The past weekend was kind of cold in the Twin Cities area of Minneapolis and St. Paul. Seems like winter a somewhat ahead of time.
In the past few months I have been spending time learning and experimenting with machine learning (ML) and Big Data. Machine learning seems to require a lot of properly cleaned samples. This is one more case when garbage in implies garbage out. That said; the first step is to collect data. Data can come from different sources i.e., databases, files, public repositories, the Internet, etc. Data can be collected from the Internet in different ways. In general one can collect data from the internet using two main approaches: web scraping and via an API. I will cover both of these approaches in the following posts. Continue reading “BeautifulSoup”
Last week I was reading a post on Medium “First Steps in Data Science with Python NumPy” by Kshitij Bajracharya.
What called my attention is his opening statement “I’ve read that the best way to learn something is to blog about it”. I believe Kshitij hit it right on. The reason I agree is that I have been a believer in “If you can’t explain it simply, you don’t understand it well enough”. This quote is attributed to Albert Einstein. Continue reading “Simple Problems in Python”
It is Friday. Many stressful things are happening in different fronts in the past few months. Most of the things are out of my control. It seems like I need to find a way to relax. Hopefully things will turn out well. I fully understand that stress is an internal thing and I am quite good at controlling it. I used to say that stress is the salt of life; but too much salt is not good for you. Continue reading “Crash Course in Python – Part II”
As I have mentioned in previous posts, I like to purchase and read computer related technical books. When I receive the book I write my name and year on the first page. I then locate the date for the last revision and circle it. In 2017 I purchased “Data Science from Scratch” by Joel Grus. I read the first five chapters that I was interested it at the time and moved on to the next book. Continue reading “Crash Course in Python – Part I”
I am constantly reading and experimenting to learn things which I may apply to work projects. A few years ago I decided to spent time on and off learning machine learning (ML). With that purpose in mind, I got a number of books on different subjects (e.g., Deep Learning, Python and Statistics) which seemed to be useful to achieve my goal.
On the platform side, I started experimenting on Windows. Most things work fine but some things do not. For example, I had to wait to use Tensorflow because it ran on Linux but not on Windows. Today it seems to work on both. The same seems to hold true for Docker. Continue reading “Machine Learning – Setup”
As you might already know, TensorFlow(tm) could not run natively on Windows. At the time you had to run it on a VM or a Docker container. Earlier this month Google released a native version for Windows. This morning I woke up around 04:00 AM and decided to install it on my computer to have it available over the weekend. The installation is quite simple and it works as I will show.
Initially I ran into problems due to different versions of Anaconda and Python on my machine. The instructions from Google call for Python 3.5 (or higher). I had installed different versions (i.e., Python 2.7) plus Python Tools 2.2 for Visual Studio 2013. I decided to remove programs in order, leaving Python for VS towards the end. I really wanted to avoid removing such package. Continue reading “TensorFlow(tm) on Windows”