Course Content
10 – Class Activation Maps
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001 Class Activation Maps (Theory).mp4
07:09
002 Class Activation Maps (Code).mp4
09:54
18 – Appendix FAQ Finale
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001 What is the Appendix.mp4
02:47
002 BONUS.mp4
05:47
17 – Effective Learning Strategies for Machine Learning (FAQ by Student Request)
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001 How to Succeed in this Course (Long Version).mp4
10:24
002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
22:04
003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4
11:18
004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4
16:07
16 – Extra Help With Python Coding for Beginners (FAQ by Student Request)
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001 How to Code by Yourself (part 1).mp4
15:54
002 How to Code by Yourself (part 2).mp4
09:22
003 Proof that using Jupyter Notebook is the same as not using it.mp4
12:29
004 Python 2 vs Python 3.mp4
04:38
005 How to use Github & Extra Coding Tips (Optional).mp4
11:12
15 – Setting Up Your Environment (FAQ by Student Request)
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001 Pre-Installation Check.mp4
04:12
002 Anaconda Environment Setup.mp4
20:20
003 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
17:30
14 – Course Conclusion
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001 What to Learn Next.mp4
02:59
13 – Keras and Tensorflow 2 Basics Review
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001 (Review) Tensorflow Basics.mp4
07:27
002 (Review) Tensorflow Neural Network in Code.mp4
09:43
003 (Review) Keras Discussion.mp4
06:48
004 (Review) Keras Neural Network in Code.mp4
06:37
005 (Review) Keras Functional API.mp4
04:26
006 (Review) How to easily convert Keras into Tensorflow 2.0 code.mp4
01:49
12 – Object Localization Project
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001 Localization Introduction and Outline.mp4
13:37
002 Localization Code Outline (pt 1).mp4
10:39
003 Localization Code (pt 1).mp4
09:10
004 Localization Code Outline (pt 2).mp4
04:51
005 Localization Code (pt 2).mp4
11:03
006 Localization Code Outline (pt 3).mp4
03:18
007 Localization Code (pt 3).mp4
04:16
008 Localization Code Outline (pt 4).mp4
03:19
009 Localization Code (pt 4).mp4
02:06
010 Localization Code Outline (pt 5).mp4
07:42
011 Localization Code (pt 5).mp4
08:39
012 Localization Code Outline (pt 6).mp4
07:06
013 Localization Code (pt 6).mp4
07:37
014 Localization Code Outline (pt 7).mp4
04:58
015 Localization Code (pt 7).mp4
12:07
11 – GANs (Generative Adversarial Networks)
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001 GAN Theory.mp4
15:51
002 GAN Code.mp4
12:10
01 – Welcome
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001 Introduction.mp4
02:35
002 Outline and Perspective.mp4
06:49
003 How to Succeed in this Course.mp4
03:03
09 – Neural Style Transfer
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001 Style Transfer Section Intro.mp4
02:53
002 Style Transfer Theory.mp4
11:23
003 Optimizing the Loss.mp4
08:02
004 Code pt 1.mp4
07:46
005 Code pt 2.mp4
07:13
006 Code pt 3.mp4
03:50
007 Style Transfer Section Summary.mp4
02:22
08 – Object Detection (SSD RetinaNet)
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001 SSD Section Intro.mp4
05:04
002 Object Localization.mp4
06:36
003 What is Object Detection.mp4
02:53
004 How would you find an object in an image.mp4
08:40
005 The Problem of Scale.mp4
03:47
006 The Problem of Shape.mp4
03:52
007 SSD Tensorflow Object Detection API (pt 1).mp4
12:04
008 SSD Tensorflow Object Detection API (pt 2).mp4
12:15
009 SSD for Video Object Detection.mp4
11:59
010 Optional Intersection over Union & Non-max Suppression.mp4
05:06
011 SSD Section Summary.mp4
02:52
07 – ResNet (and Inception)
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001 ResNet Section Intro.mp4
02:49
002 ResNet Architecture.mp4
12:44
003 Transfer Learning with ResNet in Code.mp4
08:31
004 Blood Cell Images Dataset.mp4
03:02
005 How to Build ResNet in Code.mp4
11:16
006 1×1 Convolutions.mp4
04:03
007 Optional Inception.mp4
06:47
008 Different sized images using the same network.mp4
04:12
009 ResNet Section Summary.mp4
02:26
06 – VGG and Transfer Learning
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001 VGG Section Intro.mp4
03:04
002 What’s so special about VGG.mp4
07:00
003 Transfer Learning.mp4
08:22
004 Relationship to Greedy Layer-Wise Pretraining.mp4
02:19
005 Getting the data.mp4
02:16
006 Code pt 1.mp4
09:22
007 Code pt 2.mp4
03:41
008 Code pt 3.mp4
03:26
009 VGG Section Summary.mp4
01:47
05 – Convolutional Neural Networks (CNN) Review
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001 What is Convolution (part 1).mp4
16:38
002 What is Convolution (part 2).mp4
05:56
003 What is Convolution (part 3).mp4
06:41
004 Convolution on Color Images.mp4
15:58
005 CNN Architecture.mp4
20:58
006 CNN Code Preparation.mp4
15:13
007 CNN for Fashion MNIST.mp4
06:46
008 CNN for CIFAR-10.mp4
04:28
009 Data Augmentation.mp4
08:51
010 Batch Normalization.mp4
05:14
011 Improving CIFAR-10 Results.mp4
10:22
04 – Artificial Neural Networks (ANN) Review
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001 Artificial Neural Networks Section Introduction.mp4
06:00
002 Forward Propagation.mp4
09:40
003 The Geometrical Picture.mp4
09:43
004 Activation Functions.mp4
17:18
005 Multiclass Classification.mp4
08:41
006 How to Represent Images.mp4
12:36
007 Color Mixing Clarification.mp4
00:54
008 Code Preparation (ANN).mp4
12:42
009 ANN for Image Classification.mp4
08:36
010 ANN for Regression.mp4
11:05
03 – Machine Learning Basics Review
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001 What is Machine Learning.mp4
14:25
002 Code Preparation (Classification Theory).mp4
15:58
003 Beginner’s Code Preamble.mp4
04:38
004 Classification Notebook.mp4
22:21
005 Code Preparation (Regression Theory).mp4
07:18
006 Regression Notebook.mp4
27:28
007 The Neuron.mp4
09:58
008 How does a model learn.mp4
10:53
009 Making Predictions.mp4
06:45
010 Saving and Loading a Model.mp4
04:27
011 Suggestion Box.mp4
03:10
02 – Google Colab & Getting Setup
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001 Where to get the code, notebooks, and data.mp4
04:29
002 Intro to Google Colab, how to use a GPU or TPU for free.mp4
12:32
003 Uploading your own data to Google Colab.mp4
11:41
004 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4
11:00
005 Temporary 403 Errors.mp4
02:57
[Last updated 7/2024] Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) (Udemy – Vietsub and Engsub)
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