This is the code repository for Advanced Deep Learning with Keras [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- 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
To fully benefit from the coverage included in this course, you will need:
This course is geared toward software developers and machine learning enthusiasts who would like to improve their skills and expertise in machine learning and more specifically deep learning. You should be familiar with Python and some general concepts of machine learning. No deep learning knowledge is required though.
This course has the following software requirements:
Basic knowledge of Deep learning
Keras