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Math and Architectures of Deep Learning MEAP V02

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Math and Architectures of Deep Learning...

Math and Architectures of Deep Learning MEAP V02

Krishnendu Chaudhury, Ananya Ashok, Sujay Narumanchi, Devashish Shankar

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Chapters 1 - 4

Κατηγορίες:

Computers - Computer Science

Έτος:

2020

Εκδότης:

Manning Publications

Γλώσσα:

english

ISBN 10:

1617296481

ISBN 13:

9781617296482

Αρχείο:

EPUB, 4.67 MB

IPFS:

,

english, 2020

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Ίσως σας ενδιαφέρει

Edward Raff — Inside Deep Learning: Math, Algorithms, Models [MEAP]

Bob Gregory & Harry Percival [Bob Gregory] — Architecture Patterns with Python

Krishnendu Chaudhury — Math and Architectures of Deep Learning (MEAP V10)

Andrew Glassner — Deep Learning, Vol. 1: From Basics to Practice

Mohamed Elgendy — Deep Learning for Vision Systems MEAP V06

Paul Orland — Math for Programmers: 3D graphics, machine learning, and simulations with Python MEAP V10

Nina Zumel; John Mount — Practical Data Science with R, Second Edition MEAP V06

Hasanraza Ansari — TensorFlow [Deep Learning and Artificial Intelligence, Machine Learning]

Marcello La Rocca — Algorithms and Data Structures in Action MEAP V05

Stephan Raaijmakers — Deep Learning for Natural Language Processing MEAP V04

Mohamed Elgendy — Deep Learning for Vision Systems MEAP V06

Aston Zhang, Zachary C. Lipton, Mu Li & Alexander J. Smola — Dive into Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron Courville — Deep Learning

Michael Taylor, Mark Koning — The Math of Neural Networks

Martin Anthony, Peter L. Bartlett — Neural Network Learning: Theoretical Foundations

Martin T. Hagan et al. — Neural Network Design

Steven L. Brunton, J. Nathan Kutz — Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

Doug Turnbull, John Berryman — Relevant Search: With applications for Solr and Elasticsearch

Corey Lanum — Visualizing Graph Data - MEAP edition

Will Kurt — Get Programming with Haskell

Mostafa Samir Abd El-Fattah — How Machine Learning Works MEAP V05

Eli Stevens, Luca Antiga, Thomas Viehmann — Deep Learning with PyTorch

Milan Curcic — Modern Fortran: Building Efficient Parallel Applications

Paul Orland — Math for Programmers - 3D graphics, machine learning, and simulations with Python - MEAP Version 10

Stephan Raaijmakers — Deep Learning for Natural Language Processing (MEAP V07)

Marcello La Rocca — Algorithms and Data Structures in Action MEAP V12

Reuven M. Lerner — Python Workout: 50 Essential Exercises (MEAP Early Access v03)

Jesse C. Daniel — Data Science with Python and Dask

Nina Zumel, John Mount — Practical Data Science with R

Luis G. Serrano — Grokking Machine Learning MEAP V07

David Kopec — Classic Computer Science Problems in Python

Oliver Duerr, Beate Sick, Elvis Murina — Probabilistic Deep Learning with Python

Peter Elger; Eoin Shanaghy — AI as a Service: Serverless machine learning with AWS

Krishnendu Chaudhury — Math and Architectures of Deep Learning

Alexey Grigorev — Machine Learning Bookcamp: Build a Portfolio of Real-Life Projects (MEAP v10)

Francois Chollet — Deep Learning with Python, Second Edition.

Veljko Krunic — Succeeding with AI: How to Make AI Work for Your Business

Marco Vermeulen, Rúnar Bjarnason, Paul Chiusano — Functional Programming in Kotlin, MEAP v08

Carl M. Osipov — Cloud Native Machine Learning (MEAP V05)

Dzejla Medjedovic, Emin Tahirovic, Ines Dedovic — Algorithms and Data Structures for Massive Datasets (MEAP v8)

Andrew Ferlitsch — Deep Learning Design Patterns (MEAP V03)

Ben T. Wilson — Machine Learning Engineering MEAP V2

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