You to participate in the revolution that brings artificial intelligence back to the heart of our society, thanks to data scientists.
Data science consists in translating problems of any other nature into quantitative modeling problems, solved by processing algorithms.
This book, designed for anyone wishing to learn Deep Learning. This book presents the main techniques: deep neural networks, able to model all kinds of data, convolution networks, able to classify images, segment them and discover the objects or people who are there, recurring networks, it contains sample code so that the reader can easily test and run the programs.
These are some of the topics covered in this book:
fundamentals of deep learningfundamentals of probabilityfundamentals of statisticsfundamentals of linear algebraintroduction to machine learning and deep learningfundamentals of machine learningfundamentals of neural networks and deep learningdeep learning parameters and hyper-parametersdeep neural networks layersdeep learning activation functionsconvolutional neural networkDeep learning in practice (in jupyter notebooks)python data structuresbest practices in python and zen of pythoninstalling python