augmentation Examples (PyTorch v2) —
Transfer Learning
Week-5
Introduction to the Hugging Face Ecosystem: Datasets and Tokenizers
Fine Tuning Hands On
Summarization
W5 Fine TuningBERT
W5 L2 FineTuningGPT2
Week 6
Module 1: Introduction to Prompt Engineering
Module 2: Techniques in Prompting
Module 3: Applications of Prompt Engineering
Module 4: Introduction to LLM Platforms
Module 5: Getting Started with OpenAI API
Module 6: Hands-on with OpenAI API
Module 7: Working with Text Completion in OpenAI
Module 8: Evaluating Prompt Quality and Output Reliability
Module 9: Introduction to LangChain
Module 10: Core Components of LangChain – LLMs, PromptTemplates, OutputParsers
Module 11: LangChain Chains – Sequential, SimpleSequentialChain, RouterChain
Module 12: LangChain Memory – Making LLMs Remember Context
Module 13: PromptTemplates in Depth – Partial Templates, Nesting, Debugging
Week-7
LangChain Basics
RAG
Introduction to Vector Databases
Week-10
Date Time Index
Time Resampling
3 Classical_decomposition
4 STL_Decomposition
5 Stationarity
Make data stationary
Other Links
Discussion Forum
Week 6
Prompt Engineering
W5 L2 FineTuningGPT2
Module 1: Introduction to Prompt Engineering