Deep Learning with Python

Educator and neuroscientist Dr. Jared Cooney Horvath reveals why digital tools in school consistently undermine learning--and what parents, teachers, and schools can do to push back with purpose. Our children are struggling. Schools, once alive with deep learning born of human connection, are now dominated by screens and digital tools. The result is unmistakable: falling performance, fractured attention, and the slow erosion of rigorous thought. For the first time in the history of standardized cognitive measurement, children are consistently scoring lower on key measures of cognitive development. We were told that classroom technology was progress. It wasn't. In The Digital Delusion, neuroscientist and educator Dr. Jared Cooney Horvath shows how the widespread use of laptops, tablets, and educational software in classrooms is quietly undermining how children learn, think, and develop. Drawing on decades of neuroscience and education research, Horvath dismantles the core myths driving

若要打造可推理及提取外部資料、理解前後脈絡、可投入正式環境的 AI 應用程式，你就必須熟悉熱門的開發框架與平台 LangChain。它可以用來建立、執行與管理有自主行動能力的 app。目前已有許多頂尖公司採用 LangChain，包括 Zapier、Replit、Databricks 等。對於已經學會 Python 或 JavaScript，且想要掌握 AI 能力的新手開發者而言，本書是必備的學習資源。 作者 Mayo Oshin 與 Nuno Campos 透過實用的見解與深入的教學，帶領你逐步掌握 LangChain 的運用。從基礎概念開始，一步步帶你建立一個可正式上線，並且能夠使用個人資料的 AI agent。 • 運用 retrieval-augmented generation（RAG）技術，結合外部的即時資料來提升 LLM 的準確性。 • 開發並部署能夠與使用者聰明地互動，並且記得前後脈絡的 AI 應用程式。 • 透過 LangGraph 來使用強大的 agent 架構。 • 整合並管理第三方 API 與工具，以擴充 AI 應用程式的功能。 • 監控、測試與評估 AI 應用程式，以提升效能。 • 瞭解 LLM app 開發的基礎知識，並學習如何在 LangChain 上加以活用。 ------------------------------------------------------------- 「本書包含條理分明的講解和可落實的技巧，是掌握 LangChain 的強大功能，並用它來製作可上線的生成式 AI 與 agent 的首選資源。對於想充分利用此平台之潛力的開發者來說，是必讀之作。」 ── Tom Taulli，IT 顧問暨《AI輔助程式開發》作者 「這本完整的指南涵蓋文件提取與檢索，以及在正式環境中部署與監控 AI agent 的完整知識。透過引人入勝的範例、直覺的圖解與實際的程式碼，讓 LangChain 變得既有趣又好玩！」 ── Rajat K. Goel，IBM 資深軟體工程師 「這是一本完整的 LLM 指南，不只介紹基礎知識，也探討生產階段，充滿技術見解、實用策略，以及強大的 AI 模式。」 ── Gourav Singh Bais，Allianz Services 資深資料科學家暨技術內容撰寫人

這本書引導讀者從廣闊的人工智慧來到深度學習的核心概念，深化對機器學習的理解，並揭示電腦視覺如何利用機器學習來模仿和擴展人類的視覺能力。深度學習顯著提升了電腦視覺技術的效能，而成功的電腦視覺案例又進一步推進深度學習領域的發展，形成一個良性循環。　書中不僅提供扎實的理論基礎，更搭配豐富實用的範例，這些範例都是用強大且被廣泛運用的 Python 程式語言編寫。透過這些範例，讀者能更直觀地掌握書中所述的概念，並將理論付諸實踐。　本書特別為對影像處理與電腦視覺懷抱熱情的讀者而設計，無論是大學部或研究所的學生，或是渴望自學精進的其他族群，都能從中獲益良多。

深度學習正日益深刻地滲入我們的生活，從建議內容到在任務關鍵型和安全關鍵型應用中發揮核心作用，其影響無所不在。然而，隨著這些算法影響力的逐漸擴大，人們對於依賴這些算法的系統安全性和魯棒性的擔憂也日益加劇。簡言之，傳統的深度學習方法往往難以察覺自身的知識邊界，即它們“不知其所不知”。貝葉斯深度學習(Bayesian Deep Learning，BDL)領域包含一系列利用深度網絡進行近似貝葉斯推理的方法。這些方法通過揭示模型對其預測結果的置信度，增強了深度學習系統的魯棒性，使我們能夠更謹慎地將模型預測融入實際應用中。《Python貝葉斯深度學習》將引領你踏入迅速發展的不確定性感知深度學習領域，助你深入理解不確定性估計在構建魯棒性的機器學習系統中的重要價值。你將學習多種流行的BDL方法，並通過涵蓋多種應用場景的Python實用示例來掌握這些方法的實現技巧。讀完本書後，你將深刻理解BDL及其優勢，並能夠為更安全、更魯棒的深度學習系統開發貝葉斯深度學習模型。主要內容：● 了解貝葉斯推理和深度學習的優缺點● 了解貝葉斯神經網絡(Bayesian Neural Network，BNN)的基本原理● 了解主要貝葉斯神經網絡實現/近似之間的差異● 了解生產環境中概率深度神經網絡的優勢● 在Python代碼中實現各種貝葉斯深度學習方法● 運用貝葉斯深度學習方法解決實際問題● 學習如何評估貝葉斯深度學習方法並為特定任務選擇最佳方法● 在實際深度學習應用中處理“分布外”數據

建立智慧型系統的概念、工具與技術「這是一本出色的機器學習資源，包含淺顯易懂的解說，以及豐富的實用技巧。」—François CholletKeras作者，《Deep Learning with Python》作者「本書是運用神經網路來解決問題的絕佳入門資源，涵蓋理論及實踐。推薦給想學習實用機器學習技術的人。」—Pete WardenTensorFlow行動主管深度學習在經歷了一系列的突破之後，已經推動了整個機器學習領域的發展。如今，即使是對於這項技術非常陌生的程式設計師，也能夠使用簡單、高效率的工具，寫出能從資料中學習的程式。這本暢銷書使用具體的例子、最少的理論，以及具備生產水準的Python框架(Scikit-Learn、Keras和TensorFlow)來協助你直接瞭解智慧系統的建構概念與工具。在這本第三版中，作者Aurélien Géron將探索一系列的技術，從簡單的線性回歸開始，逐步發展到深度神經網路。本書包含許多範例程式和習題來幫助活用所學，只要具備一些程式設計經驗即可入門。‧使用Scikit-Learn自始至終完成機器學習專案‧探索多種模型，包括支援向量機、決策樹、隨機森林，和集成方法‧運用無監督學習技術，例如降維、聚類法和異常檢測‧深入探討神經網路架構，包括摺積神經網路、遞迴網路、生成對抗網路、自動編碼器、擴散模型、轉換器‧使用TensorFlow和Keras建構和訓練神經網路，以進行計算機視覺、自然語言處理、生成模型和深度強化學習

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

Fans won't want to miss this original canon story set in the world of the Emmy® Award-winning Netflix animated series The Dragon Prince, with story by the creators of the series and of the New York Times bestselling Through the Moon (The Dragon Prince Graphic Novel #1).Learning magic is no easy thing, even for young Claudia. Despite a bit of help from a book of spells created by Kpp’Ar, her father’s former mentor, it’s all she can do to keep things from blowing up in her face—often with spectacularly messy results.But a spell book isn’t the only thing that Kpp’Ar left behind when he suddenly and mysteriously vanished. In his wake stands the Puzzle House, a bizarre tower full of magical traps, tricks, contraptions, and—what else—puzzles. Claudia is sure that the old mage left a gift for her and her brother, Soren, somewhere deep within its walls, and she is determined to find out exactly what it is. Can Claudia and Soren uncover all the mysteries hidden within the Puzzle House? Or will

PyTorch是Facebook於2017年初在機器學習和科學計算工具Torch的基礎上，針對Python語言發佈的一個全新的機器學習工具套件，一經推出便受到業界廣泛關注和討論，目前已經成為機器學習從業人員首選的一款研發工具。 本書是使用PyTorch建構神經網絡模型的實用指南，內容分為9章，包括PyTorch與深度學習的基礎知識、神經網路的構成、神經網路的高階知識、機器學習基礎知識、深度學習在電腦視覺上的應用、深度學習在序列資料和文字當中的應用、生成網路、現代網路架構，以及PyTorch與深度學習的未來走向。 本書適合對深度學習領域感興趣且希望一探PyTorch究竟的業界人士閱讀。具備其他深度學習框架使用經驗的讀者，也可以透過本書掌握PyTorch的用法。 本書範例檔： https://github.com/PacktPublishing/Deep-Learning-with-PyTorch

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication.To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an al

Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more.Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors.Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for unit te

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod

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.

Immerse yourself in fascinating facts about water! This comprehensive yet accessible exploration of water will help young readers understand many aspects of one of our planet's most precious resources - and how they can protect it. A friendly water droplet character guides children through topics ranging from melting and freezing to the ways in which water literally shapes the Earth. Tales by storytellers from around the world are sprinkled through the book, highlighting the variety of ways in which global cultures value water. The engaging format includes gatefolds and booklets with hands-on activity ideas for learning about and protecting water. Topics covered include: The importance of water to life How much of the planet is made of water Where in the world water is located Freezing, melting and evaporation The water cycle Why animals and plants need water Salt water versus fresh water environments Uses for water, including water as a source of renewable energy Water conservation an

Ⓞ 16 堂課引領入門, 學得會、做得順的絕佳教材！Ⓞ 最詳盡的深度學習基石書, CNN + RNN + GAN + DQN + DRL 各種模型學好學滿初學者想要自學深度學習 (Deep Learning), 可以在市面上找到一大堆「用 Python 學深度學習」、「用 xxx 框架快速上手深度學習」的書；也有不少書說「請從數學複習起！」, 捲起袖子好好探究底層那些數學原理......但過早切入

Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging – including compressed sensing, wavelets and optimization – in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli

This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples.