Ahlad Kumar

Reinforcement Learning 1: Introduction

Deep Learning 19: (1) Variational AutoEncoder : Introduction and Probability Refresher

Deep Learning 6: Easy Understanding of Variables, Constants and Placeholders in Tensor Flow (Part A)

Deep Learning 1: Google Colaboratory and cloning GitHub repository

Deep Learning 33: Conditional Generative Adversarial Network (C-GAN) : Coding in Google Colab

Deep Learning 24: (6) Variational AutoEncoder : Implementation in Tensor Flow

Deep Learning 59: Fundamentals of Graph Neural Network

Lecture 16: AutoEncoders (Denoising, Sparse and Contractive)

Deep Learning 8: Running Tensorboard in Google Colaboratory for a simple Tensorflow based code

Deep Learning 3: Importing Kaggle's dataset in Google Colaboratory

Deep Learning 7: Simple Examples of Variables, Constants and Placeholders in TensorFlow (Part B)

Deep Learning 13: Dynamic visualization of Linear Regression parameters using Tensor Board

Deep Learning 2: How to Upload Data in Google Colaboratory

Deep Learning 60: Architecture of Graph Neural Network

Lecture 23

Deep Learning 4: Basic File operations in Google Colaboratory.

Lecture 4: Linear Regression 2

Lecture 12: Perceptron Algorithm

Lecture 6 : Kernel Regression

Deep Learning 62: Introduction to Recurrent Neural Network (RNN)

Lecture 7: Multi-Class and Multi-Label Classification

Lecture 5: Gradient Tape Continued: Higher Order Derivatives

Lecture 1: Basics of Signals and Systems (Signal operations)

Reinforcement Learning 4: Agent-Environment Interaction

Lecture 11: Logistic Regression 2

Lecture 14: Support Vector Machine 2

ISRO Sponsored Natural Language Processing (NLP) Seminar

Lecture 2: TensorFlow Constant, Reshape, Slicing and Broadcasting

ISRO Sponsored Generative Adversarial Network (GAN) Seminar

Lecture 3: TensorFlow Variables

Lecture 24 : Transformers

Lecture 2 : Basics of Machine Learning

Lecture 4: Gradient Tape

Lecture 10: Logistic Regression 1

Lecture 3: Linear Regression

Natural Language Processing 3: Text Pre-processing : Tokenization

Lecture 1: Introduction

Lecture 7: Role of Regularization in Regression

Lecture 8 : LASSO Regression

Lecture 5 : Non Linear Regression and Kernel Trick

Lecture 13: Support Vector Machine (SVM) 1

Lecture 9: Proximal Gradient Descent

Reinforcement Learning 1: Introduction

Lecture 15: Support Vector Machine 3 (Soft SVM and Kernel SVM)