This textbook presents a concise, accessible, and engaging first introduction to deep learning, offering a wide range of connectionist models that represent the current state of the art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks, and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately. Topics and features include an introduction to the fundamentals of machine learning, as well as the mathematical and computational prerequisites for deep learning; a discussion of feed-forward neural networks, and an exploration of the modifications that can be applied to any neural network.