Deep Learning with Python

As a major breakthrough in artificial intelligence, deep learning has achieved impressive success on solving grand challenges in many fields including speech recognition, natural language processing,

Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targe

Leverage the power of Python design patterns to solve real-world problems in software architecture and designAbout This BookUnderstand the structural, creational, and behavioral Python design patterns

Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualizationAbout This BookLearn how to set up an optimal Python environment for data visualiz

Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This BookLearn the basics of Python in the Jupyter NotebookAnalyze and visualize data with pandas, NumPy,

Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming lan

Create a rich learning environment and support deeper thinking with primary sources. This resource provides teachers with thought-provoking questions to help students perform deep analysis of various

Shift classroom structures to enhance student success. By becoming learning partners with their students, teachers can help them develop enthusiasm for learning and employ deep learning goals. Discove

It's easy to start writing code with Python: that's why the language is so immensely popular. However, Python has unique strengths, charms, and expressivity that can be hard to grasp at first -- as we

If you are a Python programmer or a security researcher who has basic knowledge of Python programming and want to learn about penetration testing with the help of Python, this book is ideal for you. E

This book is for Python programmers with an intermediate background and an interest in design patterns implemented in idiomatic Python. Programmers of other languages who are interested in Python can

If you are a quality testing professional, or a software or web application developer looking to create automation test scripts for your web applications, with an interest in Python, then this is the

A guide to completing Python projects for those ready to take their skills to the next levelPython Projects is the ultimate resource for the Python programmer with basic skills who is ready to move be

Dig deep into the data with a hands-on guide to machine learningMachine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for

Fiction. A.W. DeAnnuntis writes with verve, deep learning, and comedic panache, creating improbable worlds that manage, somehow, to make sense.

If you want to learn how to program in Python, but don't know where to start read on. Knowing where to start when learning a new skill can be a challenge, especially when the topic seems so vast. Ther

This book is intended for Python programmers interested in learning how to do natural language processing. Maybe you've learned the limits of regular expressions the hard way, or you've realized that

Idiot's Guides: Beginning Programming takes the fear out of learning programming by teaching readers the basics with Python, an open-source (free) environment which is considered one of the easiest la

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate

Advanced Python for Biologists is a programming course for workers in biology and bioinformatics who want to develop their programming skills. It starts with the basic Python knowledge outlined in Pyt

Learning Trajectories, Violence and Empowerment amongst Adult Basic Skills Learners offers deep insights into the lives of marginalised communities and the link between learning, literacy and violence

Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that

Kivy: Interactive Applications in Python is an easy-to-follow book that will guide you into the world of Kivy. This book is aimed at Python developers who are familiar with Python and have a good unde

Learning to program is one of the best investments that you can make for your research and your career. Python for biologists is a complete programming course for beginners that will give you the skil

Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that

Putting Assessment for Learning into Practice is about the purpose of teaching and assessment as a means to ensuring deep, maximised, engaged and challenging learning. The simple me

Mark Pilgrim's Dive Into Python 3 is a hands-on guide to Python 3 and its differences from Python 2. As in the original book,Dive Into Python, each chapter starts with a real, complete code sample, pr

Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on such topics as GANs, transformers, BERT, GPT, VAE, adversarial examples, pre-training and fine-tuning, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with ne

Migrate from pandas and scikit-Learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na鴳e Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using

Machine learning-a computer's ability to learn-is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well.Introduction to Machine Learning weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kep

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

《Python+Office：輕松實現Python辦公自動化》分為6篇。第1篇Python編程基礎篇，介紹Python語言及開發環境搭建、Python編程基礎、利用Python進行數據準備；第2篇Excel數據自動化處理篇，介紹利用Python進行數據處理、數據分析和數據可視化；第3篇Word文本自動化處理篇，介紹文本自動化處理、利用Python進行文本自動化處理、利用Python制作企業運營月報Word版；第4篇幻燈片自動化制作篇，介紹幻燈片自動化制作、利用Python進行幻燈片自動化制作、利用Python制作企業運營月報幻燈片；第5篇郵件自動化處理篇，介紹利用Python批量發送電子郵件、利用Python獲取電子郵件、利用Python自動發送電商會員郵件；第6篇文件自動化處理篇，介紹利用Python進行文件自動化處理。《Python+Office：輕松實現Python辦公自動化》從實際工作需求的角度，詳細介紹了基於Python的辦公自動化技術，既可以作為職場人員學習Python辦公自動化的自學用書，也可以作為高等院校相關專業學生的參考用書。

《Python超實用：全彩：你的Python實踐工具書》全面介紹了Python與編程的基礎知識，也講解了如何開發應用於不同場景的程序。讀者可跟隨作者的講解進行實際的操作和練習。《Python超實用：全彩：你的Python實踐工具書》是面向各層次的Python讀者而作的Python入門書，全書共分為9個部分，主要介紹了包括Python的基本概念、Python庫、Tello EDU編程、用Python

《Python編程實戰》是一本語法與實踐相結合的Python入門教程，以簡明的語言、易懂的案例介紹Python編程。 《Python編程實戰》共11章，其中，第1-4章介紹Python的基礎知識，如Python的下載與安裝，Python結構，Python程序、算法和函數，Python擴展、文件和繪圖；第5-8章介紹Python的類和方法、標準庫、高級編程和仿真測試

Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Summary Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you'll build your own quantum simulator and apply quantum programming techniques to real-world examples including cryptography and chemical analysis. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Quantum computers present a radical leap in speed and computing power. Improved scientific simulations and new frontiers in cryptography that are impossible with classical computing may soon be in reach. Microsoft's Quantum Development Kit and the Q# language give you the tools to experiment with quantum computing without knowing advanced math or theoretical physics. About the book Learn Quantum Computing with Python and Q# introduces quantum computing from a practical pers

Fully-updated, second edition of this worldwide bestseller with over 100,000 copies sold. When it comes to creating powerful and effective hacking tools, Python is the language of choice for most secu

① Do you know what AI is doing to improve our health and wellbeing?② Does this new technology concern you, or impress you?③ Do you want to know more about the future of AI in healthcare?Technology continues to advance at a pace that can seem bewildering. Nowhere else is it moving faster than in the health sector, where ♥AI is now being used to improve millions of lives♥.In this book, ◆ Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone ◆, you can discover the great improvements that AI is making, with chapters covering: ✓ The current applications and future of AI in healthcare and all major medical specialties✓ The benefits and risks weighed up✓ The ethics involved✓ Machine learning and data science simplified✓ AI's role in medical research and education, health insurance, drug discovery, electronic health records, and the fight against COVID-19✓ The roles that major corporations and start-up companies are playing✓ The

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

With new deep-sea friends to meet, colours to name, things to spot and lots to count, this peek-through book is perfect to share with little ones.Toddlers are learning new things all the time, and the