Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms 2nd Edition
暫譯: 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
- 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
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相關分類:
DeepLearning、Algorithms-data-structures
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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性成長中。深度學習解鎖了超人類的感知能力，推動我們朝著創造自駕車的目標邁進，並在包括圍棋在內的各種困難遊戲中擊敗人類專家，甚至生成令人驚訝的連貫散文。然而，解讀這些突破通常需要機器學習和數學的博士學位。
本書的更新第二版描述了這些創新背後的直覺，沒有行話或複雜性。精通Python的程式設計師、軟體工程專業人士和計算機科學專業學生將能夠自行重新實現這些突破，並以與該領域一些最佳開發者相媲美的深度進行推理。
- 學習機器學習行話背後的數學
- 檢視機器學習和神經網絡的基礎
- 管理在開始加深網絡時出現的問題
- 建立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，該公司是一家位於哈佛和史丹佛的初創企業，致力於幫助醫療保健公司利用其龐大的數據集。
Nikhil Buduma 是 Remedy 的共同創辦人及首席科學家，該公司位於舊金山，正在建立一個以數據為驅動的初級醫療保健新系統。在 16 歲時，他在聖荷西州立大學管理一個藥物發現實驗室，並為資源有限的社區開發了新穎的低成本篩選方法。到 19 歲時，他已經是國際生物奧林匹克競賽的兩屆金牌得主。之後，他進入麻省理工學院（MIT），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 MIT，他共同創辦了 Lean On Me，這是一個全國性的非營利組織，提供匿名短信熱線，以促進大學校園內有效的同儕支持，並利用數據來促進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投資基金 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一個數據分析團隊。
Joe Papa 擁有超過 25 年的研究與開發經驗，是 INSPIRD.ai 的創始人。他擁有電機工程碩士學位（MSEE），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 上教授了超過 6,000 名學生。
