Aart's Blog

I am deviating a bit from the originally planned order of reading books as well as from the topic of TensorFlow, but I picked up the next book "Python - Machine Learning" by Wei-Meng Lee anyway from the local bookstore, since it presents some basic technologies that are relevant to ML. As the introduction states, the book takes a gentle approach to the ML topic, but it provides an interesting read nevertheless. First some fundamental Python libraries are presented: NumPy (multi-dimensional arrays), Pandas (Panel Data Analysis), matplotlib/Searborn (data visualization), and Scikit-learn (ML algorithms for classification, regression, clustering, decision tree, etc.). The book provides resources for data sets, discusses data cleaning, and goes over several ML examples for supervised learning (one regression chapter and three classification chapters) and unsupervised learning (one clustering chapter). The book finishes with a presentation of the Azure Machine Learning Studio (...

A while back, I posted on my interest in Tensorflow , and planned a few brief follow-up impressions of some books I bought. This was soon followed by a first posting on  "Tensorflow for Deep Learning"  with a  follow-up on a CNN for a digit recognition example  from that book. After that, the distractions of life and work took over, but now I am ready to continue these postings. Since then, I even bought a few new books! I am really enthusiastic about the second book, "Learning TensorFlow" by Tom Hope, Yehezkel S. Resheff, and Itay Lieder. Unlike the first book, which was informative but a bit sparse on details, this book explains the details of constructing and running a TensorFlow computation graph really well. After the first three chapters, I was quite comfortable with the fundamental basic blocks of TensorFlow and experimenting with some different setups. What was particularly helpful was showing a toy graph first to explain underlying principles before m...