Description
Machine Vision, GANs, and Deep Reinforcement Learning LiveLessons, 2nd Edition, is an intuitive introductory course for deep learning training, where three of the most popular deep learning topics are taught. Modern machine vision has surpassed human ability in image recognition, object recognition and image segmentation tasks by introducing automatic systems. By forming two deep learning networks against each other in a forger-detective relationship, adversarial generative networks allow the creation and forgery of real-world images with objects of the user’s choice.
What you will learn in the course Machine Vision, GANs, and Deep Reinforcement Learning LiveLessons, 2nd Edition:
- Understanding of high level theory and key language around machine learning, reinforcement learning and adversarial generative networks
- Building state-of-the-art models for image recognition, object recognition and image segmentation
- The architecture of GANs that have the ability to produce convincing images in the form of human imagination
- Building a deep RL assistant that can adapt to diverse environments, such as those provided by OpenAI Gym.
- Running automated experiments to optimize deep reinforcement learning models
- Understanding the current limitations of artificial intelligence and how to overcome them in the future
Course details
Publisher: InformIT
Instructors: Jon Krohn
English language
Education level: Intermediate
Number of courses: 45
Duration: 6 hours and 6 minutes
Course topics:
Lesson 1: Orientation
Topics
1.1 Running the Hands-On Code Examples in Jupyter Notebooks
1.2 Review of Prerequisite Deep Learning Theory
1.3 A Sneak Peak
Lesson 2: Convolutional Neural Networks for Machine Vision
Topics
2.1 Convolutional Layers
2.2 Convolutional Filter Hyperparameters
2.3 Activation Pooling and Flattening
2.4 Building A ConvNet in TensorFlow
2.5 ConvNet Model Architectures
2.6 Residual Networks
2.7 Image Segmentation
2.8 Object Detection
2.9 Transfer Learning
2.10 Capsule Networks
Lesson 3: Generative Adversarial Networks for Creativity
Topics
3.1 A Boozy All-Nighter
3.2 Latent Space: Arithmetic on Fake Human Faces
3.3 Style Transfer: Converting Photos into Monet (and Vice Versa)
3.4 Applications of GANs
3.5 Essential GAN Theory
3.6 The “Quick, Draw!” Dataset
3.7 The Discriminator Network
3.8 The Generator Network
3.9 Training the Adversarial Network
Lesson 4: Deep Reinforcement Learning
Topics
4.1 Three Categories of Machine Learning Problems
4.2 When Reinforcement Learning Becomes Deep
4.3 Applications to Video Games
4.4 Applications to Board Games
4.5 Real-World Applications
4.6 Reinforcement Learning Environments
4.7 Three Categories of Artificial Intelligence
Lesson 5: Deep Q-Learning and Beyond
Topics
5.1 The Cart-Pole Game
5.2 Essential Reinforcement Learning Theory
5.3 Deep Q-Learning Networks
5.4 Defining a DQN Agent
5.5 Interacting with an Environment
5.6 Hyperparameter Optimization with SLM Lab
5.7 Agents Beyond DQN
5.8 Datasets, Project Ideas, and Resources for Self-Study
5.9 Approaching Artificial General Intelligence
Summary
Course prerequisites:
The author’s Deep Learning with TensorFlow, Keras, and PyTorch LiveLessonsor familiarity with the topics covered in Chapters 5 through 9 of his book Deep Learning Illustratedare a prerequisite.
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
Subtitle: None
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
9.6
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