资源介绍：

Deep Learning Advanced Computer Vision (GANs, SSD, +More!)

深度学习：高级计算机视觉教程（英文外语教学）

推荐学习《深度学习高级计算机视觉（GANs、SSD等）》！这个课程涵盖了计算机视觉领域的高级主题，如生成式对抗网络（GANs）和单发多框检测器（SSD）。学习这些技能可以让你更好地处理图像数据和进行视觉分析

理解它。但现在，我们没有必要一步步实现所有这些高级应用程序，因为我们可以使用现成的库和框架。所以让我们开始探索这个激动人心的领域，看看我们能创造出什么样的东西！

课程目录

• 1. Welcome
• 1. Introduction39
• 1. Introduction39.srt
• 2. Outline and Perspective
• 2. Outline and Perspective.srt
• 3. Where to get the code
• 3. Where to get the code.srt
• 3.1 Colab Notebooks.html
• 3.2 Github Link.html
• 4. How to Succeed in this Course
• 4. How to Succeed in this Course.srt
• 10. GANs (Generative Adversarial Networks)
• 1. GAN Theory
• 1. GAN Theory.srt
• 2. GAN Colab Notebook.html
• 3. GAN Code
• 3. GAN Code.srt
• 11. Object Localization Project
• 1. Localization Introduction and Outline
• 1. Localization Introduction and Outline.srt
• 10. Localization Code (pt 4)
• 10. Localization Code (pt 4).srt
• 11. Localization Code Outline (pt 5)
• 11. Localization Code Outline (pt 5).srt
• 12. Localization Code (pt 5)
• 12. Localization Code (pt 5).srt
• 13. Localization Code Outline (pt 6)
• 13. Localization Code Outline (pt 6).srt
• 14. Localization Code (pt 6)
• 14. Localization Code (pt 6).srt
• 15. Localization Code Outline (pt 7)
• 15. Localization Code Outline (pt 7).srt
• 16. Localization Code (pt 7)
• 16. Localization Code (pt 7).srt
• 2. Localization Code Outline (pt 1)
• 2. Localization Code Outline (pt 1).srt
• 3. Object Localization Colab Notebooks.html
• 4. Localization Code (pt 1)
• 4. Localization Code (pt 1).srt
• 5. Localization Code Outline (pt 2)
• 5. Localization Code Outline (pt 2).srt
• 6. Localization Code (pt 2)
• 6. Localization Code (pt 2).srt
• 7. Localization Code Outline (pt 3)
• 7. Localization Code Outline (pt 3).srt
• 8. Localization Code (pt 3)
• 8. Localization Code (pt 3).srt
• 9. Localization Code Outline (pt 4)
• 9. Localization Code Outline (pt 4).srt
• 12. Keras and Tensorflow 2 Basics Review
• 1. (Review) Tensorflow Basics
• 1. (Review) Tensorflow Basics.srt
• 2. (Review) Tensorflow Neural Network in Code
• 2. (Review) Tensorflow Neural Network in Code.srt
• 3. (Review) Keras Discussion
• 3. (Review) Keras Discussion.srt
• 4. (Review) Keras Neural Network in Code
• 4. (Review) Keras Neural Network in Code.srt
• 5. (Review) Keras Functional API
• 5. (Review) Keras Functional API.srt
• 6. (Review) How to easily convert Keras into Tensorflow 2.0 code
• 6. (Review) How to easily convert Keras into Tensorflow 2.0 code.srt
• 13. Setting Up Your Environment (FAQ by Student Request)
• 1. Windows-Focused Environment Setup 2018
• 1. Windows-Focused Environment Setup 2018.srt
• 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
• 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt
• 14. Extra Help With Python Coding for Beginners (FAQ by Student Request)
• 1. How to Code by Yourself (part 1)
• 1. How to Code by Yourself (part 1).srt
• 2. How to Code by Yourself (part 2)
• 2. How to Code by Yourself (part 2).srt
• 3. Proof that using Jupyter Notebook is the same as not using it
• 3. Proof that using Jupyter Notebook is the same as not using it.srt
• 4. Python 2 vs Python 3
• 4. Python 2 vs Python 3.srt
• 15. Effective Learning Strategies for Machine Learning (FAQ by Student Request)
• 1. How to Succeed in this Course (Long Version)
• 1. How to Succeed in this Course (Long Version).srt
• 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced
• 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt
• 3. Machine Learning and AI Prerequisite Roadmap (pt 1)
• 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt
• 4. Machine Learning and AI Prerequisite Roadmap (pt 2)
• 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt
• 16. Appendix FAQ Finale
• 1. What is the Appendix (1).srt
• 1. What is the Appendix
• 1. What is the Appendix.srt
• 2. BONUS Where to get discount coupons and FREE deep learning material
• 2. BONUS Where to get discount coupons and FREE deep learning material.srt
• 2. Machine Learning Basics Review
• 1. What is Machine Learning
• 1. What is Machine Learning.srt
• 10. Saving and Loading a Model
• 10. Saving and Loading a Model.srt
• 11. Suggestion Box
• 11. Suggestion Box.srt
• 2. Code Preparation (Classification Theory)
• 2. Code Preparation (Classification Theory).srt
• 3. Beginner’s Code Preamble
• 3. Beginner’s Code Preamble.srt
• 3.1 Notebooks.html
• 4. Classification Notebook
• 4. Classification Notebook.srt
• 5. Code Preparation (Regression Theory)
• 5. Code Preparation (Regression Theory).srt
• 6. Regression Notebook
• 6. Regression Notebook.srt
• 7. The Neuron
• 7. The Neuron.srt
• 8. How does a model learn
• 8. How does a model learn.srt
• 9. Making Predictions
• 9. Making Predictions.srt
• 3. Artificial Neural Networks (ANN) Review
• 1. Artificial Neural Networks Section Introduction
• 1. Artificial Neural Networks Section Introduction.srt
• 2. Forward Propagation
• 2. Forward Propagation.srt
• 3. The Geometrical Picture
• 3. The Geometrical Picture.srt
• 4. Activation Functions
• 4. Activation Functions.srt
• 5. Multiclass Classification
• 5. Multiclass Classification.srt
• 6. How to Represent Images
• 6. How to Represent Images.srt
• 7. Code Preparation (ANN)
• 7. Code Preparation (ANN).srt
• 8. ANN for Image Classification
• 8. ANN for Image Classification.srt
• 9. ANN for Regression
• 9. ANN for Regression.srt
• 4. Convolutional Neural Networks (CNN) Review
• 1. What is Convolution (part 1)
• 1. What is Convolution (part 1).srt
• 10. Batch Normalization
• 10. Batch Normalization.srt
• 11. Improving CIFAR-10 Results
• 11. Improving CIFAR-10 Results.srt
• 2. What is Convolution (part 2)
• 2. What is Convolution (part 2).srt
• 3. What is Convolution (part 3)
• 3. What is Convolution (part 3).srt
• 4. Convolution on Color Images
• 4. Convolution on Color Images.srt
• 5. CNN Architecture
• 5. CNN Architecture.srt
• 6. CNN Code Preparation
• 6. CNN Code Preparation.srt
• 7. CNN for Fashion MNIST
• 7. CNN for Fashion MNIST.srt
• 8. CNN for CIFAR-10
• 8. CNN for CIFAR-10.srt
• 9. Data Augmentation
• 9. Data Augmentation.srt
• 5. VGG and Transfer Learning
• 1. VGG Section Intro
• 1. VGG Section Intro.srt
• 2. What’s so special about VGG
• 2. What’s so special about VGG.srt
• 3. Transfer Learning
• 3. Transfer Learning.srt
• 4. Relationship to Greedy Layer-Wise Pretraining
• 4. Relationship to Greedy Layer-Wise Pretraining.srt
• 5. Getting the data
• 5. Getting the data.srt
• 6. Code pt 1
• 6. Code pt 1.srt
• 7. Code pt 2
• 7. Code pt 2.srt
• 8. Code pt 3
• 8. Code pt 3.srt
• 9. VGG Section Summary
• 9. VGG Section Summary.srt
• 6. ResNet (and Inception)
• 1. ResNet Section Intro
• 1. ResNet Section Intro.srt
• 10. Building ResNet – Putting it all together
• 10. Building ResNet – Putting it all together.srt
• 11. Exercise Apply ResNet
• 11. Exercise Apply ResNet.srt
• 12. Applying ResNet
• 12. Applying ResNet.srt
• 13. 1×1 Convolutions
• 13. 1×1 Convolutions.srt
• 14. Optional Inception
• 14. Optional Inception.srt
• 15. Different sized images using the same network
• 15. Different sized images using the same network.srt
• 16. ResNet Section Summary
• 16. ResNet Section Summary.srt
• 2. ResNet Architecture
• 2. ResNet Architecture.srt
• 3. Building ResNet – Strategy
• 3. Building ResNet – Strategy.srt
• 4. Uh-oh! What Happens if the Implementation Changes
• 4. Uh-oh! What Happens if the Implementation Changes.srt
• 5. Building ResNet – Conv Block Details
• 5. Building ResNet – Conv Block Details.srt
• 6. Building ResNet – Conv Block Code
• 6. Building ResNet – Conv Block Code.srt
• 7. Building ResNet – Identity Block Details
• 7. Building ResNet – Identity Block Details.srt
• 8. Building ResNet – First Few Layers
• 8. Building ResNet – First Few Layers.srt
• 9. Building ResNet – First Few Layers (Code)
• 9. Building ResNet – First Few Layers (Code).srt
• 7. Object Detection (SSD RetinaNet)
• 1. SSD Section Intro
• 1. SSD Section Intro.srt
• 10. RetinaNet with Custom Dataset (pt 2)
• 10. RetinaNet with Custom Dataset (pt 2).srt
• 11. RetinaNet with Custom Dataset (pt 3)
• 11. RetinaNet with Custom Dataset (pt 3).srt
• 12. Optional Intersection over Union & Non-max Suppression
• 12. Optional Intersection over Union & Non-max Suppression.srt
• 13. SSD Section Summary
• 13. SSD Section Summary.srt
• 2. Object Localization
• 2. Object Localization.srt
• 3. What is Object Detection
• 3. What is Object Detection.srt
• 4. How would you find an object in an image
• 4. How would you find an object in an image.srt
• 5. The Problem of Scale
• 5. The Problem of Scale.srt
• 6. The Problem of Shape
• 6. The Problem of Shape.srt
• 7. 2020 Update – More Fun and Excitement
• 7. 2020 Update – More Fun and Excitement.srt
• 8. Using Pretrained RetinaNet
• 8. Using Pretrained RetinaNet.srt
• 8.1 Notebooks.html
• 9. RetinaNet with Custom Dataset (pt 1)
• 9. RetinaNet with Custom Dataset (pt 1).srt
• 8. Neural Style Transfer
• 1. Style Transfer Section Intro
• 1. Style Transfer Section Intro.srt
• 2. Style Transfer Theory
• 2. Style Transfer Theory.srt
• 3. Optimizing the Loss
• 3. Optimizing the Loss.srt
• 4. Code pt 1
• 4. Code pt 1.srt
• 5. Code pt 2
• 5. Code pt 2.srt
• 6. Code pt 3
• 6. Code pt 3.srt
• 7. Style Transfer Section Summary
• 7. Style Transfer Section Summary.srt
• 9. Class Activation Maps
• 1. Class Activation Maps (Theory)
• 1. Class Activation Maps (Theory).srt
• 2. Class Activation Maps (Code)
• 2. Class Activation Maps (Code).srt