The book offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

With the help of this 3-in-1 guide, you will be given carefully sequenced Python Programming lessons that’ll maximize your understanding, and equip you with all the skills for real-life application

A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning.

This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python.

Feed forward neural nets; tensorflow; convolutional neural networks; word embeddings and recurrent NNs; sequence to sequence learning; deep reinforcement learning; unsupervised neural network medels.

If you’ve picked up this book, you’re probably aware of the exưaordinary progress that deep leam ing has represented for the íìeld of artificial intelligence in the recent past. In a mere fĩve years, we’ve gone from near-unusable image recognition and speech transcription, to superhum an períorm ance on these tasks.