Deep Neural Networks

17 lectures

Architectures, training dynamics, and optimization behind modern deep learning models.

01Lecture 01 · Session 1: Introduction and Overview of Deep Neural Networks

02Lecture 02 · Deep Neural Network Components and Perceptron

03Lecture 03 · Perceptron Learning and Introduction to Regression

04Lecture 04 · Linear Neural Networks for Regression

05Lecture 05 · Gradient Descent Variants, Classification, and Evaluation

06Lecture 06 · Deep Feedforward Neural Networks

07Lecture 07 · Deep Feed-Forward Neural Networks

08Lecture 08 · Deep Neural Networks — Core Concepts and Introduction to CNNs

09Lecture 09 · Convolutional Neural Networks

10Lecture 10 · Convolutional Neural Networks — From Pixels to Patterns

11Lecture 11 · CNN Architectures and Applications

12Lecture 12 · Recurrent Neural Networks — Foundations and Architecture

13Lecture 13 · Recurrent Neural Networks — Advanced Architectures

14Lecture 14 · Attention Mechanisms

15Lecture 15 · Attention Mechanisms and Transformer Architecture

16Lecture 16 · Transformer Architectures and Optimization

17Lecture 17 · Optimization and Regularization

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