Company: Packt Publishing Author: Philippe Remy Full Title: Advanced Deep Learning With Keras Year: 2017 Language: English Genre: Educational: Deep Learning Skill Level: - Price: €124.99 - Files: MP4 (+ Code Files) Time: 05:11:16 Video: AVC, 1920 x 1080 (1.778) at 29.970 fps, 200 kbps Audio: AAC at 132 Kbps, 2 channels, 48.0 KHz Explore Deep Learning with Keras. Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning. Then, we introduce neural networks and the optimization techniques to train them. We’ll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, we present two types of neural architecture: convolutional and recurrent neural networks First, we present a well-known use case of deep learning: recommender systems, where we try to predict the "rating" or "preference" that a user would give to an item. Then, we introduce an interesting subject called style transfer. Deep learning has this ability to transform images based on a set of inputs, so we’ll morph an image with a style image to combine them into a very realistic result. In the third section, we present techniques to train on very small datasets. This comprises transfer learning, data augmentation, and hyperparameter search, to avoid overfitting and to preserve the generalization property of the network. Finally, we complete this course by what Yann LeCun, Director at Facebook, considered as the biggest breakthrough in Machine Learning of the last decade: Generative Adversarial Networks. These networks are amazingly good at capturing the underlying distribution of a set of images to generate new images. Style and Approach Expect a smooth combination of theory and practice. Code examples illustrate all the important concepts in the course, and you can implement them yourself, guided by the course Learn: ✓ Understand the main concepts of machine learning and deep learning ✓ Install and use Python and Keras to build deep learning models ✓ Build, train, and run fully-connected, convolutional and recurrent neural networks ✓ Optimize deep neural networks through efficient hyper parameter searches ✓ See many real-world applications to identify which tasks can be leveraged with deep learning ✓ Work with any kind of data involving images, text, time series, sound and videos ✓ Use GPUs to leverage the training experience. ✓ Discover some advanced neural architectures such as generative adversarial networks ✓ Find out about a wide range of subjects from recommender systems to transfer learning Features: ✓ Recognize whose practical applications can benefit from Deep Learning ✓ Get equipped with the knowledge of building, training and using convolutional neural network ✓ Solve supervised and unsupervised learning problems using images, text and time series Lessons: 1. Introduction to Deep Learning 01. The Course Overview 02. What is Deep Learning? 03. Machine Learning Concepts 04. Foundations of Neural Networks 05. Optimization 2. Get Started with Keras 06. Configuration of Keras 07. Presentation of Keras and Its API 08. Design and Train Deep Neural Networks 09. Regularization in Deep Learning 3. Convolutional and Recurrent Neural Networks 10. Introduction to Computer Vision 11. Convolutional Networks 12. CNN Architectures 13. Image Classification Example 14. Image Segmentation Example 15. Introduction to Recurrent Networks 16. Recurrent Neural Networks 17. “One to Many” Architecture 18. “Many to One” Architecture 19. “Many to Many” Architecture 20. Embedding Layers 4. Recommender Systems 21. What are Recommender Systems 22. Content/Item Based Filtering 23. Collaborative Filtering 24. Hybrid System 5. Neural Style Transfer 25. Introduction to Neural Style Transfer 26. Single Style Transfer 27. Advanced Techniques 28. Style Transfer Explained 6. Advanced Techniques 29. Data Augmentation 30. Transfer Learning 31. Hyper Parameter Search 32. Natural Language Processing 7. Generative Adversarial Networks 33. An Introduction to Generative Adversarial Networks (GAN) 34. Run Our First GAN 35. Deep Convolutional Generative Adversarial Networks (DCGAN) 36. Techniques to Improve GANs Our members see more. Join us! ------------- Our members see more. Join us!