Section 1 : Welcome
Lecture 1 Introduction and Outline 00:04:19 Duration
Lecture 2 Who should take this course in 2020 and beyond. 00:08:45 Duration
Lecture 3 Where to get the code 00:04:52 Duration
Lecture 4 Anyone Can Succeed in this Course 00:11:44 Duration

Section 2 : Review
Lecture 1 Review Section Introduction 00:01:47 Duration
Lecture 2 Review Section Summary 00:03:37 Duration
Lecture 3 Neuron Predictions 00:04:49 Duration
Lecture 4 Neuron Training 00:08:36 Duration
Lecture 5 Deep Learning Readiness Test 00:05:25 Duration
Lecture 6 Review Section Introduction 00:01:47 Duration

Section 3 : Preliminaries From Neurons to Neural Networks
Lecture 1 Neural Networks with No Math 00:04:15 Duration
Lecture 2 Introduction to the E-Commerce Course Project 00:08:49 Duration

Section 4 : Classifying more than 2 things at a time
Lecture 1 Prediction Section Introduction and Outline 00:05:29 Duration
Lecture 2 From Logistic Regression to Neural Networks 00:05:05 Duration
Lecture 3 Interpreting the Weights of a Neural Network 00:07:55 Duration
Lecture 4 Softmax 00:02:44 Duration
Lecture 5 Sigmoid vs. Softmax 00:01:21 Duration
Lecture 6 Feedforward in Slow-Mo (part 1) 00:19:34 Duration
Lecture 7 Feedforward in Slow-Mo (part 2) 00:10:55 Duration
Lecture 8 Where to get the code for this course 00:01:30 Duration
Lecture 9 Softmax in Code 00:03:40 Duration
Lecture 10 Building an entire feedforward neural network 00:06:23 Duration
Lecture 11 E-Commerce Course Project Pre-Processing the 00:05:24 Duration
Lecture 12 E-Commerce Course Project Making Predictions
Lecture 13 Prediction Quizzes 00:03:15 Duration
Lecture 14 Prediction Section Summary 00:01:37 Duration
Lecture 15 Suggestion Box 00:02:27 Duration

Section 5 : Training a neural network
Lecture 1 Training Section Introduction and Outline 00:02:41 Duration
Lecture 2 What do all these symbols and letters mean 00:09:37 Duration
Lecture 3 What does it mean to train a neural network 00:06:35 Duration
Lecture 4 How to Brace Yourself to Learn Backpropagation 00:07:28 Duration
Lecture 5 Categorical Cross-Entropy Loss Function 00:10:50 Duration
Lecture 6 Training Logistic Regression with Softmax 00:14:41 Duration
Lecture 7 Training Logistic Regression with Softmax (Part 2 00:05:30 Duration
Lecture 8 Backpropagation (part 1) 00:05:03 Duration
Lecture 9 Backpropagation (part 2) 00:10:39 Duration
Lecture 10 Backpropagation in code 00:17:07 Duration
Lecture 11 Backpropagation (part 3) 00:16:01 Duration
Lecture 12 The WRONG Way to Learn Backpropagation 00:03:42 Duration
Lecture 13 E-Commerce Course Project Training Logistic 00:08:11 Duration
Lecture 14 E-Commerce Course Project Training a Neural 00:06:19 Duration
Lecture 15 Training Quiz 00:05:20 Duration
Lecture 16 Training Section Summary 00:02:31 Duration

Section 6 : Practical Machine Learning
Lecture 1 Practical Issues Section Introduction and 00:01:32 Duration
Lecture 2 Donut and XOR Review 00:00:29 Duration
Lecture 3 Donut and XOR Revisited 00:04:21 Duration
Lecture 4 Neural Networks for Regression 00:11:27 Duration
Lecture 5 Common nonlinearities and their derivatives 00:01:18 Duration
Lecture 6 Practical Considerations for Choosing Activati 00:07:37 Duration
Lecture 7 Hyperparameters and Cross-Validation 00:04:03 Duration
Lecture 8 Manually Choosing Learning Rate and Regulariza 00:04:00 Duration
Lecture 9 Why Divide by Square Root of D 00:06:27 Duration
Lecture 10 Practical Issues Section Summary 00:06:01 Duration

Section 7 : TensorFlow, exercises, practice, and what to learn next
Lecture 1 TensorFlow plug-and-play example 00:19:08 Duration
Lecture 2 Visualizing what a neural network has learned 00:11:36 Duration
Lecture 3 Where to go from here
Lecture 4 You know more than you think you know 00:04:47 Duration
Lecture 5 How to get good at deep learning + exercises 00:05:00 Duration
Lecture 6 Deep neural networks in just 3 lines of code 00:08:38 Duration

Section 8 : Project Facial Expression Recognition
Lecture 1 Facial Expression Recognition Project Introduc 00:04:45 Duration
Lecture 2 Facial Expression Recognition Problem Descript 00:11:00 Duration
Lecture 3 The class imbalance problem 00:05:44 Duration
Lecture 4 Utilities walkthrough 00:05:45 Duration
Lecture 5 acial Expression Recognition in Code 00:12:14 Duration
Lecture 6 Facial Expression Recognition in Code (Logisti 00:08:57 Duration
Lecture 7 Facial Expression Recognition in Code 00:10:45 Duration
Lecture 8 Facial Expression Recognition Project Summary 00:01:14 Duration

Section 9 : Backpropagation Supplementary Lectures
Lecture 1 Backpropagation Supplementary Lectures Introdu 00:00:54 Duration
Lecture 2 Why Learn the Ins and Outs of Backpropagation
Lecture 3 Gradient Descent Tutorial 00:04:22 Duration
Lecture 4 Help with Softmax Derivative 00:04:00 Duration
Lecture 5 Backpropagation with Softmax Troubleshooting 00:07:48 Duration

Section 10 : Higher-Level Discussion
Lecture 1 What's the difference between neural networks 00:11:46 Duration
Lecture 2 Who should learn backpropagation in 2020
Lecture 3 Where does this course fit into your deep 00:10:32 Duration

Section 11 : Setting Up Your Environment (FAQ by Student Request)
Lecture 1 Windows-Focused Environment Setup 2018 00:17:46 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Panda

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

Lecture 1 How to Uncompress a .tar.gz file 00:02:46 Duration
Lecture 2 How to Code by Yourself (part 1) 00:15:42 Duration
Lecture 3 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 4 Proof that using Jupyter Notebook is the same 00:12:24 Duration
Lecture 5 Python 2 vs Python 3 00:04:29 Duration

Section 13 : Effective Learning Strategies for Machine Learning (FAQ by S
Lecture 1 How to Succeed in this Course (Long Version) 00:10:17 Duration
Lecture 2 Is this for Beginners or Experts Academic 00:21:56 Duration
Lecture 3 Where does this course fit into your deep lear 00:04:49 Duration
Lecture 4 Machine Learning and AI Prerequisite Roadmap 00:11:12 Duration
Lecture 5 Machine Learning and AI Prerequisite Roadmap 00:16:07 Duration

Section 14 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:41 Duration