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Getting Start

Below are the course contents that will be noted in this particular parent section “Deep Learning Course Content”

Course Content

Unit 1: Foundations & Applied Math (8 Hours)

  • Introduction and History: Motivation for Deep Learning; Historical trends; Success stories.
  • Linear Algebra & Probability: Tensors, Eigendecomposition, Information Theory, and Numerical Optimization.
  • Bayesian Decision Theory: Making optimal decisions under uncertainty, inference vs. decision, and loss functions for classification/regression.
  • Machine Learning Basics: Capacity, Overfitting/Underfitting, Hyperparameters, and the Bias-Variance tradeoff.

Unit 2: Deep Networks & Training Optimization (12 Hours)

  • Deep Feedforward Networks: Multilayer Perceptrons (MLP); Gradient-Based Learning; Backpropagation and the Chain Rule.
  • Modern Regularization: L1/L2 penalties, Dropout, Early Stopping, and Dataset Augmentation.
  • Optimization & Normalization: SGD, Momentum, Adam Optimizer; Batch Normalization and Layer Normalization.

Unit 3: Convolutional Networks & Computer Vision (10 Hours)

  • The Convolution Operation: Motivation, Pooling, and the Neuroscientific basis for CNNs.
  • Modern Vision Architectures: Residual Networks (ResNets), Inception, and Deep CNN variants.
  • Advanced Vision Tasks: Object Detection (YOLO/SSD), Semantic Segmentation, and the U-Net architecture.

Unit 4: Sequence Modeling & The Attention Revolution (10 Hours)

  • Recurrent Neural Networks: RNNs, the Vanishing Gradient problem, and Gated Units (LSTM and GRU).
  • The Attention Mechanism: Self-Attention, Multi-head Attention, and the “Attention is All You Need” paradigm.
  • The Transformer Blueprint: Encoder-Decoder architecture, Positional Encoding, and scaling to Large Language Models (LLMs).

Unit 5: Frontiers: Generative & Graph Models (8 Hours)

  • Autoencoders & Latent Spaces: Undercomplete autoencoders and Representation Learning.
  • Generative AI: Variational Autoencoders (VAEs) and Diffusion Models.
  • Graph Neural Networks: Message Passing, Node Embeddings, and Graph Convolutional Networks (GCNs).

Notation