Matriz de recursos de Deep Learning
El siguiente recurso describe los siguientes marcos:
TensorFlow
Theano
Cafe
MXNet
apache
SystemML (proyecto de incubadora)
BigDL
DistBelief
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Medium
C++ Notes for Professionals book
C++ Notes for Professionals book is compiled from Stack Overflow Documentation,. 708 pages, published on May 2018
AutoML: a introduction tutorial about H2O Driverless AI
«H2O has been the driver for building models at scale. We are talking about billions of claims. You can’t do this with standard off the shelf open source techniques». (H2o.ai).
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Resources on learning deeplearning from the Massachusetts Institute of Technology
A great selection of resources on learning deeplearning by Lex Fridman from the Massachusetts Institute of Technology (MIT).
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Python for Everybody: Exploring Data in Python 3
Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet. (Dr. Charles R. Severance)
Natural Language Processing Succinctly
AI assistants represent a significant frontier for development. But the complexities of such systems pose a significant barrier for developers. In Natural Language Processing Succinctly, author Joseph Booth will guide readers through designing a simple system that can interpret and provide reasonable responses to written English text. With this foundation, readers will be prepared to tackle the greater challenges of natural language development. (Syncfusion).
Natural Language Processing with Python
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library.
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Scikit-Learn
Scikit-Learn Tutorial (Materials for my scikit-learn tutorial)
By Jake VanderPlas who is the director of Open Software at the University of Washington’s eScience institute, and researches and teaches in a variety of areas, including Astronomy, Astrostatistics, Machine Learning, and Scalable Computation.
Neural Networks with JavaScript Succinctly
Although most concepts are relatively simple, there are many of them, and they interact with each other in unobvious ways, which is a major challenge of neural networks. But you can learn all important neural network concepts by running and examining the code in this book, with complete example programs for the three major types of neural network problems.
JavaScript® Notes for Professionals book
This JavaScript® Notes for Professionals book is compiled from Stack Overflow Documentation. (490 pages, published on June 2018)