Section 1 : INTRODUCTION AND COURSE OUTLINE
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Lecture 1 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 2 | About Certification | |
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Lecture 3 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 4 | ML, AI and DL | 00:12:00 Duration |
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Lecture 5 | Machine Learning Big Picture | 00:08:15 Duration |
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Lecture 6 | About Proctor Testing | |
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Lecture 7 | Whats New in TensorFlow 2 | 00:15:07 Duration |
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Lecture 8 | What is Google Colab | 00:05:08 Duration |
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Lecture 9 | Google Colab Demo | 00:07:17 Duration |
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Lecture 10 | Eager Execution | 00:10:30 Duration |
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Lecture 11 | Keras API | 00:06:56 Duration |
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Lecture 12 | About Certification |
Section 2 : REVIEW OF ARTIFICIAL NEURAL NETWORKS AND CONVOLUTIONAL NEURAL NETWORKS
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Lecture 1 | ANN and CNN - Part 1 | 00:17:48 Duration |
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Lecture 2 | ANN and CNN - Part 2 | 00:08:13 Duration |
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Lecture 3 | ANN and CNN - Part 3 | 00:13:33 Duration |
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Lecture 4 | ANN and CNN - Part 4 | 00:05:33 Duration |
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Lecture 5 | ANN and CNN - Part 5 | 00:10:54 Duration |
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Lecture 6 | ANN and CNN - Part 6 | 00:05:56 Duration |
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Lecture 7 | ANN and CNN - Part 7 | 00:16:24 Duration |
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Lecture 8 | ANN and CNN - Part 8 | |
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Lecture 9 | Project 1 - Solution Part 1 | 00:06:06 Duration |
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Lecture 10 | Project 1 - Solution Part 2 | 00:12:33 Duration |
Section 3 : TRANSFER LEARNING (TF HUB)
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Lecture 1 | What is Transfer learning | 00:08:26 Duration |
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Lecture 2 | Transfer Learning Process | 00:10:10 Duration |
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Lecture 3 | Transfer Learning Strategies | 00:07:54 Duration |
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Lecture 4 | ImageNet | 00:08:35 Duration |
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Lecture 5 | Transfer Learning Project 1 - Coding P1 | 00:09:51 Duration |
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Lecture 6 | Transfer Learning Project 1 - Coding P2 | 00:14:32 Duration |
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Lecture 7 | Transfer Learning Project 1 - Coding P3 | 00:10:17 Duration |
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Lecture 8 | Transfer Learning Project 1 - Coding P4 | 00:11:25 Duration |
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Lecture 9 | Transfer Learning Project 1 - Coding P5 | 00:08:02 Duration |
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Lecture 10 | Transfer Learning Project 2 - Coding P1 | 00:05:23 Duration |
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Lecture 11 | Transfer Learning Project 2 - Coding P2 | 00:07:14 Duration |
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Lecture 12 | Transfer Learning Project 2 - Coding P3 | 00:09:35 Duration |
Section 4 : AUTOENCODERS
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Lecture 1 | Autoencoders intuition | 00:12:29 Duration |
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Lecture 2 | Autencoders Math | 00:14:49 Duration |
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Lecture 3 | Linear Autoencoders vs | 00:05:53 Duration |
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Lecture 4 | Autoencoders Applications | 00:10:16 Duration |
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Lecture 5 | Variational Autoencoders (VARS) | 00:08:21 Duration |
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Lecture 6 | Autoencoders CNN Dimensionality Review | 00:09:37 Duration |
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Lecture 7 | Autoencoders Project 1 - Coding P1 | 00:09:48 Duration |
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Lecture 8 | Autoencoders Project 1 - Coding P2 | 00:08:44 Duration |
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Lecture 9 | Autoencoders Project 1 - Coding P3 | 00:09:10 Duration |
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Lecture 10 | Autoencoders Project 1 - Coding P4 | 00:09:32 Duration |
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Lecture 11 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 12 | Autoencoders Project 2 - Coding P1 | 00:12:03 Duration |
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Lecture 13 | Autoencoders Project 2 - Coding P2 | 00:21:14 Duration |
Section 5 : DEEP DREAM
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Lecture 1 | What is Deep Dream | 00:13:25 Duration |
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Lecture 2 | How does DeepDream Algo work | |
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Lecture 3 | Deep Dream Simpified | 00:06:36 Duration |
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Lecture 4 | Deep Dream Coding P1 | |
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Lecture 5 | Deep Dream Coding P2 | 00:09:04 Duration |
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Lecture 6 | Deep Dream Coding P3 | 00:05:51 Duration |
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Lecture 7 | Deep Dream Coding P4 | 00:11:39 Duration |
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Lecture 8 | Deep Dream Coding P5 | 00:19:18 Duration |
Section 6 : GANs
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Lecture 1 | GANS intuition | 00:10:51 Duration |
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Lecture 2 | Discriminator and Generator Networks | 00:13:39 Duration |
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Lecture 3 | Let's put the Discriminator and Generator together | 00:13:39 Duration |
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Lecture 4 | GAN Lab | 00:12:19 Duration |
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Lecture 5 | GANs applications | |
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Lecture 6 | GANS Project 1 P1 | 00:08:09 Duration |
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Lecture 7 | GANS Project 1 P2 | 00:10:53 Duration |
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Lecture 8 | GANS Project 1 P3 | 00:04:00 Duration |
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Lecture 9 | GANS Project 1 P4 | 00:05:38 Duration |
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Lecture 10 | GANS Project 1 P5 | 00:12:57 Duration |
Section 7 : RECURRENT NEURAL NETWORKS (RNNs) AND LSTMs
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Lecture 1 | Recurrent Neural Networks Intuition | 00:04:47 Duration |
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Lecture 2 | RNN Architecture | 00:09:17 Duration |
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Lecture 3 | What makes RNN so special | 00:06:51 Duration |
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Lecture 4 | RNN Math | 00:05:46 Duration |
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Lecture 5 | Fun with RNN | 00:07:07 Duration |
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Lecture 6 | Vanishing Gradient Problem | 00:12:19 Duration |
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Lecture 7 | Long Short Term Memory LSTM | |
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Lecture 8 | RNN Project #1 - Part #1 | 00:08:18 Duration |
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Lecture 9 | RNN Project #1 - Part #2 | 00:06:14 Duration |
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Lecture 10 | RNN Project #1 - Part #3 | 00:05:44 Duration |
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Lecture 11 | RNN Project #1 - Part #4 | 00:07:58 Duration |
Section 8 : TENSORFLOW SERVING AND TENSORBOARD
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Lecture 1 | TF Serving Coding Part 1 | 00:09:11 Duration |
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Lecture 2 | TF Serving Coding Part 2 | 00:07:50 Duration |
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Lecture 3 | TF Serving Coding Part 3 | 00:12:18 Duration |
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Lecture 4 | Tensorboard Example 1 | 00:12:23 Duration |
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Lecture 5 | Tensorboard Example 2 | 00:09:21 Duration |
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Lecture 6 | Distributed Strategy | 00:03:10 Duration |