Python Machine Learning: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics 1st Edition, Kindle Edition
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About the Author
Sebastian Raschka is a PhD student at Michigan State University, who develops new computational methods in the field of computational biology. He has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning. Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background. He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle. In his free time, he works on models for sports predictions, and if he is not in front of the computer, he enjoys playing sports.
- ASIN : B00YSILNL0
- Publisher : Packt Publishing; 1st edition (September 23, 2015)
- Publication date : September 23, 2015
- Language : English
- File size : 18652 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Print length : 456 pages
- Lending : Not Enabled
- Best Sellers Rank: #653,615 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
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Top reviews from the United States
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Let’s face it, we know that machine learning isn’t an easy subject. You need theory…but you also need practice in the form of some serious coding before you really start understanding it. And this is one area where Sebastian’s book shines: it contains a plethora of really good code examples that are illuminating and well explained, and which cover a very wide range of different machine learning algorithms. And, speaking of code, as another reviewer has pointed out, another huge plus is that, in many places, Sebastian shows you how to gauge the performance of your code and make it more efficient.
For me, the best measure of any book such as this is how many “ah ha!” moments I had while reading it. And I had more than a few while reading Sebastian’s book. One such “ah ha!” moment came while reading chapter 12 (and this also illustrates that nice blend of theory and practice I already mentioned above). In this particular chapter, he discusses training artificial neural networks for image recognition. At the heart of this approach is back propagation, which is pretty much THE bread and butter behind multilayered neural networks. He presents a detailed discussion of back propagation in two separate pieces: one that is intuitive and “top down”; the other a more mathematical, “bottoms up” approach that goes through the algorithm step by step, showing how the gradients are computed and the weights updated. His treatment of back propagation was one of the better explanations I’ve seen and really cleared things up for me.
One last thing I must mention: at the time of release, this was the first machine learning book for Python (to my knowledge) that has an entire chapter devoted to Theano, which he uses to parallelize neural network training. For those who don’t know, Theano is a particularly nice (not to mention very powerful) Python library for doing machine learning, most especially if you can utilize the power of GPU computing. In addition, that particular chapter (13) also introduces the brand new Python library named Keras, which is built on top of Theano and is a really nice library for the rapid building and prototyping of neural networks (in the spirit of Torch). Being a brand new library, his treatment of Keras was necessarily brief, but it was a great starting point.
In conclusion, I am very confident that if you do pick up this book, you won’t be at all disappointed. And be sure and grab the accompanying code for the book on his GitHub repository (just look for “python-machine-learning-book” on github.com/rasbt.) His code is top notch and I’ve yet to encounter any problems with it.
Update: Having finished the book now, I can definitely reaffirm my original position. This is one of the best technical books I have ever read. The last few chapters especially, image recognition with MLP networks and parallelizing networks with Theano and Keras are extremely interesting. I have taken these ideas and applied them in several of my own projects now. Also, as I'm planning on going to graduate school in the very near future, I'm thinking that machine learning and ANNs will likely be at the top of my list of areas to specialize in. The research that is going on in this field is huge, and this book manages to touch at the very base of neural networks, but enough to get your feet wet and show you where to go from there.
Top reviews from other countries
L'immense intérêt de ce livre, c'est qu'en exemple est entièrement codé de A-Z, avec toutes les étapes de test pour vérifier que les algorithmes sont bien ajustés. Le code est même fourni sur internet, merci bien car l'objectif n'est pas d'écrire du code mais de comprendre ce qu'il fait précisément.
On peut facilement remplacer l'exemple du livre tel quel et le transposer facilement à l'exemple qui nous intéresse sans y passer des mois à chercher comment on code ce genre d'application, comment on affiche les données, comment on contrôle que ça marche effectivement etc.
Très bonne acquisition pour un débutant en machine learning (et débutant ayant pratiqué un peu de python par la même occasion).