Artificial Intelligence, A Modern Approach - 2nd Edition 真正清晰英文版pdf第二版，希望对您有帮助

Artificial Intelligence for Humans, Volume 1_Fundamental Algorithms-Heaton Research(2013) Artificial Intelligence for Humans, Volume 2_Nature-Inspired Algorithms-Heaton Research(2014) Artificial Intelligence for Humans, Volume 3_Deep Learning and Neural Networks-Heaton Research(2015)

这是第二部分，跟第一部分是一本书，想学Prolog的就看看吧

我正在看这本书，感觉不错，与大家分享一下，想学Prolog的可以看一下，分两部分（因为我一次只能上传小于20M的文件）

GTA V的无人驾驶汽车 概述 该项目的目的是使用虚拟模拟器（尤其是GTA V）创建自动驾驶汽车。

Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you’ll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that’s based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it Table of Contents Chapter 1. Introduction to Artificial Intelligence Chapter 2. Classification and Regression Using Supervised Learning Chapter 3. Predictive Analytics with Ensemble Learning Chapter 4. Detecting Patterns with Unsupervised Learning Chapter 5. Building Recommender Systems Chapter 6. Logic Programming Chapter 7. Heuri

声明：本pdf来自网络，仅供学习用，不可用于商业用途。 深度学习，大牛作品，希望对大家有用

This new edition provides a comprehensive, colorful, up to date, and accessible presentation of AI without sacrificing theoretical foundations. It includes numerous examples, applications, full color images, and human interest boxes to enhance student interest. New chapters on robotics and machine learning are now included. Advanced topics cover neural nets, genetic algorithms, natural language processing, planning, and complex board games. A companion DVD is provided with resources, applications, and figures from the book. Numerous instructors’ resources are available upon adoption. FEATURES: Includes new chapters on robotics and machine learning and new sections on speech understanding and metaphor in NLP Provides a comprehensive, colorful, up to date, and accessible presentation of AI without sacrificing theoretical foundations Uses numerous examples, applications, full color images, and human interest boxes to enhance student interest Introduces important AI concepts e.g., robotics, use in video games, neural nets, machine learning, and more thorough practical applications Features over 300 figures and color images with worked problems detailing AI methods and solutions to selected exercises Includes DVD with resources, simulations, and figures from the book Provides numerous instructors’ resources, including: solutions to exercises, Microsoft PP slides, etc. Table of Contents Part 1: Introduction Chapter 1 Overview of Artificial Intelligence PART II: FUNDAMENTALS Chapter 2 Uninformed Search Chapter 3 Informed Search Chapter 4 Search Using Games Chapter 5 Logic in Artificial Intelligence Chapter 6 Knowledge Representation Chapter 7 Production Systems PART III: KNOWLEDGE-BASED SYSTEMS Chapter 8 Uncertainty in AI Chapter 9 Expert Systems Chapter 10 Machine Learning: Part I Inductive Learning with Decision Trees Chapter 11 Machine Learning : Part II Neural Networks Chapter 12 Search Inspired by Mother Nature PART IV: ADVANCED TOPICS Chapter 13 Natural Language

瓶颈变压器-火炬 在Pytorch中，在性能-计算权衡方面优于EfficientNet和DeiT的卷积（SotA）视觉识别模型的卷积+注意实现 安装 $ pip install bottleneck-transformer-pytorch 用法 import torch from torch import nn from bottleneck_transformer_pytorch import BottleStack layer = BottleStack ( dim = 256 , # channels in fmap_size = 64 , # feature map size dim_out = 2048 , # channels out proj_factor = 4 , #

这是系列文章中的第一篇，我们将解释如何使用转换器创建AI聊天机器人。