Section 1 : Welcome

lecture 1 Introduction and Outline 4:19
lecture 2 Who should take this course in 2020 and beyond. 8:45
lecture 3 Where to get the code 4:52
lecture 4 Anyone Can Succeed in this Course 11:44

Section 2 : Review

lecture 5 Review Section Introduction 1:47
lecture 6 Review Section Summary 3:37
lecture 7 Neuron Predictions 4:49
lecture 8 Neuron Training 8:36
lecture 9 Deep Learning Readiness Test 5:25
lecture 10 Review Section Introduction 1:47

Section 3 : Preliminaries From Neurons to Neural Networks

lecture 11 Neural Networks with No Math 4:15
lecture 12 Introduction to the E-Commerce Course Project 8:49

Section 4 : Classifying more than 2 things at a time

lecture 13 Prediction Section Introduction and Outline 5:29
lecture 14 From Logistic Regression to Neural Networks 5:5
lecture 15 Interpreting the Weights of a Neural Network 7:55
lecture 16 Softmax 2:44
lecture 17 Sigmoid vs. Softmax 1:21
lecture 18 Feedforward in Slow-Mo (part 1) 19:34
lecture 19 Feedforward in Slow-Mo (part 2) 10:55
lecture 20 Where to get the code for this course 1:30
lecture 21 Softmax in Code 3:40
lecture 22 Building an entire feedforward neural network 6:23
lecture 23 E-Commerce Course Project Pre-Processing the 5:24
lecture 24 E-Commerce Course Project Making Predictions
lecture 25 Prediction Quizzes 3:15
lecture 26 Prediction Section Summary 1:37
lecture 27 Suggestion Box 2:27

Section 5 : Training a neural network

lecture 28 Training Section Introduction and Outline 2:41
lecture 29 What do all these symbols and letters mean 9:37
lecture 30 What does it mean to train a neural network 6:35
lecture 31 How to Brace Yourself to Learn Backpropagation 7:28
lecture 32 Categorical Cross-Entropy Loss Function 10:50
lecture 33 Training Logistic Regression with Softmax 14:41
lecture 34 Training Logistic Regression with Softmax (Part 2 5:30
lecture 35 Backpropagation (part 1) 5:3
lecture 36 Backpropagation (part 2) 10:39
lecture 37 Backpropagation in code 17:7
lecture 38 Backpropagation (part 3) 16:1
lecture 39 The WRONG Way to Learn Backpropagation 3:42
lecture 40 E-Commerce Course Project Training Logistic 8:11
lecture 41 E-Commerce Course Project Training a Neural 6:19
lecture 42 Training Quiz 5:20
lecture 43 Training Section Summary 2:31

Section 6 : Practical Machine Learning

lecture 44 Practical Issues Section Introduction and 1:32
lecture 45 Donut and XOR Review 0:29
lecture 46 Donut and XOR Revisited 4:21
lecture 47 Neural Networks for Regression 11:27
lecture 48 Common nonlinearities and their derivatives 1:18
lecture 49 Practical Considerations for Choosing Activati 7:37
lecture 50 Hyperparameters and Cross-Validation 4:3
lecture 51 Manually Choosing Learning Rate and Regulariza 4:0
lecture 52 Why Divide by Square Root of D 6:27
lecture 53 Practical Issues Section Summary 6:1

Section 7 : TensorFlow, exercises, practice, and what to learn next

lecture 54 TensorFlow plug-and-play example 19:8
lecture 55 Visualizing what a neural network has learned 11:36
lecture 56 Where to go from here
lecture 57 You know more than you think you know 4:47
lecture 58 How to get good at deep learning + exercises 5:0
lecture 59 Deep neural networks in just 3 lines of code 8:38

Section 8 : Project Facial Expression Recognition

lecture 60 Facial Expression Recognition Project Introduc 4:45
lecture 61 Facial Expression Recognition Problem Descript 11:0
lecture 62 The class imbalance problem 5:44
lecture 63 Utilities walkthrough 5:45
lecture 64 acial Expression Recognition in Code 12:14
lecture 65 Facial Expression Recognition in Code (Logisti 8:57
lecture 66 Facial Expression Recognition in Code 10:45
lecture 67 Facial Expression Recognition Project Summary 1:14

Section 9 : Backpropagation Supplementary Lectures

lecture 68 Backpropagation Supplementary Lectures Introdu 0:54
lecture 69 Why Learn the Ins and Outs of Backpropagation
lecture 70 Gradient Descent Tutorial 4:22
lecture 71 Help with Softmax Derivative 4:0
lecture 72 Backpropagation with Softmax Troubleshooting 7:48

Section 10 : Higher-Level Discussion

lecture 73 What's the difference between neural networks 11:46
lecture 74 Who should learn backpropagation in 2020
lecture 75 Where does this course fit into your deep 10:32

Section 11 : Setting Up Your Environment (FAQ by Student Request)

lecture 76 Windows-Focused Environment Setup 2018 17:46
lecture 77 How to install Numpy, Scipy, Matplotlib, Panda

Section 12 : Extra Help With Python Coding for Beginners (FAQ by Student

lecture 78 How to Uncompress a .tar.gz file 2:46
lecture 79 How to Code by Yourself (part 1) 15:42
lecture 80 How to Code by Yourself (part 2) 9:23
lecture 81 Proof that using Jupyter Notebook is the same 12:24
lecture 82 Python 2 vs Python 3 4:29

Section 13 : Effective Learning Strategies for Machine Learning (FAQ by S

lecture 83 How to Succeed in this Course (Long Version) 10:17
lecture 84 Is this for Beginners or Experts Academic 21:56
lecture 85 Where does this course fit into your deep lear 4:49
lecture 86 Machine Learning and AI Prerequisite Roadmap 11:12
lecture 87 Machine Learning and AI Prerequisite Roadmap 16:7

Section 14 : Appendix FAQ Finale

lecture 88 What is the Appendix 2:41