Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)

Sandro Skansi

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商品描述

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.

Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.

 

 

 

This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

商品描述(中文翻譯)

這本教科書提供了一個簡潔、易懂且引人入勝的深度學習初級介紹,介紹了當前最先進的連接模型。本書以簡單直觀的方式探討了最受歡迎的演算法和架構,並以逐步解釋數學推導的方式呈現。內容涵蓋了卷積網絡、LSTMs、Word2vec、RBMs、DBNs、神經圖靈機、記憶網絡和自編碼器。書中提供了大量的Python程式碼示例,並且程式碼也可以在附帶的網站上單獨獲取。

本書的主題和特點包括:介紹機器學習的基礎知識,以及深度學習的數學和計算先決條件;討論前饋神經網絡,並探討可應用於任何神經網絡的修改;研究卷積神經網絡以及與前饋神經網絡的循環連接;描述分佈式表示的概念、自編碼器的概念以及深度學習在語言處理中的應用思想;簡要介紹人工智慧和神經網絡的歷史,並回顧深度學習和連接主義中有趣的開放性研究問題。

這本關於深度學習的清晰寫作和生動入門讀物對於計算機科學、認知科學和數學的研究生和高年級本科生以及語言學、邏輯學、哲學和心理學等領域的學生來說是必讀的。