Paperback: 278 pages
Publisher: Packt Publishing - ebooks Account (July 28, 2016)
Product Dimensions: 7.5 x 0.6 x 9.2 inches
Shipping Weight: 1.1 pounds (View shipping rates and policies)
Average Customer Review: 4.0 out of 5 stars See all reviews (1 customer review)
Best Sellers Rank: #100,823 in Books (See Top 100 in Books) #54 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design #59 in Books > Computers & Technology > Databases & Big Data > Data Processing #104 in Books > Computers & Technology > Programming > Languages & Tools > Python
A few word about myself:I am a Analyst, I have a MSc. in Mathematics and Statistics and do analytics for a living. While I have studied about neural networks and machine learning a while ago, only past year have I (re)-discovered the power of neural nets and Deep Learning.In my quest to improve my knowledge, I have taken many certificates in ML and have bought a few books about Machine Learning. Among these are:-Python Machine Learning by Sebastian Raschka (recommended)-Building Machine Learning Systems with Python by by Luis Pedro Coelho and Willi Richert (nice to have for additional perspective)However, I wanted to go beyond what one can find in those two books. The topics I was specifically interested in were:-Deep Belief Networks (inc. Restricted Boltzmann Machine)-Autoencoders-Convolutional Neural NetworksSo where does Advanced Machine Learning rank among these?I must say, and that will be my main criticism of the book that it is not for the faint of heart. It is fast, sometimes too fast... I suppose there is so much you can put in 250 pages to explain about these topics, and it is easy to become lost.However, do not get me wrong. This book is a small gem in itself.Why? Because while I have found online many tutorials or courses about the topics I was interested, the book gives you additional information and explanations that I haven't found anywhere else. How do you set your hyper-parameters in a CNN? What is the depth exactly representing, what are the current architectures, are they really all that good? Why?It is the difference between the how and the more precise what and why. Tutorials online are great but many people just do things without clearly showing why.
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