PAC | Advanced Deep Learning With Keras (2017 EN)

Discussion in 'Artificial Intelligence' started by Kanka, Jul 22, 2019.

  1. Kanka

    Kanka Well-Known Member Loyal User

    Messages:
    13,502
    Likes Received:
    359
    Trophy Points:
    83
    [​IMG]

    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


    -------------