《Artificial Intelligence with Python》这本书适合哪些人学?它讲了哪些关键技术又怎么上手实践?
创作声明:本文部分内容由AI辅助生成(AIGC),仅供参考
Python内容推荐
Artificial Intelligence with Python + 源碼
Packtpub 所出的以python来实作各种 AI算法,亚马逊评价五颗星。
Artificial Intelligence with Python
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!
Artificial Intelligence with Python (including Code)
[英文原版 附随书代码]You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue!
Artificial Intelligence with Python(2017年出版)
Artificial Intelligence with Python(2017年1月最新出版) 重点:文件包括书中涉及到的16章所有的源代码
Packt Artificial Intelligence with Python-英文原版
Artificial Intelligence with Python – Deep Neural Networks February 1, 2018 Video Artificial Intelligence with Python – Deep Neural Networks Artificial Intelligence with Python – Deep Neural Networks English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 19m | 367 MB Learn different Artificial Intelligence learning techniques with neural networks The course is an introduction to the basics of deep learning methods. We will start with object detection and tracking, in which we will track faces, objects and eyes. We will then build a neural network and an OCR. We will then learn how to build learning agents that can learn from interacting with the environment. We will use Deep Learning with Convolutional Neural Networks, and use TensorFlow to build neural networks. We will then build an image classifier using convolutional neural networks. The video course is structured in such a way that the explanation of a concept is followed by a relevant example. Also, algorithms are explained and their respective code and training dataset is provided. What You Will Learn Build applications based on deep learning algorithms Detect and track objects using different algorithms Learn how reinforcement learning works
Artificial.Intelligence.with.Python
Packt.Artificial.Intelligence.with.Python
Artificial-Intelligence-with-Python,英文原版,很适合python新手提高代码能力!
Artificial-Intelligence-with-Python, 英文原版,非常适合python新手提高代码能力,没事时候多写写代码,多看点书,长长姿势,以后总会用上的,愿爱好python的你和我共同进步提高!
Artificial Intelligence With Python[January 2017]
Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively across many fields such as image recognition, robotics, search engines, and self-driving cars. In this book, we will explore various real-world scenarios. We will understand what algorithms to use in a given context and write functional code using this exciting book. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This book is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques. You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue! What this book covers Chapter 1, Introduction to Artificial Intelligence, teaches you various introductory concepts in artificial intelligence. It talks about applications, branches, and modeling of Artificial Intelligence. It walks the reader through the installation of necessary Python packages. Chapter 2, Classification and Regression Using Supervised Learning, covers various supervised learning techniques for classification and regression. You will learn how to analyze income data and predict housing prices. Chapter 3, Predictive Analytics with Ensemble Learning, explains predictive modeling techniques using Ensemble Learning, particularly focused on Random Forests. We will learn how to apply these techniques to predict traffic on the roads near sports stadiums. Chapter 4, Detecting Patterns with Unsupervised Learning, covers unsupervised learning algorithms including K-means and Mean Shift Clustering. We will learn how to apply these algorithms to stock market data and customer segmentation. Chapter 5, Building Recommender Systems, illustrates algorithms used to build recommendation engines. You will learn how to apply these algorithms to collaborative filtering and movie recommendations. Chapter 6, Logic Programming, covers the building blocks of logic programming. We will see various applications, including expression matching, parsing family trees, and solving puzzles. Chapter 7, Heuristic Search Techniques, shows heuristic search techniques that are used to search the solution space. We will learn about various applications such as simulated annealing, region coloring, and maze solving. Chapter 8, Genetic Algorithms, covers evolutionary algorithms and genetic programming. We will learn about various concepts such as crossover, mutation, and fitness functions. We will then use these concepts to solve the symbol regression problem and build an intelligent robot controller. Chapter 9, Building Games with Artificial Intelligence, teaches you how to build games with artificial intelligence. We will learn how to build various games including Tic Tac Toe, Connect Four, and Hexapawn. Chapter 10, Natural Language Processing, covers techniques used to analyze text data including tokenization, stemming, bag of words, and so on. We will learn how to use these techniques to do sentiment analysis and topic modeling. Chapter 11, Probabilistic Reasoning for Sequential Data, shows you techniques used to analyze time series and sequential data including Hidden Markov models and Conditional Random Fields. We will learn how to apply these techniques to text sequence analysis and stock market predictions. Chapter 12, Building A Speech Recognizer, demonstrates algorithms used to analyze speech data. We will learn how to build speech recognition systems. Chapter 13, Object Detection and Tracking, It covers algorithms related to object detection and tracking in live video. We will learn about various techniques including optical flow, face tracking, and eye tracking. Chapter 14, Artificial Neural Networks, covers algorithms used to build neural networks. We will learn how to build an Optical Character Recognition system using neural networks. Chapter 15, Reinforcement Learning, teaches the techniques used to build reinforcement learning systems. We will learn how to build learning agents that can learn from interacting with the environment. Chapter 16, Deep Learning with Convolutional Neural Networks, covers algorithms used to build deep learning systems using Convolutional Neural Networks. We will learn how to use TensorFlow to build neural networks. We will then use it to build an image classifier using convolutional neural networks.
ARTIFICIAL_INTELLIGENCE_WITH_PYTHON【百度云】
Artificial Intelligence with Python 作者: Prateek Joshi 出版社: Packt Publishing - ebooks Account 出版年: 2017-5-4 页数: 521 定价: USD 49.99 装帧: Paperback ISBN: 9781786464392
Artificial Intelligence with Python [EPUB]
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
Artificial Intelligence with Python 英文原版PDF
Artificial Intelligence with Python 英文原版PDF 文字清晰 资源网上转载 侵权请联系删除
Artificial Intelligence with Python(pdf+epub+mobi+code_files).zip
Artificial Intelligence with Python Artificial Intelligence with Python
Artificial-Intelligence-with-Python.pdf
Artificial-Intelligence-with-Python
Python for Programmers: with Big Data and Artificial Intelligence Case Studies
The professional programmer’s Deitel guide to Pythonwith introductory artificial intelligence case studies Written for programmers with a background in another high-level language, this book uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1—5 and a few key parts of Chapters 6—7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11—16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter for sentiment analysis, cognitive computing with IBM Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft Azure, OpenMapQuest, PubNub and more.
Prateek_Joshi_Artificial_Intelligence_with_Python.epub
“We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This book is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code.”
Prateek Joshi-Artificial Intelligence with Python
Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively across many fields such as image recognition, robotics, search engines, and self-driving cars. In this book, we will explore various real-world scenarios. We will understand what algorithms to use in a given context and write functional code using this exciting book. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This book is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques. You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue! What this book covers Chapter 1, Introduction to Artificial Intelligence, teaches you various introductory concepts in artificial intelligence. It talks about applications, branches, and modeling of Artificial Intelligence. It walks the reader through the installation of necessary Python packages. Chapter 2, Classification and Regression Using Supervised Learning, covers various supervised learning techniques for classification and regression. You will learn how to analyze income data and predict housing prices. Chapter 3, Predictive Analytics with Ensemble Learning, explains predictive modeling techniques using Ensemble Learning, particularly focused on Random Forests. We will learn how to apply these techniques to predict traffic on the roads near sports stadiums. Chapter 4, Detecting Patterns with Unsupervised Learning, covers unsupervised learning algorithms including K-means and Mean Shift Clustering. We will learn how to apply these algorithms to stock market data and customer segmentation.
Artificial Intelligence with Python-(2017)
Prateek Joshi Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you First published: January 2017 Production reference: 1230117 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78646-439-2 This book is focused on artificial intelligence in Python as opposed to the Python itself. We have used Python 3 to build various applications. We focus on how to utilize various Python libraries in the best possible way to build real world applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.
Packt.Python.Artificial.Intelligence.Projects.for.Beginners.2018
Packt.Python.Artificial.Intelligence.Projects.for.Beginners.2018
python3-311-Artificial-Intelligence-笔记
python3_311-Artificial-Intelligence-笔记
Artificial-Intelligence-with-Python-Cookbook:Packt出版的《人工智能与Python食谱》
人工智能与Python Cookbook 这是Packt出版的《 的代码库。 使用TensorFlow和PyTorch的下一代深度学习和神经网络的实用食谱 这本书是关于什么的? 借助人工智能(AI)系统,我们可以开发目标驱动的代理以自动解决问题。 这涉及对可用数据进行预测和分类,并培训代理以成功执行任务。 本书将帮助您使用实用食谱解决复杂的AI问题。 本书涵盖以下激动人心的功能: 实施数据预处理步骤并优化模型超参数 使用分布式和并行计算技术处理大量数据 使用InfoGAN掌握图像的代表性学习 使用贝叶斯网络深入研究概率模型 使用对抗神经网络创建自己的艺术品 如果您觉得这本书适合您,请立即获取! 说明和导航 所有代码都组织在文件夹中。 例如,Chapter02。 该代码将如下所示: from sklearn.datasets import fetch_openml data
最新推荐




![Artificial Intelligence with Python [EPUB]](https://img-home.csdnimg.cn/images/20210720083646.png)