deep reinforcement learning tutorial

Turn from 70271574AI (AI) is the future, is science fiction, is part of our daily life. All the assertions are correct, just to see what you are talking about AI in the end.For example, when Google DeepMind developed the Alphago program to defeat the Korean professional Weiqi master Lee Se-dol, the media in the description of the victory of DeepMind used AI, machine learning, deep

7 mins version:dqn for Flappy Bird Overview This project follows the description of the "Deep Q Learning algorithm described" Playing Atari with deep reinforcement L Earning [2] and shows that this learning algorithm can is further generalized to the notorious Flappy Bird. i

Deep Reinforcement Learning Guide: http://mp.weixin.qq.com/s?__biz=MzI1NTE4NTUwOQ==mid=2650324914idx=1sn= 0baaf404b3d8132243d08b55310de210scene=2srcid=062732p5u33rrnikuedslvxnfrom=timeline Isappinstalled=0#wechat_redirect Detailed in-depth study, build DQN Guide (based on neon framework):https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==mid=2650716425idx=1sn= bf52

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a

Chinese books. But "machine learning", "statistical learning method" is still worth a look. Foreign language Recommendation "Pattern Recognition and machine learning" and "Machine learning:a Probabilistic Perspective", the latter containing the chapters of the Deep Neural network。 3.

few years there have been many algorithms for machine learning, including decision tree learning, inductive logic programming, clustering Analysis (clustering), reinforcement learning, Bayesian networks, etc. As we all know, no one really achieve the ultimate goal of "strong artificial intelligence", using the early m

random game without any supervision or use of artificial data. Second, it uses only the black and white on the chessboard as the input feature (the previous Alphago has many features that are artificially constructed). Third, use only one neural network, not separate strategic networks and value networks. Finally, only a simplified version tree search based on a single neural network is used to evaluate the drop probability and the effect of drop on the situation, and the Monte Carlo method is

most important thing to know about OpenAI is to understand the frontiers of AI research.What is the research direction of Ai's frontier?OpenAI raised three points:-Training Generative Models-Algorithms for inferring algorithms from data-New approaches to reinforcement learningSo what do these three categories represent, respectively?Deep generative ModelsThe first type is oriented to the generation model,

a larger new dataset that can be adjusted. Image datasets are larger than 200x10. A complex network structure requires more training sets. Be careful about fitting. References 1. cs231n convolutional neural Networks for Visual recognition 2. TensorFlow convolutional Neural Networks 3. How to Retrain Inception's Final Layer for New Categories 4. K-nn Classifier for image classification 5. Image augmentation for Deep

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 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

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.

learning works. "Back to TopFurther ReadingHinton, G.E, and Salakhutdinov, R.R.Reducing the dimensionality of data with neural networks, Science (2006), Vol 313, p 504.Schmidhuber, J.Deep learning in Neural networks:an overview, Neural Networks , Volume, pp85–117 (ArXiv preprint:http: Arxiv.org/pdf/1404.7828.pdf)Mnih, V., et alHuman-level control through deep