deep reinforcement learning book

Course Description: This is an introductory course on deep learning, and deep learning is mainly used for machine translation, image recognition, games, image generation and more. The course also has two very interesting practical projects: (1) Generate music based on RNN (2) Basic X-ray detection, GitHub address: Http

the average person, this is enough. How to buy this book. In fact, the entire content of the book has been "online", the full text of this document, download at will. The title is "Deep Learning" (Deep Learning). On the network,

. After all, although set theory is the foundation of the entire mathematics, logical reasoning can be considered as the foundation of mathematics. First, streamline the logic and use logical symbols later to make other theories more concise.Finally, it is very effective to check whether your understanding of the knowledge is in place: General difficult questions. If you do, you will basically understand this knowledge point, if you don't know the answer, but you can understand it, it means you

Learning software Development should read the bookSource: The log of Li Yue Jia 1. C Language Promotion"C and Pointers"C Defects and pitfalls"C Expert Programming"This three book is a junior C programmer must read three books, but also fresh new employees have to study the textbook, very suitable for the newly graduated college students study2. C + + language promotion"Effective C + +: 55 specific practices

The theme report of "Transfer model of deep learning" shorthand and commentary (iv) Bai Chu of the Red bean Family concern 2017.11.04 22:33* 3275 reading 141 comments 0 like 0 The author presses: machine learning is moving towards a new era of interpretive models based on "semantics". Migration learning is likely to ta

Deep learning--the artificial neural network and the upsurge of researchHu XiaolinThe artificial neural network originates from the last century 40 's, to today already 70 years old. Like a person's life, has experienced the rise and fall, has had the splendor, has had the dim, has had the noisy, has been deserted. Generally speaking, the past 20 years of artificial neural network research tepid, until the

memory overhead.4. One trend I have seen is that architecture is rapidly becoming larger and more complex. We are moving towards building large neural network systems, exchanging input and output of neural components, pre-trained network parts on different datasets, adding new modules, fine-tuning everything, and so on. For example, the Convolutional network was once one of the largest/deepest neural network architectures, but today it is abstracted into a small part of most new architectures.

deep Learning network uses a sophisticated algorithm, with millions of simulated neurons, with billions of connections, but their training methods are the same. Rosenblatt predicted that Perceptrons would soon being capable of feats like greeting people by name, and he idea became a l Inchpin of the nascent field of artificial intelligence. Work focused in making perceptrons with more complex networks, arr