Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms 2nd Edition (深度學習基礎：設計下一代機器智慧演算法（第二版）)

Buduma, Nithin, Buduma, Nikhil, Papa, Joe

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms 2nd Edition
  • 出版商: O'Reilly
  • 出版日期: 2022-06-21
  • 定價: $2,560
  • 售價: 8.0 折 $2,048
  • 語言: 英文
  • 頁數: 387
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 149208218X
  • ISBN-13: 9781492082187
  • 相關分類: DeepLearning、Algorithms-data-structures

    • 立即出貨

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商品描述

We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics.

The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field.

- Learn the mathematics behind machine learning jargon
- Examine the foundations of machine learning and neural networks
- Manage problems that arise as you begin to make networks deeper
- Build neural networks that analyze complex images
- Perform effective dimensionality reduction using autoencoders
- Dive deep into sequence analysis to examine language
- Explore methods in interpreting complex machine learning models
- Gain theoretical and practical knowledge on generative modeling
- Understand the fundamentals of reinforcement learning

商品描述(中文翻譯)

我們正處於人工智慧研究的爆炸時期。深度學習已經解鎖了超人類的感知能力，推動我們朝著創造自駕車、在各種困難的遊戲中擊敗人類專家，甚至生成具有驚人連貫性的散文的目標前進。但要解讀這些突破往往需要擁有機器學習和數學博士學位。

本書的第二版更新描述了這些創新的直觀理解，不使用行話或複雜性。精通Python的程式設計師、軟體工程專業人員和計算機科學專業的學生將能夠自行重新實現這些突破並以與該領域最佳開發人員相媲美的水平進行推理。

- 學習機器學習術語背後的數學知識
- 檢視機器學習和神經網絡的基礎
- 解決在網絡變得更深時出現的問題
- 構建分析複雜圖像的神經網絡
- 使用自編碼器進行有效的降維
- 深入研究序列分析以檢視語言
- 探索解釋複雜機器學習模型的方法
- 獲得生成建模的理論和實踐知識
- 理解強化學習的基本原理

作者簡介

Nithin Buduma is one of the first machine learning engineers at XY.ai, a start-up based out of Harvard and Stanford working to help healthcare companies leverage their massive datasets.

Nikhil Buduma is the cofounder and chief scientist of Remedy, a San Francisco-based company that is building a new system for data-driven primary healthcare. At the age of 16, he managed a drug discovery laboratory at San Jose State University and developed novel low-cost screening methodologies for resource-constrained communities. By the age of 19, he was a two-time gold medalist at the International Biology Olympiad. He later attended MIT, where he focused on developing large-scale data systems to impact healthcare delivery, mental health, and medical research. At MIT, he cofounded Lean On Me, a national nonprofit organization that provides an anonymous text hotline to enable effective peer support on college campus and leverages data to effect positive mental health and wellness outcomes. Today, Nikhil spends his free time investing in hard technology and data companies through his venture fund, Q Venture Partners, and managing a data analytics team for the Milwaukee Brewers baseball team.

Joe Papa has over 25 years experience in research & development and is the founder of INSPIRD.ai. He holds an MSEE and has led AI Research teams with PyTorch at Booz Allen and Perspecta Labs. Joe has mentored hundreds of Data Scientists and has taught 6,000+ students across the world on Udemy.

作者簡介(中文翻譯)

Nithin Buduma是XY.ai的首批機器學習工程師之一，該初創公司位於哈佛和斯坦福，致力於幫助醫療保健公司利用其龐大的數據集。

Nikhil Buduma是Remedy的聯合創始人和首席科學家，Remedy是一家位於舊金山的公司，正在建立一個新的基於數據驅動的初級醫療系統。16歲時，他在聖荷西州立大學管理了一個藥物發現實驗室，並為資源有限的社區開發了新型低成本篩選方法。19歲時，他曾兩次在國際生物學奧林匹亞競賽中獲得金牌。後來，他就讀於麻省理工學院，專注於開發大規模數據系統，以影響醫療服務、心理健康和醫學研究。在麻省理工學院期間，他共同創辦了Lean On Me，這是一個國家性的非營利組織，提供匿名短信熱線，以在大學校園內實現有效的同儕支持，並利用數據實現積極的心理健康和福祉結果。如今，Nikhil在空閒時間通過他的風險基金Q Venture Partners投資於硬科技和數據公司，並為密爾瓦基釀酒人棒球隊管理數據分析團隊。

Joe Papa在研究與開發領域擁有超過25年的經驗，他是INSPIRD.ai的創始人。他擁有MSEE學位，並在Booz Allen和Perspecta Labs領導過使用PyTorch的人工智慧研究團隊。Joe指導過數百名數據科學家，並在Udemy上教授了6000多名學生。

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