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Ebook Free Deep Learning with R
Ebook Free Deep Learning with R
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Deep Learning with R
Ebook Free Deep Learning with R
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About the Author
Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io.J.J. Allaire is the Founder of RStudio and the creator of the RStudio IDE. J.J. is the author of the R interfaces to TensorFlow and Keras.
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Product details
Paperback: 360 pages
Publisher: Manning Publications; 1 edition (February 9, 2018)
Language: English
ISBN-10: 9781617295546
ISBN-13: 978-1617295546
ASIN: 161729554X
Product Dimensions:
7.2 x 0.8 x 9.2 inches
Shipping Weight: 1.4 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
12 customer reviews
Amazon Best Sellers Rank:
#78,291 in Books (See Top 100 in Books)
It is an awesome book, because explains deep learning for dummies without losing technical accuracy. The vas thing is that printed in a black and white format, so you loose the details related yo the visuals. Also this force you to check the PDF version oficina the book.
Very good!
This is thee book if you want to get started in deep learning with keras in R. It is a great intro to neural networks and how they learn. This book also goes over the syntax, functions, and organization of neural nets in keras. Overall a great book.
Great book with examples. Took me from zero to expert
In three words- an outstanding book!
This is an excellent introduction to deep learning, especially if you have intermediate or better skills with the R programming language. The book focuses on using the Keras library and TensorFlow (with "light instructions" relative to Theano but no coverage of CNTK). The topics covered include fundamentals of deep learning and neural networks, how to set up your own deep-learning environment (Ubuntu is recommended; Windows is not), image classification and generation tasks, and deep learning for text and sequences.The authors explain: "Deep learning is a specific subfield of machine learning: a new take on learning representations from data that puts an emphasis on learning successive layers of increasingly meaningful representations. The deep in deep learning isn't a reference to any kind of deeper understanding achieved by the approach; rather, it stands for this idea of successive layers of representation. How many layers contribute to a model of the data is called the depth of the model. Other appropriate names for the field could have been layered representation learning and hierarchical representations learning. Modern deep learning often involves tens or even hundreds of successive layers of representations--and they're all learned automatically from exposure to training data. Meanwhile, other approaches to machine learning tend to focus on learning only one or two layers of representation of the data; hence, they're sometimes called shallow learning."Later in the book, they also note: "The fundamental issue in machine learning is the tension between optimization and generalization. Optimization refers to the process of adjusting a model to get the best performance possible on the training data (the learning in machine learning), whereas generalization refers to how well the trained model performs on data is has never seen before. The goal of the game is to get good generalization, of course, but you don't control generalization; you can only adjust the model based on its training data."The authors, by the way, are well qualified in the deep learning field. Francois Chollet is a deep-learning researcher at Google and author of the Keras library. J.J. Allaire, meanwhile, is the founder of RStudio, and wrote R interfaces to TensorFlow and KerasEven if you don't yet have adequate R skills, you can learn a lot about deep learning from the explanations and illustrations presented in this well-written book. Thus, if you are considering a career in machine learning or are just curious about what exactly deep learning is and how it works, "Deep Learning with R" can be beneficial reading.
Very clear, up to date.
The content of the book is ok. It provides a solid introduction to technical aspects of learning algorithms.The actual book is not ok. There are a volume of misprinted copies out there, and last I checked the code for access to the digital version does not work.
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