Artificial Intelligence with Python

Author: Prateek Joshi

Publisher: Packt Publishing Ltd

ISBN: 1786469677

Category: Computers

Page: 446

View: 1241

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Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. 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 In Detail 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! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
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Python Artificial Intelligence Projects for Beginners

Get up and running with Artificial Intelligence using 8 smart and exciting AI applications

Author: Dr. Joshua Eckroth

Publisher: Packt Publishing Ltd

ISBN: 1789538246

Category: Computers

Page: 162

View: 3998

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Build smart applications by implementing real-world artificial intelligence projects Key Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code
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Hands-On Reinforcement Learning with Python

Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Author: Sudharsan Ravichandiran

Publisher: Packt Publishing Ltd

ISBN: 178883691X

Category: Computers

Page: 318

View: 2520

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A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art architectures along with math Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. What you will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and Tensorflow Understand the Markov Decision Process, Bellman’s optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agents using the DRQN algorithm to play the Doom game Teach agents to play the Lunar Lander game using DDPG Train an agent to win a car racing game using dueling DQN Who this book is for If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
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Python: Beginner's Guide to Artificial Intelligence

Build applications to intelligently interact with the world around you using Python

Author: Denis Rothman,Matthew Lamons,Rahul Kumar,Abhishek Nagaraja,Amir Ziai,Ankit Dixit

Publisher: Packt Publishing Ltd

ISBN: 1789959195

Category: Computers

Page: 676

View: 3802

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Develop real-world applications powered by the latest advances in intelligent systems Key Features Gain real-world contextualization using deep learning problems concerning research and application Get to know the best practices to improve and optimize your machine learning systems and algorithms Design and implement machine intelligence using real-world AI-based examples Book Description This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries. Throughout the Learning Path, you’ll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way. By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects. This Learning Path includes content from the following Packt products: Artificial Intelligence By Example by Denis Rothman Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Dixit What you will learn Use adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Understand future AI solutions and adapt quickly to them Master deep neural network implementation using TensorFlow Predict continuous target outcomes using regression analysis Dive deep into textual and social media data using sentiment analysis Who this book is for This Learning Path is for anyone who wants to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. You will learn to extend your machine learning and deep learning knowledge by creating practical AI smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this Learning Path.
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Machine Learning Python

2 Manuscripts - Artificial Intelligence Python and Reinforcement Learning with Python

Author: Anthony Williams

Publisher: Createspace Independent Publishing Platform

ISBN: 9781977829696

Category:

Page: 186

View: 7432

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Machine Learning Python - 2 BOOK BUNDLE!! Book 1: Artificial Intelligence with Python It is more than apparent that artificial intelligence techniques and practices will navigate the changes in the near future and simply shape the world. It is fair to say that AP is leading approach when it comes to the various scientific fields as well as various industries and today, it is almost impossible the world without advancements in the artificial intelligence field. Experts and scientists both agree that artificial intelligence is the field which will most certainly shape our economic future, automotive industry, health care, cybersecurity as well as cybercrime. Over the coming decades, AI will greatly impact every aspect of our lives including our work, careers, education, care for elderly and much more. Eventually, it will alter the world completely, as machines will pursue complex goals independently of their creators. AI tools have become mainstream tools when it comes to the various industries and science fields since these tools greatly reduce costs, increase profits and even save lives. If you understand the basic concept behind different AI techniques and approaches, you will be able to greatly benefit from it in various aspects. In order to maximize the benefits of AI advancements, you have to be ready to embark on different challenges. However, with this book, you will be able to overcome challenges and the reward is a success. What you will learn in this book: Different artificial intelligence approaches and goals How to define AI system Basic AI techniques Reinforcement learning How to build a recommender system Genetic and logic programming And much, much more... Book 2: Reinforcement Learning with Python Reinforcement learning is one of those data science fields, which will most certainly shape the world. The changes are already visible since we have self-driving cars, robots and much more we used to see only in some futuristic movies. Reinforcement learning is widely used machine learning technique, a computational approach when it comes to the different software agents, which are trying to maximize the total amount of possible reward they receive while interacting with some uncertain as well as very complex environments. This book is divided into seven chapters in which you will get to reinforcement techniques and methodology better. The first chapters will introduce you to the main concept laying being reinforcement learning techniques. Further, you will see what is the difference between reinforcement learning and other machine learning techniques. The book also provides some of the basic solution methods when it comes to the Markov decision processes, dynamic programming, Monte Carlo methods and temporal difference learning. What you will learn by reading this book: Types of fundamental machine learning algorithms in comparison to reinforcement learning Essentials of reinforcement learning process Marko decision processes and basic parameters How to integrate reinforcement learning algorithm using OpenAI Gym How to integrate Monte Carlo methods for prediction Monte Carlo tree search Dynamic programming in Python for policy evaluation, policy iteration and value iteration Temporal difference learning or TD And much, much more... Get this book bundle NOW and SAVE money!
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Neural Network Projects with Python

The ultimate guide to using Python to explore the true power of neural networks through six projects

Author: James Loy

Publisher: Packt Publishing Ltd

ISBN: 1789133319

Category: Computers

Page: 308

View: 7008

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Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Build expert neural networks in Python using popular libraries such as Keras Includes projects such as object detection, face identification, sentiment analysis, and more Book Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learn Learn various neural network architectures and its advancements in AI Master deep learning in Python by building and training neural network Master neural networks for regression and classification Discover convolutional neural networks for image recognition Learn sentiment analysis on textual data using Long Short-Term Memory Build and train a highly accurate facial recognition security system Who this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.
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Python: Advanced Guide to Artificial Intelligence

Expert machine learning systems and intelligent agents using Python

Author: Giuseppe Bonaccorso,Armando Fandango,Rajalingappaa Shanmugamani

Publisher: Packt Publishing Ltd

ISBN: 1789951720

Category: Computers

Page: 764

View: 9253

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Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.
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Python Programming

4 Manuscripts - Artificial Intelligence Python, Reinforcement Learning with Python, Text Analytics with Python, Convolutional Neural Networks in Python

Author: Anthony Williams

Publisher: Createspace Independent Publishing Platform

ISBN: 9781978317116

Category:

Page: 382

View: 4977

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Python Programming - 4 BOOK BUNDLE!! Book 1: Artificial Intelligence with Python What you will learn in this book: Different artificial intelligence approaches and goals How to define AI system Basic AI techniques Reinforcement learning How to build a recommender system Genetic and logic programming And much, much more... Book 2: Reinforcement Learning with Python What you will learn by reading this book: Types of fundamental machine learning algorithms in comparison to reinforcement learning Essentials of reinforcement learning process Marko decision processes and basic parameters How to integrate reinforcement learning algorithm using OpenAI Gym How to integrate Monte Carlo methods for prediction Monte Carlo tree search Dynamic programming in Python for policy evaluation, policy iteration and value iteration Temporal difference learning or TD And much, much more... Book 3: Reinforcement Learning with Python What you will learn by reading this book: Types of fundamental machine learning algorithms in comparison to reinforcement learning Essentials of reinforcement learning process Marko decision processes and basic parameters How to integrate reinforcement learning algorithm using OpenAI Gym How to integrate Monte Carlo methods for prediction Monte Carlo tree search Dynamic programming in Python for policy evaluation, policy iteration and value iteration Temporal difference learning or TD And much, much more... Book 4: Text Analytics with Python What you will learn by reading this book: Text analytics process How to build a corpus and analyze sentiment Named entity extraction with Groningen meaning bank corpus How to train your system Getting started with NLTK How to search syntax and tokenize sentences Automatic text summarization Stemming word and topic modeling with NLTK Using scikit-learn for text classification Part of speech tagging and POS tagging models in NLTK And much, much more... Get this book bundle NOW and SAVE money!
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Artificial Intelligence Python

A Short Introduction to Artificial Intelligence with Python

Author: Anthony Williams

Publisher: Createspace Independent Publishing Platform

ISBN: 9781976456695

Category:

Page: 84

View: 6031

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Artificial Intelligence With Python It is more than apparent that artificial intelligence techniques and practices will navigate the changes in the near future and simply shape the world. It is fair to say that AP is leading approach when it comes to the various scientific fields as well as various industries and today, it is almost impossible the world without advancements in the artificial intelligence field. Experts and scientists both agree that artificial intelligence is the field which will most certainly shape our economic future, automotive industry, health care, cybersecurity as well as cybercrime. Over the coming decades, AI will greatly impact every aspect of our lives including our work, careers, education, care for elderly and much more. Eventually, it will alter the world completely, as machines will pursue complex goals independently of their creators. AI tools have become mainstream tools when it comes to the various industries and science fields since these tools greatly reduce costs, increase profits and even save lives. If you understand the basic concept behind different AI techniques and approaches, you will be able to greatly benefit from it in various aspects. In order to maximise the benefits of AI advancements, you have to be ready to embark on different challenges. However, with this book, you will be able to overcome challenges and the reward is a success. What you will learn in this book: Different artificial intelligence approaches and goals How to define AI system Basic AI techniques Reinforcement learning How to build a recommender system Genetic and logic programming And much, much more... Get this book NOW and learn more about Artificial Intelligence With Python!
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AI for Data Science

Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond

Author: Zacharias Voulgaris PhD,Yunus Emrah Bulut

Publisher: Technics Publications

ISBN: 1634624114

Category: Computers

Page: N.A

View: 6753

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Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code. Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world. The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache’s MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA). Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline. Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS). Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on. A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book’s data and code. The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.
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