Python For Ai

DOWNLOAD

Download Python For Ai PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Python For Ai book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Python Ai Programming

DOWNLOAD
Author : Patrick J
language : en
Publisher: GitforGits
Release Date : 2024-01-03

Python Ai Programming written by Patrick J and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-03 with Computers categories.

This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable. Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence. Key Learnings Explore Python basics and AI integration for real-world application and career advancement. Experience the power of Python in AI with practical machine learning techniques. Practice Python's deep learning tools for innovative AI solution development. Dive into NLP with Python to revolutionize data interpretation and communication strategies. Simple yet practical understanding of reinforcement learning for strategic AI decision making. Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI. Harness Python's capabilities for creating AI applications with a focus on fairness and bias. Table of Content Introduction to Artificial Intelligence Python for AI Data as Fuel for AI Machine Learning Foundation Essentials of Deep Learning NLP and Computer Vision Hands-on Reinforcement Learning Ethics to AI

Artificial Intelligence Programming With Python

DOWNLOAD
Author : Perry Xiao
language : en
Publisher: John Wiley & Sons
Release Date : 2022-02-21

Artificial Intelligence Programming With Python written by Perry Xiao and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-21 with Computers categories.

A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learning Expansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learning Practical AI and Python “cheat sheet” quick references This hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.

Python

DOWNLOAD
Author : Giuseppe Bonaccorso
language : en
Publisher:
Release Date : 2018-12-19

Python written by Giuseppe Bonaccorso and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-19 with Computers categories.

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.

Ai With Python For Beginners

DOWNLOAD
Author : Jim Smith
language : en
Publisher:
Release Date : 2019-07-30

Ai With Python For Beginners written by Jim Smith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-30 with categories.

AI With PythonSince the invention of computers or machines, their capability to perform various tasks has experienced an exponential growth. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.Artificial intelligence's progress is staggering. Efforts to advance AI concepts over the past 20 years have resulted in some truly amazing innovations. Big data, medical research, and autonomous vehicles are just some of the incredible applications emerging from AI development.This book covers the basic concepts of various fields of artificial intelligence like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms etc., and its implementation in Python.What You Will Learn: -Introduction-Machine Learning-Data Preparations-Supervised Learning-Logic Programming-Clustering-Natural Language Processing-Time Series Data-Speech Recognition-Heuristic Search-Gaming-Much, Much More!

Python Artificial Intelligence Projects For Beginners

DOWNLOAD
Author : Dr. Joshua Eckroth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-31

Python Artificial Intelligence Projects For Beginners written by Dr. Joshua Eckroth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-31 with Computers categories.

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

Deep Learning With Pytorch Lightning

DOWNLOAD
Author : Kunal Sawarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-29

Deep Learning With Pytorch Lightning written by Kunal Sawarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.

Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper Key FeaturesBecome well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook Description PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time. You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging. By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning. What you will learnCustomize models that are built for different datasets, model architectures, and optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho this book is for This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.

The Python Ai Blueprint

DOWNLOAD
Author : Eric Porter
language : en
Publisher: Independently Published
Release Date : 2025-08-20

The Python Ai Blueprint written by Eric Porter and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-20 with Computers categories.

Unlock the Power of Python and Artificial Intelligence-No Experience Required! Are you ready to dive into the world of coding, automation, and artificial intelligence but don't know where to start? The Python AI Blueprint is your step-by-step guide to learning Python while building real, practical projects that prepare you for today's tech-driven world. This beginner-friendly guide walks you through the essentials of Python programming, from writing your first lines of code to creating automation scripts, analyzing data, and building AI projects. Every chapter is designed to be hands-on and approachable, making sure you not only learn the concepts but also apply them immediately through engaging projects. By the end of this book, you'll have the confidence to write Python code, automate everyday tasks, analyze real datasets, and even create AI-powered applications. Whether you're looking to boost your career, explore a new hobby, or step into the future of technology, this book gives you the blueprint to succeed. Inside, you'll discover how to: Learn Python programming from scratch with clear explanations and examples Automate repetitive tasks to save time and increase productivity Work with data to gain valuable insights using modern Python libraries Build beginner-friendly AI projects and understand the basics of machine learning Develop a solid foundation in coding that you can apply to real-world scenarios About the Author Eric Porter is passionate about making coding and artificial intelligence accessible to everyone. With a clear teaching style and a focus on practical projects, he helps beginners learn the skills they need to thrive in today's digital world. Start your journey into coding and AI today.

Python Programming For Beginners

DOWNLOAD
Author : Wojciech Matusik
language : en
Publisher: Independently Published
Release Date : 2025-07-04

Python Programming For Beginners written by Wojciech Matusik and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-04 with Computers categories.

Python Artificial Intelligence Development: AI & ML Essentials and Python AI Programming Book is your all-in-one guide to mastering Python for Artificial Intelligence, Machine Learning, and real-world applications. Whether you're a complete beginner or an aspiring developer seeking to build AI-powered solutions, this book provides a comprehensive learning path tailored to practical success. From foundational Python programming to cutting-edge AI projects and financial applications, each chapter is carefully crafted to teach, inspire, and empower you to create intelligent systems that matter. Unlock the power of AI with Python through hands-on projects, easy-to-understand explanations, and detailed examples covering both beginner and intermediate levels. This book combines essential AI and ML theory with practical tools to help you build solutions that work in real-world scenarios-from chatbots and data models to finance-based analytics. Inside this book, you will discover: Master Python fundamentals tailored for AI beginners Learn the essential building blocks of Python programming with a focus on Artificial Intelligence applications. You'll gain a strong foundation in variables, loops, functions, and object-oriented programming while preparing for more complex AI tasks. Explore artificial intelligence and machine learning techniques step by step Understand how AI systems are designed by diving into supervised, unsupervised, and reinforcement learning models. This section equips you with theoretical knowledge and practical code implementations in Python using key libraries. Build real-world AI projects with Python to sharpen your skills Develop and deploy intelligent systems like recommendation engines, natural language processors, AI chatbots, and image recognition apps. Each project includes annotated code, development strategy, and optimization tips. Integrate Python into finance applications with AI-powered insights Discover how Python is transforming the finance world with predictive analytics, algorithmic trading, fraud detection, and data visualization. Learn how to write efficient Python scripts that automate tasks and deliver financial intelligence. Boost your career with in-demand Python AI programming techniques Learn how to use libraries such as NumPy, pandas, scikit-learn, TensorFlow, and Keras to solve real-life problems. Build a portfolio of projects that showcase your Python AI development skills to future employers or clients. Designed for learners who want clarity, confidence, and real progress This book eliminates technical jargon and teaches through visual diagrams, clear examples, and simplified code walkthroughs. Whether you're self-learning or supplementing a course, it's your ideal resource for AI development in Python. Start your journey into the world of Artificial Intelligence today, and equip yourself with the tools and knowledge to create intelligent applications using Python!

Mastering Ai And Machine Learning With Python

DOWNLOAD
Author : Anshuman Mishra
language : en
Publisher: Independently Published
Release Date : 2025-05-12

Mastering Ai And Machine Learning With Python written by Anshuman Mishra and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-12 with Computers categories.

This ambitious two-volume work, "Mastering AI and Machine Learning with Python: From Fundamentals to Advanced Deep Learning," aims to be a definitive guide for anyone seeking to understand, implement, and master the intricate world of Artificial Intelligence (AI) and Machine Learning (ML) using the versatile Python programming language. Spanning a projected 10,000 words across both volumes (with Volume 1 detailed below), this book meticulously progresses from foundational concepts to cutting-edge deep learning techniques, providing readers with a robust theoretical understanding coupled with practical implementation skills. Volume 1: Foundations and Core Machine Learning Techniques Volume 1 lays the essential groundwork for embarking on the journey of AI and ML. It is structured to take individuals with varying levels of prior knowledge - from complete beginners to those with some programming experience - and equip them with the core competencies required to understand and apply fundamental machine learning algorithms. Chapter 1: Introduction to AI and Machine Learning This introductory chapter serves as a compass, orienting the reader within the broad landscape of AI and its subfields. It begins by clearly delineating the concepts of Artificial Intelligence, Machine Learning, and Deep Learning, highlighting their relationships and distinctions. Understanding AI, Machine Learning, and Deep Learning: This section meticulously unpacks these often-interchangeable terms. It defines AI as the overarching field focused on creating intelligent agents capable of performing tasks that typically require human intelligence. Machine Learning is then presented as a subset of AI, where systems learn from data without being explicitly programmed. Finally, Deep Learning is introduced as a subfield of ML that utilizes artificial neural networks with multiple layers (deep neural networks) to extract complex patterns from large datasets. The chapter will use analogies and real-world examples to solidify these definitions, ensuring a clear understanding of the hierarchy and unique characteristics of each field. Real-World Applications of AI: To underscore the practical relevance and transformative power of AI, this section delves into a diverse range of real-world applications. It will explore how AI is revolutionizing industries such as healthcare (diagnosis, drug discovery), finance (fraud detection, algorithmic trading), transportation (autonomous vehicles), entertainment (recommendation systems), manufacturing (predictive maintenance), and customer service (chatbots). Each application will be briefly described, highlighting the specific AI techniques employed and the tangible benefits realized. This section aims to inspire the reader and contextualize the learning journey ahead. The Role of Python in AI Development: This crucial segment emphasizes why Python has emerged as the lingua franca of AI and ML. It will discuss Python's key advantages, including its clear and concise syntax, extensive ecosystem of powerful libraries (such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch), large and active community support, and its versatility for various stages of the AI development lifecycle - from data preprocessing to model deployment. The chapter will briefly introduce some of these key libraries, setting the stage for their detailed exploration in subsequent chapters. Overview of TensorFlow and PyTorch: As two of the most prominent deep learning frameworks, TensorFlow and PyTorch are introduced in this section. The chapter will provide a high-level overview of their functionalities, key features, and their respective strengths and weaknesses. It will touch upon their roles in building and training neural networks, their support for hardware acceleration (GPUs), and their growing adoption in both research and industry.

Explainable Ai With Python

DOWNLOAD
Author : Leonida Gianfagna
language : en
Publisher: Springer Nature
Release Date : 2021-04-28

Explainable Ai With Python written by Leonida Gianfagna and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Computers categories.

This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.