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There is a fresh, good book on deep learning. Unfortunately I can't openly link to it, it's on rutracker.org.
Deep Learning.
Year of publication: 2018
Author: Nikolenko S. I., Kadurin A. A., Arkhangelskaya E. О.
Genre or topic: Neural networks
Publisher: Peter
Series: Library of Programmer
ISBN: 978-5-496-02536-2
Language: Russian
Format: PDF
Quality: Recognized text with errors (OCR)
Interactive table of contents: No
Number of pages: 479
Thank you. If you have a link, please send it to me. Not looking for it yet.
Classification defines a point in time where a trade is only statistically promising. Well, it's not a forecast. Rather, it is more like pattern recognition.
Once again: the combination of predictors says that there will be a long before the next clause. Naturally, the VARIETY of such an event is determined, but that probability is divided into two classes (with a binary teacher). You can go 50/50, you can go the other way.
This is not a prediction?
Thank you. If you have a link to throw in a private message, please. Not looking for it yet.
Search here.
For a fee. The mentioned book costs 10 rubles. So it is official.
Downloaded it for you, but I can not attach - too big file (18mb).
Search here.
For a fee. The mentioned book costs 10 rubles. So it is official.
I downloaded it for you, but I can not attach - too big file (18mb).
But download to Ya or G disc plz, also read
But upload to me or G-disk please, I'll read it too.
Seems to be here
I think it 's here.
Yes, thank you :) it was recommended to me too, by the way.
There is a fresh, good book on deep learning. Unfortunately, I can't give an open link, it was uploaded to rutracker.org.
In-Depth Learning.
Year of publication: 2018
Author: Nikolenko S. I., Kadurin A. A., Arkhangelskaya E. O.
Genre or theme: Neural networks
Publisher: Peter
Series: Programmer's Library
ISBN: 978-5-496-02536-2
Language: Russian
Format: PDF
Quality: Recognized text with errors (OCR)
Interactive table of contents: No
Number of pages: 479
I looked through it, read something diagonally.
Overall impression:
The book is not bad, with concrete examples in Python in each chapter. Just started doing Ruthon, and the choice of subject libraries is a problem. Of course, the choice is not limited to TensorFlow, but the actual Python code gives a lot.
Issues missing from other books are covered. Translated literature on the subject is clearly scarce right now. In particular, incomplete and convolutional nets. Something I have also recently started to deal with.
On the disadvantages, perhaps, a lot of general reasoning. I do not speak about a historical excursus starting from Wiener and Turing.
Perhaps the foreign (translated) books are better. Once again, this book is still very good from the beginning of time (from the 90's).
chapters 9,10 are fiery, q-learning and probabilsitic NN
just what you need... by the way, Heikin also has
chapters 9,10 are fiery, q-learning and probabilsitic NN
just what you need... by the way, Haikin also has