• 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