Combining the theory with practice, Big Data and Deep Learning is focused on the applications of Deep Learning and Big Data. The book, originally titled, Big Data, Machine Learning et apprentissage profonde, first written and published in French in April 2019, is based on graduate courses taught by Dr Tuffery at ENSAI, the top graduate school in France for statistics and data science, the Institut des Actuaires (the Institute of Actuaries) in Paris, and the University of Rennes 1. Thoroughly revised for the English edition and illustrated by numerous, up-to-date examples throughout, the book is focused on the key topics in data science today; the tools and optimization of processing in the context of Big Data, deep learning techniques, and neural networks and their applications, both to natural language processing and image recognition. The book complements the theoretical understanding it provides by giving practical instructions through various software tools and explores deep learning methods using three of the major deep learning libraries: MXNet, PyTorch, and Keras-TensorFlow. This reference is aimed at graduate students in data science, researchers and data scientists with an interest in Big Data, deep learning and artificial intelligence.