Okay, best technical book read this year award still three months to go though goes to this book Initially it feels a bit odd that the focus is first put on scikit learn and that tensorflow seems to be added as an afterthought but in the end it s really a right tool for the job approach Don t use this book to learn scikit learn or tensorflow use this book to get your face wet in deep learning where the two libraries are just a tool.
great introduction into machine learning for both developer and non developers authors suggests to just go through even if you don t understand math details main points are extraction of field expert knowledge is very important you should know which model will serve better for the given solution luckily lot of models are available already from other scientists training data is the most important part theyou have it the better so if you can you should accumulate as much data as you can, preferably categorized you may not still know how you will apply the accumulated data in the future but you will need it labeling training data is very important too to train neural network you need to have at least thousands of labeled data samples thethe better Machine learning algorithms and neural networks are pretty common for years but latest b Series Of Deep Learning Breakthroughs Have Boosted The Whole Field Of Machine Learning Over The Last Decade Now That Machine Learning Is Thriving, Even Programmers Who Know Close To Nothing About This Technology Can Use Simple, Efficient Tools To Implement Programs Capable Of Learning From Data This Practical Book Shows You HowBy Using Concrete Examples, Minimal Theory, And Two Production Ready Python Frameworks Scikit Learn And TensorFlow Author Aur Lien G Ron Helps You Gain [Aurélien Géron] Ú Hands-On Machine Learning with Scikit-Learn and TensorFlow [trivia PDF] Ebook Epub Download ✓ An Intuitive Understanding Of The Concepts And Tools For Building Intelligent Systems You Ll Learn How To Use A Range Of Techniques, Starting With Simple Linear Regression And Progressing To Deep Neural Networks If You Have Some Programming Experience And You Re Ready To Code A Machine Learning Project, This Guide Is For YouThis Hands On Book Shows You How To Use Scikit Learn, An Accessible Framework That Implements Many Algorithms Efficiently And Serves As A Great Machine Learning Entry PointTensorFlow, A Complex Library For Distributed Numerical Computation, Ideal For Training And Running Very Large Neural NetworksPractical Code Examples That You Can Apply Without Learning Excessive Machine Learning Theory Or Algorithm Details A very excellent introduction to many machine learning algorithms beginning at the very beginning and ending much further than I expected I can t wait for the updated edition to reference because, yes, many tensorflow functions changed name.
5 for the first half of the book, scikit learn 3 for the second half, Tensor Flow Nice examples with Jupyter notebooks Good mix of practical with theoretical The scikit learn section is a great reference, nice detailed explanation with good references for further reading to deepen your knowledge The tensor flow part is weaker as examples becomecomplex Chollet s book Deep Learning with Python, which uses Keras is much stronger, as the examples are easier to understand as Keras is a simple layer over tensor flow to ease the use Also Chollet explains the concepts better and nicely annotates his code.
Buy this book for scikit learn and overall best practise for machine learning and data science Buy Chollet s Deep Learning using Python for practical deep learning itself.
Overall still a practical book with Jupyter Notebook supplementary This is the best book I ve read on machine learning It is well written and the examples are very good with real data sets.
The first half is an introduction to machine learning and the second half explores deep learning It is a great book to read along an online course.
Ù Hands-On Machine Learning with Scikit-Learn and TensorFlow ñ , ,
Nicely well explained from scratch to advanced The book contains a chapter that shows a basic flow for working with data problems The TF chapters are interesting but somehow short I would have likedon convolutional layers and RNN.
The reinforcement learning chapter is very interesting.
My favorite part is the reinforce learning in the last chapter The chapter makes sense, is easy to understand, and its sample is very practical.