Section 1 : Welcome
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Lecture 1 | Introduction and Outline | 00:04:19 Duration |
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Lecture 2 | Who should take this course in 2020 and beyond. | 00:08:45 Duration |
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Lecture 3 | Where to get the code | 00:04:52 Duration |
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Lecture 4 | Anyone Can Succeed in this Course | 00:11:44 Duration |
Section 2 : Review
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Lecture 1 | Review Section Introduction | 00:01:47 Duration |
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Lecture 2 | Review Section Summary | 00:03:37 Duration |
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Lecture 3 | Neuron Predictions | 00:04:49 Duration |
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Lecture 4 | Neuron Training | 00:08:36 Duration |
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Lecture 5 | Deep Learning Readiness Test | 00:05:25 Duration |
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Lecture 6 | Review Section Introduction | 00:01:47 Duration |
Section 3 : Preliminaries From Neurons to Neural Networks
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Lecture 1 | Neural Networks with No Math | 00:04:15 Duration |
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Lecture 2 | Introduction to the E-Commerce Course Project | 00:08:49 Duration |
Section 4 : Classifying more than 2 things at a time
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Lecture 1 | Prediction Section Introduction and Outline | 00:05:29 Duration |
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Lecture 2 | From Logistic Regression to Neural Networks | 00:05:05 Duration |
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Lecture 3 | Interpreting the Weights of a Neural Network | 00:07:55 Duration |
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Lecture 4 | Softmax | 00:02:44 Duration |
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Lecture 5 | Sigmoid vs. Softmax | 00:01:21 Duration |
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Lecture 6 | Feedforward in Slow-Mo (part 1) | 00:19:34 Duration |
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Lecture 7 | Feedforward in Slow-Mo (part 2) | 00:10:55 Duration |
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Lecture 8 | Where to get the code for this course | 00:01:30 Duration |
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Lecture 9 | Softmax in Code | 00:03:40 Duration |
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Lecture 10 | Building an entire feedforward neural network | 00:06:23 Duration |
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Lecture 11 | E-Commerce Course Project Pre-Processing the | 00:05:24 Duration |
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Lecture 12 | E-Commerce Course Project Making Predictions | |
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Lecture 13 | Prediction Quizzes | 00:03:15 Duration |
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Lecture 14 | Prediction Section Summary | 00:01:37 Duration |
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Lecture 15 | Suggestion Box | 00:02:27 Duration |
Section 5 : Training a neural network
Section 6 : Practical Machine Learning
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Lecture 1 | Practical Issues Section Introduction and | 00:01:32 Duration |
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Lecture 2 | Donut and XOR Review | 00:00:29 Duration |
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Lecture 3 | Donut and XOR Revisited | 00:04:21 Duration |
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Lecture 4 | Neural Networks for Regression | 00:11:27 Duration |
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Lecture 5 | Common nonlinearities and their derivatives | 00:01:18 Duration |
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Lecture 6 | Practical Considerations for Choosing Activati | 00:07:37 Duration |
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Lecture 7 | Hyperparameters and Cross-Validation | 00:04:03 Duration |
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Lecture 8 | Manually Choosing Learning Rate and Regulariza | 00:04:00 Duration |
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Lecture 9 | Why Divide by Square Root of D | 00:06:27 Duration |
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Lecture 10 | Practical Issues Section Summary | 00:06:01 Duration |
Section 7 : TensorFlow, exercises, practice, and what to learn next
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Lecture 1 | TensorFlow plug-and-play example | 00:19:08 Duration |
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Lecture 2 | Visualizing what a neural network has learned | 00:11:36 Duration |
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Lecture 3 | Where to go from here | |
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Lecture 4 | You know more than you think you know | 00:04:47 Duration |
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Lecture 5 | How to get good at deep learning + exercises | 00:05:00 Duration |
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Lecture 6 | Deep neural networks in just 3 lines of code | 00:08:38 Duration |
Section 8 : Project Facial Expression Recognition
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Lecture 1 | Facial Expression Recognition Project Introduc | 00:04:45 Duration |
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Lecture 2 | Facial Expression Recognition Problem Descript | 00:11:00 Duration |
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Lecture 3 | The class imbalance problem | 00:05:44 Duration |
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Lecture 4 | Utilities walkthrough | 00:05:45 Duration |
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Lecture 5 | acial Expression Recognition in Code | 00:12:14 Duration |
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Lecture 6 | Facial Expression Recognition in Code (Logisti | 00:08:57 Duration |
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Lecture 7 | Facial Expression Recognition in Code | 00:10:45 Duration |
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Lecture 8 | Facial Expression Recognition Project Summary | 00:01:14 Duration |
Section 9 : Backpropagation Supplementary Lectures
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Lecture 1 | Backpropagation Supplementary Lectures Introdu | 00:00:54 Duration |
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Lecture 2 | Why Learn the Ins and Outs of Backpropagation | |
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Lecture 3 | Gradient Descent Tutorial | 00:04:22 Duration |
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Lecture 4 | Help with Softmax Derivative | 00:04:00 Duration |
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Lecture 5 | Backpropagation with Softmax Troubleshooting | 00:07:48 Duration |
Section 10 : Higher-Level Discussion
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Lecture 1 | What's the difference between neural networks | 00:11:46 Duration |
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Lecture 2 | Who should learn backpropagation in 2020 | |
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Lecture 3 | Where does this course fit into your deep | 00:10:32 Duration |
Section 11 : Setting Up Your Environment (FAQ by Student Request)
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Lecture 1 | Windows-Focused Environment Setup 2018 | 00:17:46 Duration |
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Lecture 2 | How to install Numpy, Scipy, Matplotlib, Panda |
Section 12 : Extra Help With Python Coding for Beginners (FAQ by Student
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Lecture 1 | How to Uncompress a .tar.gz file | 00:02:46 Duration |
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Lecture 2 | How to Code by Yourself (part 1) | 00:15:42 Duration |
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Lecture 3 | How to Code by Yourself (part 2) | 00:09:23 Duration |
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Lecture 4 | Proof that using Jupyter Notebook is the same | 00:12:24 Duration |
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Lecture 5 | Python 2 vs Python 3 | 00:04:29 Duration |
Section 13 : Effective Learning Strategies for Machine Learning (FAQ by S
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Lecture 1 | How to Succeed in this Course (Long Version) | 00:10:17 Duration |
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Lecture 2 | Is this for Beginners or Experts Academic | 00:21:56 Duration |
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Lecture 3 | Where does this course fit into your deep lear | 00:04:49 Duration |
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Lecture 4 | Machine Learning and AI Prerequisite Roadmap | 00:11:12 Duration |
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Lecture 5 | Machine Learning and AI Prerequisite Roadmap | 00:16:07 Duration |
Section 14 : Appendix FAQ Finale
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Lecture 1 | What is the Appendix | 00:02:41 Duration |