DLP-NLP-W4

LLM Finetuning Techniques

DLP_NLP_W3

DLP-Lecture-2

DLP-Lecture-1

Transformers-Distilled-HF-Workshop-IITMBS

Opportunities-in-the-llm-field

Kolmogorov-Arnold-Networks (KAN)

Intro to KAN

PostNorm-vs-PreNorm

Lecture-9-Positional-Encoding-Schemes

Lecture-7-Pre-training Dataset

Lecture-8-Fast Attention Mechanisms

Lecture-6-BigPicture

Lecture-5-T5

Lecture-3-BERT

Linear Regression - Kernel Regression - ProbabilisticView

MLT - Week5 Session slides

Kernel-PCA-MLT

A presentation used in Machine Learning Techniques Week-2 live session

IntrotoGPT-Decoding-Strategies

Lecture-4-Tokenizers

Covers Different Types of tokenizers used in NLP

Introduction-To-Transformers

Pytorch

A walkthrough over Pytorch framework to develop dl models using both low level and high-level APIs

Transformers-A-Short-Version

CS6910: Lecture 5-Part2

Workshop-IITM-BS-Math

CS6910: Lecture 1

CS6910: Lecture 4

Sigmoid Neuron to Feedforward Neural Networks

CS6910: Lecture 3

Sigmoid Neuron to Feedforward Neural Networks

CS6910: Lecture 2

A (brief/partial) History of Deep Learning

Pre-Training_and_fine-tuning

ML_Tamil_w1

Copy of CS6910: Lecture 3

Sigmoid Neuron to Feedforward Neural Networks

CS6910: Lecture 5

CS6910:Lecture-11

CS6910: Lecture 7

CS6910: Lecture 6

MLP_Linear regression

MLF_SWI_5

MLP_Week1

MLF_Tutorial_5

MLF_SWI_1

Geogebra for Interactive Teaching

Sensitization on Geogebra

Lecture 5

Sigmoid Neuron to Feedforward Neural Networks