This introduces the field of deep learning using the Python language (which is now a day very interesting language) and the most powerful library of Keras. Moreover, this book is written by Keras creator & also Google AI researcher, François Chollet. This book will be going to builds your understanding through very intuitive explanations and practical examples
Additionally, Purchase of this print book includes a free eBook in PDF format and also available in Kindle and in ePub formats from Manning Publications.
Officially Published - 28 October 2017
Total no of pages-384
About the Technology
We all know that Machine learning has made wonderful progress in the recent few years. From unusable speech and image recognition, human accuracy, machines that couldn't beat a serious Go player, defeating a world champion. Do you know! That behind this progress is deep learning. Deep learning is the technology behind photo tagging systems at Facebook and Google.so, whenever you upload any group pic it automatically tags your friends which are in the pic, self-driving cars, speech recognition systems on your smartphone, text classification, question answering, text-to-speech, and optical character recognition, etc. Also applicable to a wide range of artificial intelligence problems.
Deep Learning with Python is structured around a series of examples of practical codes which illustrate each new concept introduced and also demonstrate best practices. After ending the book, you will become a Keras expert and, you'll have the knowledge and hands-on skills and becomes capable to apply deep learning in your own projects.
What is inside the book (Deep Learning Course)?
• First principles of Deep learning (Deep Learning Introduction)
• You can Set-up your own deep-learning environment
• You can create Image-classification models and many other models also
• Learn about how deep learning is used in text and sequences
• Text and image generation Neural style transfer
• Examples like practical codes are also available (Deep Learning Frameworks)
• Introduction to Keras library in detailed
• In the end, you have a clear difference between Deep Learning and AI
• You will be going to explore challenging concepts and practice with different applications in computer vision, natural language processing, and generative models.
Author’s Name: Adam Gibson, Josh Patterson
Year of Publishing: August 2017
Total number of Pages in Book: 532
Category of the Book: Data Mining
About the Book:
Recently interest in machine learning has reached the highest point, lofty expectations often scuttle projects before they get very far. You may also think that How can machine learning especially deep neural networks can make a real difference in your organization? The hands-on guide (which is provided) not only provides the most practical information available on the subject, but also helps you to get started for building efficient deep learning networks.
Authors have provided the theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing workflows on production-class. From examples of real world, one can very smoothly learn deep learning algorithms and strategies for having training in deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.
Author: Douwe Osinga
Categories: Computers-Cybernetics: Artificial Intelligence
Publishing Year: 2018
Total no of Pages: 252
You should not have to be frightened with Deep learning. Recently, many analyses have done on this machine-learning method which required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter into this field. With the recipes in this cookbook, you'll be going to learn how to solve deep-learning problems for generating and classifying text, music, images and many more.
Description of the book:
Every chapter consists of several topics needed to complete a single project, such as music recommending system, training, and many more. Moreover, the author provides many techniques in every chapter to help you if you’re stuck. Examples are also written in Python code which is available on GitHub as a set of Python notebooks. In the end, you will get the following knowledge:
• Build an inverse image search service by reusing pre-trained networks
• Based on Wikipedia links build a movie recommender system
• Detect index song collections and music styles
• Visualizing the internal state of AI and learn how AIs see the world
• To serve real users you can create applications
• Compare how autoencoders, LSTMs and GANs, generate icons
• To calculate text similarity by Using word embedding
• For pieces of text build a model to suggest emoji