Anatomy of Deep Learning Principles
Anatomy of Deep Learning Principles
Writing a Deep Learning Library from Scratch
About the Book
This book introduces the basic principles and implementation process of deep learning in a simple way, and uses python's numpy library to build its own deep learning library from scratch instead of using existing deep learning libraries. On the basis of introducing basic knowledge of Python programming, calculus, and probability statistics, the core basic knowledge of deep learning such as regression model, neural network, convolutional neural network, recurrent neural network, and generative network is introduced in sequence according to the development of deep learning. While analyzing the principle in a simple way, it provides a detailed code implementation process. It is like not teaching you how to use weapons and mobile phones, but teaching you how to make weapons and mobile phones by yourself. This book is not a tutorial on the use of existing deep learning libraries, but an analysis of how to develop deep learning libraries from 0. This method of combining the principle from 0 with code implementation can enable readers to better understand the basic principles of deep learning and the design ideas of popular deep learning libraries.
- 1. array()
- 2. Multidimensional array type ndarray
- 3. asarray()
- 4. The tolist() method of ndarray
- 5. astype() and reshape()
- 6. arange() and linspace()
- 7. full(), empty(), zeros(), ones(), eye()
- 8. Common functions for creating tensors of random values
- 9. Add, Repeat & Lay, Merge & Split, Edge Fill, Add Axis & Swap Axis
- Repeat repeat()
- laying tile()
- merge concatenate()
- overlay stack()
- column_stack(), hstack(), vstack()
- split split()
- Edge Padding
- Add Axis
- Swap axes
- 7.9.1 Implementing Recurrent Neural Networks with Classes
- 7.9.2 Class implementation of recurrent neural network unit
- 7.10 Multilayer, Bidirectional Recurrent Neural Network
- 7.10.1 Multilayer Recurrent Neural Network
- 7.10.2 Training and prediction of multi-layer recurrent neural network
- 7.10.3 Bidirectional Recurrent Neural Network
The Leanpub 60 Day 100% Happiness Guarantee
Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
Now, this is technically risky for us, since you'll have the book or course files either way. But we're so confident in our products and services, and in our authors and readers, that we're happy to offer a full money back guarantee for everything we sell.
You can only find out how good something is by trying it, and because of our 100% money back guarantee there's literally no risk to do so!
So, there's no reason not to click the Add to Cart button, is there?
See full terms...
80% Royalties. Earn $16 on a $20 book.
We pay 80% royalties. That's not a typo: you earn $16 on a $20 sale. If we sell 5000 non-refunded copies of your book or course for $20, you'll earn $80,000.
(Yes, some authors have already earned much more than that on Leanpub.)
In fact, authors have earnedover $12 millionwriting, publishing and selling on Leanpub.
Learn more about writing on Leanpub
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.