Brief Introduction
Explore Deep Learning with KerasDescription
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.
About the Author
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 to society with the ultimate goal of enhancing 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.
Requirements
- Requirements
- You should be familiar with Python and some general concepts of machine learning. No deep learning knowledge is required though.
Packt Publishing
3.4 Raiting
Python: A-Z Artificial Intelligence with Python: 5-in-1
- 3.9
-
![]()
![]()
![]()
![]()
![]()
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Are you a Python developer who is interested to build real-world Artificial Intelligence applications?...
Java Multithreading , Concurrency & Parallel Programming 2/e
- 4.3
-
![]()
![]()
![]()
![]()
![]()
Java MultiThreading, Concurrency, Parallel Algorithms, Asynchronous programming , Executors, CompletableFuture and more BEST in Class course for programmers to learn multitasking, MultiThreading and Parallel programming paradigm....
Learn Music Production at Home from Scratch: Stages II & III
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Getting ready to start working in a world-class project Hi! I am Sebastian and I want to welcome you to our course....
Functional Programming Principles in Scala
- 4.8
-
![]()
![]()
![]()
![]()
![]()
Functional programming is becoming increasingly widespread in industry. This trend is driven by the adoption of Scala as the main programming language for many applications....
