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Deep Learning - Basic Track

Taught as part of Google's Explore ML program at BITS Pilani Goa Campus (Fall 2019). Covers the fundamentals of deep learning — regression and classification, gradient descent, and neural network architectures for computer vision and NLP — through hands-on notebooks.

Prerequisites

  • Python 3 and basic programming (functions, OOP)
  • NumPy, Pandas, Matplotlib
  • Basic multivariable calculus, linear algebra, probability & statistics

Data & Learning Problem

0.1 Data and Learning Problem

Intro to Deep Learning

1.1 Linear Regression 1.2 Polynomial Regression 1.3 Logistic Regression 1.4 Multi Class Classification 1.5 Motivation for Multi Layer Perceptron

Deep Neural Networks

2.1 Neural Network Architectures 2.2 Batch Training 2.3 Optimizers 2.4 Learning Rate 2.5 Bias & Variance 2.6 Overfitting & Regularization 2.7 ANN - Medical Diagnosis 2.8 ANN - Computer Vision 2.9 ANN - Natural Language Processing