Progressive Deep Learning with Keras in Practice

Description

Keras is an (Open source Neural Network library written in Python) Deep Learning library for fast, efficient training of Deep Learning models. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time.

This comprehensive 3-in-1 course takes a step-by-step practical approach to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning. Initially, you’ll learn backpropagation, install and configure Keras and understand callbacks and for customizing the process. You’ll build, train, and run fully-connected, Convolutional and Recurrent Neural Networks. You’ll also solve Supervised and Unsupervised learning problems using images, text and time series. Moving further, you’ll use concepts, intuitive understating and applications of Autoencoders and Generative Adversarial Networks. Finally, you’ll build projects on Image Processing, NLP, and Reinforcement Learning and build cutting-edge Deep Learning models in a simple, easy to understand way.

Towards the end of this course, you'll get to grips with the basics of Keras to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Deep Learning with Keras, covers implementing deep learning neural networks with Python. Keras is a high-level neural network library written in Python and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner.

The second course, Advanced Deep Learning with Keras, covers Deep learning with one of it's most popular frameworks: Keras. 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.

The third course, Keras Deep Learning Projects, covers Projects on Image Processing, NLP, and Reinforcement Learning. This course will show you how to leverage the power of Keras to build and train high performance, high accuracy deep learning models, by implementing practical projects in real-world domains. Spanning over three hours, this course will help you master even the most advanced concepts in deep learning and how to implement them with Keras. You will train CNNs, RNNs, LSTMs, Autoencoders and Generative Adversarial Networks using real-world training datasets. These datasets will be from domains such as Image Processing and Computer Vision, Natural Language Processing, Reinforcement Learning and more. By the end of this highly practical course, you will be well-versed with deep learning and its implementation with Keras. By the end of this course, you will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.

Towards the end of this course, you'll get to grips with the basics of Keras to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning.

About the Authors

• Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and has managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields ranging from publishing (Elsevier) to consumer internet (Ask and Tiscali) and high-tech R&D (Microsoft and Google).

  
  

• Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.

  
  

• Philippe Remy is a research engineer and entrepreneur working on deep learning and living in Tokyo, Japan. As a research engineer, Philippe reads scientific papers and implements artificial intelligence algorithms related to handwriting character recognition, time series analysis, and natural language processing. As an entrepreneur, his vision is to bring a meaningful and transformative impact on society with the ultimate goal of enhancing the overall quality of life and pushing the limits of what is considered possible today. Philippe contributes to different open source projects related to deep learning and fintech (github/philipperemy). You can visit Philippe Remy’s blog on philipperemy.

  
  

• Tsvetoslav Tsekov has worked for 5 years on various software development projects - desktop applications, backend applications, WinCE embedded software, RESTful APIs. He then became exceedingly interested in Artificial Intelligence and particularly Deep Learning. After receiving his Deep Learning Nanodegree, he has worked on numerous projects - Image Classification, Sports Results Prediction, Fraud Detection, and Machine Translation. He is also very interested in General AI research and is always trying to stay up to date with the cutting-edge developments in the field.

What You Will Learn!

• Understand the main concepts of machine learning and deep learning
• Build, train, and run fully-connected, convolutional and recurrent neural networks
• Optimize Deep Neural Networks through efficient hyper-parameter searches
• Work with any kind of data involving images, text, time series, sound, and videos
• Discover some advanced neural architectures such as generative adversarial networks
• Find out about a wide range of subjects from recommender systems to transfer learning
• Explore the Concepts of Convolutional Neural Networks and Recurrent Neural Networks
• Use Concepts, intuitive understating and applications of Autoencoders and Generative Adversarial Networks
• Build Autoencoders and Generative Adversarial Networks

Who Should Attend!

• This course is perfect for:
• Software developers, Data Scientists with experience in Machine Learning or an AI Programmer with some exposure to Neural Networks: would like to improve their skills and expertise in Machine Learning and more specifically Deep Learning.

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