Section 1 : Introduction
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Lecture 1 | Welcome | 00:03:52 Duration |
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Lecture 2 | Overview and Outline | 00:13:03 Duration |
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Lecture 3 | Where to get the Code | 00:05:27 Duration |
Section 2 : Google Colab
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Lecture 1 | Intro to Google Colab, how to use a GPU or TPU for free | |
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Lecture 2 | Uploading your own data to Google Colab | 00:13:00 Duration |
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Lecture 3 | Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn | 00:08:22 Duration |
Section 3 : Machine Learning and Neurons
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Lecture 1 | What is Machine Learning | 00:14:15 Duration |
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Lecture 2 | Regression Basics | 00:14:28 Duration |
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Lecture 3 | Regression Code Preparation | 00:11:36 Duration |
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Lecture 4 | Regression Notebook | |
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Lecture 5 | Moore's Law | 00:06:47 Duration |
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Lecture 6 | Moore's Law Notebook | 00:13:41 Duration |
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Lecture 7 | About Certification | |
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Lecture 8 | Linear Classification Basics | 00:14:56 Duration |
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Lecture 9 | Classification Code Preparation | 00:06:42 Duration |
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Lecture 10 | Classification Notebook | 00:11:49 Duration |
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Lecture 11 | About Proctor Testing | |
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Lecture 12 | Saving and Loading a Model | 00:05:08 Duration |
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Lecture 13 | A Short Neuroscience Primer | 00:09:41 Duration |
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Lecture 14 | How does a model learn | 00:10:40 Duration |
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Lecture 15 | Model With Logits | 00:04:07 Duration |
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Lecture 16 | Train Sets vs | 00:10:02 Duration |
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Lecture 17 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM |
Section 4 : Feedforward Artificial Neural Networks
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Lecture 1 | Artificial Neural Networks Section Introduction | 00:05:49 Duration |
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Lecture 2 | Forward Propagation | 00:09:30 Duration |
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Lecture 3 | The Geometrical Picture | 00:09:34 Duration |
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Lecture 4 | Activation Functions | 00:17:08 Duration |
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Lecture 5 | Multiclass Classification | 00:09:28 Duration |
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Lecture 6 | How to Represent Images | 00:12:10 Duration |
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Lecture 7 | Code Preparation (ANN) | 00:14:46 Duration |
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Lecture 8 | ANN for Image Classification | 00:18:18 Duration |
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Lecture 9 | ANN for Regression | 00:10:43 Duration |
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Lecture 10 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM |
Section 5 : Convolutional Neural Networks
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Lecture 1 | What is Convolution (part 1) | 00:16:27 Duration |
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Lecture 2 | What is Convolution (part 2) | 00:05:46 Duration |
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Lecture 3 | What is Convolution (part 3) | 00:06:29 Duration |
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Lecture 4 | Convolution on Color Images | 00:15:58 Duration |
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Lecture 5 | CNN Architecture | 00:20:42 Duration |
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Lecture 6 | CNN Code Preparation (part 1) | 00:16:42 Duration |
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Lecture 7 | CNN Code Preparation (part 2) | 00:07:50 Duration |
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Lecture 8 | CNN Code Preparation (part 3) | 00:05:30 Duration |
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Lecture 9 | CNN for Fashion MNIST | 00:11:22 Duration |
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Lecture 10 | CNN for CIFAR-10 | 00:07:55 Duration |
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Lecture 11 | Data Augmentation | 00:09:33 Duration |
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Lecture 12 | Batch Normalization | 00:05:04 Duration |
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Lecture 13 | Improving CIFAR-10 Results | 00:10:36 Duration |
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Lecture 14 | About Certification |
Section 6 : Recurrent Neural Networks, Time Series, and Sequence Data
Section 7 : Natural Language Processing (NLP)
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Lecture 1 | Embeddings | 00:13:02 Duration |
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Lecture 2 | Neural Networks with Embeddings | 00:03:35 Duration |
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Lecture 3 | Text Preprocessing (pt 1) | 00:13:23 Duration |
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Lecture 4 | About Certification | |
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Lecture 5 | Text Preprocessing (pt 2) | 00:11:42 Duration |
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Lecture 6 | Text Preprocessing (pt 3) | |
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Lecture 7 | Text Classification with LSTMs | 00:08:45 Duration |
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Lecture 8 | CNNs for Text | 00:11:57 Duration |
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Lecture 9 | Text Classification with CNNs | 00:04:39 Duration |
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Lecture 10 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 11 | VIP Making Predictions with a Trained NLP Model | 00:07:27 Duration |
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Lecture 12 | Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM |
Section 8 : Recommender Systems
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Lecture 1 | Recommender Systems with Deep Learning Theory | 00:10:16 Duration |
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Lecture 2 | Recommender Systems with Deep Learning Code Preparation | 00:09:28 Duration |
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Lecture 3 | Recommender Systems with Deep Learning Code (pt 1) | 00:08:43 Duration |
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Lecture 4 | Recommender Systems with Deep Learning Code (pt 2) | 00:12:20 Duration |
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Lecture 5 | VIP Making Predictions with a Trained Recommender Model | 00:04:41 Duration |
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Lecture 6 | About Proctor Testing |
Section 9 : Transfer Learning for Computer Vision
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Lecture 1 | Transfer Learning Theory | 00:08:02 Duration |
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Lecture 2 | Some Pre-trained Models (VGG, ResNet, Inception, MobileNet) | 00:03:55 Duration |
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Lecture 3 | Large Datasets | 00:07:01 Duration |
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Lecture 4 | 2 Approaches to Transfer Learning | 00:04:41 Duration |
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Lecture 5 | Transfer Learning Code (pt 1) | 00:09:25 Duration |
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Lecture 6 | Transfer Learning Code (pt 2) | 00:07:30 Duration |
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Lecture 7 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM |
Section 10 : GANs (Generative Adversarial Networks)
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Lecture 1 | GAN Theory | 00:15:52 Duration |
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Lecture 2 | GAN Code Preparation | 00:06:08 Duration |
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Lecture 3 | GAN Code | 00:09:11 Duration |
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Lecture 4 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM |
Section 11 : Deep Reinforcement Learning (Theory)
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Lecture 1 | Deep Reinforcement Learning Section Introduction | 00:06:23 Duration |
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Lecture 2 | Elements of a Reinforcement Learning Problem | 00:20:08 Duration |
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Lecture 3 | States, Actions, Rewards, Policies | 00:09:15 Duration |
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Lecture 4 | Markov Decision Processes (MDPs) | 00:09:55 Duration |
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Lecture 5 | The Return | 00:04:46 Duration |
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Lecture 6 | Value Functions and the Bellman Equation | 00:09:43 Duration |
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Lecture 7 | What does it mean to “learn” | 00:07:05 Duration |
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Lecture 8 | Solving the Bellman Equation with Reinforcement Learning (pt 1) | 00:09:38 Duration |
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Lecture 9 | Solving the Bellman Equation with Reinforcement Learning (pt 2) | 00:11:51 Duration |
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Lecture 10 | Epsilon-Greedy | 00:05:56 Duration |
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Lecture 11 | Q-Learning | 00:14:04 Duration |
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Lecture 12 | Deep Q-Learning DQN (pt 1) | 00:13:55 Duration |
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Lecture 13 | Deep Q-Learning DQN (pt 2) | 00:10:10 Duration |
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Lecture 14 | How to Learn Reinforcement Learning | 00:05:47 Duration |
Section 12 : Stock Trading Project with Deep Reinforcement Learning
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Lecture 1 | Reinforcement Learning Stock Trader Introduction | 00:05:04 Duration |
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Lecture 2 | Data and Environment | 00:12:12 Duration |
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Lecture 3 | Replay Buffer | 00:05:30 Duration |
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Lecture 4 | Program Design and Layout | 00:06:46 Duration |
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Lecture 5 | Code pt 1 | 00:09:11 Duration |
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Lecture 6 | Code pt 2 | 00:09:30 Duration |
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Lecture 7 | Code pt 3 | 00:06:43 Duration |
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Lecture 8 | Code pt 4 | 00:07:14 Duration |
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Lecture 9 | Reinforcement Learning Stock Trader Discussion | 00:03:25 Duration |
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Lecture 10 | About Certification |
Section 13 : VIP Uncertainty Estimation
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Lecture 1 | Custom Loss and Estimating Prediction Uncertainty | 00:09:26 Duration |
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Lecture 2 | Estimating Prediction Uncertainty Code | 00:07:02 Duration |
Section 14 : VIP Facial Recognition
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Lecture 1 | Facial Recognition Section Introduction | 00:03:29 Duration |
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Lecture 2 | Siamese Networks | 00:10:08 Duration |
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Lecture 3 | Code Outline | 00:04:55 Duration |
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Lecture 4 | Loading in the data | 00:05:42 Duration |
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Lecture 5 | Splitting the data into train and test | 00:04:17 Duration |
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Lecture 6 | Converting the data into pairs | 00:04:54 Duration |
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Lecture 7 | Generating Generators | 00:04:55 Duration |
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Lecture 8 | Creating the model and loss | 00:04:18 Duration |
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Lecture 9 | Accuracy and imbalanced classes | 00:07:38 Duration |
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Lecture 10 | Facial Recognition Section Summary | 00:03:22 Duration |
Section 15 : In-Depth Loss Functions
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Lecture 1 | Mean Squared Error | |
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Lecture 2 | Binary Cross Entropy | 00:05:47 Duration |
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Lecture 3 | Categorical Cross Entropy | 00:07:56 Duration |
Section 16 : In-Depth Gradient Descent
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Lecture 1 | Gradient Descent | 00:07:42 Duration |
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Lecture 2 | Stochastic Gradient Descent | 00:04:25 Duration |
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Lecture 3 | Momentum | 00:05:57 Duration |
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Lecture 4 | Variable and Adaptive Learning Rates | 00:11:34 Duration |
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Lecture 5 | Adam | 00:11:06 Duration |
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Lecture 6 | About Proctor Testing |
Section 17 : Extras
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Lecture 1 | Links To Colab Notebooks | |
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Lecture 2 | Links to VIP Notebooks |
Section 18 : Setting up your Environment
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Lecture 1 | How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Thean | 00:17:30 Duration |
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Lecture 2 | Windows-Focused Environment Setup 2018 | 00:20:12 Duration |
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Lecture 3 | Installing NVIDIA GPU-Accelerated Deep Learning Libraries | 00:22:15 Duration |
Section 19 : Extra Help With Python Coding For Beginners
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Lecture 1 | How to Code Yourself (part 1) | 00:15:47 Duration |
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Lecture 2 | How to Code Yourself (part 2) | 00:09:23 Duration |
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Lecture 3 | Proof that using Jupyter Notebook is the same as not using | 00:12:24 Duration |
Section 20 : Effective Learning Startegies For Machine Learning
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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 | 00:21:57 Duration |
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Lecture 3 | What order should I take your courses in (part 1) | 00:11:12 Duration |
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Lecture 4 | What order should I take your courses in (part 2) | 00:16:07 Duration |
Section 21 : Appendix FAQ
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Lecture 1 | What is the Appendix | 00:02:41 Duration |