搜索资源列表
machine-learning-ex1
- Standford machine-learning 网课第一周编程作业，线性回归的算法实现。(Standford machine-learning method first week of programming operations, the realization of the linear regression algorithm.)
Deep Learning
- deep learning 书籍，此书包括机器学习基础，深度前馈网络，卷积网络，蒙特卡洛方法等的详细介绍(Deep learning books, which include a detailed introduction to machine learning, deep feedforward networks, convolution networks, Monte Carlo methods, and so on)
Deep Learning
- 深度学习（deep learning）英文专著，适合深度学习的理论基础学习。(A monographs which suitable for the theoretical basis of deep learning.)
Learning to rank - Tie yan liu.pdf
- Learning to rank Tie yan liu
Chapter 6 (Temporal Difference Learning)
- 讲解强化学习中的Q学习和sarsa学习，并通过具体实例讲解怎样运用这些学习方法(Explain Q learning and sarsa learning in intensive learning, and explain how to use these learning methods through concrete examples)
learning opencv(中文版)
- learning opencv,中文版的learning opencv书籍(learning opencv Chinese version, learning opencv book)
Pattern Recognition and Machine Learning
- for machine learning
Machine Learning With Go
- learning machine learning with golang.
作业数据---machine-learning-master
- machine learning作业数据(machine learning homework)
Java-Deep-Learning-Essentials-master
- java 深度学习源码，有需要的可以下载(Java-Deep-Learning-Essentials-master)
Q-Learning-master
- Successfully implemented Q-Learning for a simple robot navigation problem of a robot moving on a 5 x 5 grid with one arbitrary goal (reward of +10) and three arbitrary obstacles (reward of -10)
reinforcement-learning-robot-in-maze-master
- Reinforcement learning, a Q learning algorithm, implementation on a robot that tryies to solve randomly created maze and reach the goal. Note that you can run .m files both on Matlab and Octave.
Q-Learning-Algorithm-Implementation
- This Q-Learning code for MATLAB has been written by Ioannis Makris and Andrew Chalikiopoulos. It trains an agent to find the shortest way through a 25x25 maze. Following convergence of the algorithm, MATLAB will print out the shortest path to the goa
Machine learning A Probabilistic Perspective
- 经典教学书籍<Machine learning A Probabilistic Perspective>(book <Machine learning A Probabilistic Perspective>)
Machine Learning with TensorFlow
- 机器学习 人工智能 决策树 随机森林 机器学习 人工智能 决策树 随机森林(machine learning tensor flow intelligent artifish)
Deep Learning Based Communication Over the Air
- 通信系统的端到端学习是a 引人入胜的新颖概念迄今为止仅被验证 模拟基于块的传输。它允许学习 发射机和接收机实现为深度神经网络 （NN），它们针对任意可区分的端到端进行了优化 performancemetric，例如块错误率（BLER）。在本文中，我们 证明无线传输是可能的：我们建造， 训练，并运行完整的通讯系统 的神经网络使用非同步的现成软件定义无线电 和开源深度学习软件库。(End-to-end learning of communications systems is a
stanford-deep-learning-matlab-code
- Stanford 大学的深度学习源代码，可用于模式识别和预测，比较稳定。(Stanford University's deep learning source code can be used for pattern recognition and prediction, and is relatively stable.)
TensorFlow Machine Learning Cookbook
- 机器学习入门平台，谷歌公司的机器学习平台说明，对机器学习初学者比较实用(Machine learning platform, Google Corporation's machine learning platform descr iption, for machine learning beginners more practical.)
Deep Learning with Python
- 本书由Keras之父、现任Google人工智能研究员的弗朗索瓦?肖莱（Fran?ois Chollet）执笔，详尽介绍了用Python和Keras进行深度学习的探索实践，涉及计算机视觉、自然语言处理、生成式模型等应用。书中包含30多个代码示例，步骤讲解详细透彻。由于本书立足于人工智能的可达性和大众化，读者无须具备机器学习相关背景知识即可展开阅读。在学习完本书后，读者将具备搭建自己的深度学习环境、建立图像识别模型、生成图像和文字等能力。(Deep Learning with Python intr
ICEM learning geometry files
- 简单的ICEM CFD算例，入门者必学，配套书籍为ANSYS ICEM CFD 基础教程与实例详解(simple ICEM CFD learning examples)