[Last updated 7/2024] Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) (Udemy – Vietsub and Engsub)
About Course
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Unlock the power of advanced computer vision with our comprehensive course, “
Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
“
What you’ll learn:
Understand and apply transfer learning
Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception
Understand and use object detection algorithms like SSD
Understand and apply neural style transfer
Understand state-of-the-art computer vision topics
Class Activation Maps
GANs (Generative Adversarial Networks)
Object Localization Implementation Project
Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
Description:
This course is ideal for data scientists, AI researchers, software developers, and anyone interested in mastering
advanced computer vision
techniques. Whether you’re looking to advance your career or expand your expertise, this course provides the tools and knowledge you need.
Link gốc:
https://www.udemy.com/course/advanced-computer-vision/
Time Course:
16 hours (116 Lectures + Documents)
Instructor
: Lazy Programmer Inc.
Total Weight:
5.3 GB
** Note
:
Chú ý:
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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02:35
-
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
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