DynamoDB

I do not like to live of past memories but this morning I spent a few minutes updating the About me … section in this blog. I included the fact that since my early 20’s I started dabbling with starting my own businesses. At the time I got together with a couple friends and opened a liquor store. I was able to secure beer distribution of one of the largest breweries in Peru. The rules are that breweries sell only via distributors and distributors only sell to liquor stores. About a year after, I sold the business due to the fact that I received a scholarship to attend Cornell University and permanently move to the USA.

Continue reading “DynamoDB”

Logistic Regression with a Neural Network mindset

It is a very snowy day in the Twin Cities of Minneapolis and St. Paul. Schools are closed due to the amount of snow and low visibility. It started snowing earlier this morning and according to forecast, it should end around 09:00 PM this evening. We have already surpassed the snow amount for February according to records that go back over a century. We will be receiving more snow in the upcoming days. Will see if we set other new records.

In this post I will cover a logistic regression implementation used to determine if pictures contain a cat or not. The code is based on an edited assignment for Coursera Neural Networks and Deep Learning. Continue reading “Logistic Regression with a Neural Network mindset”

Broadcasting in Numpy

Broadcasting is a feature of Python and Numpy. When one is performing array operations, in some cases the shapes of the arguments do not match. The good and bad thing is that Python assumes what you want to happen and does it. In most cases the results are fine, but on occasions Python might do something that you are not expecting. This post discusses to some degree what is broadcasting. The idea is that we will be using it in a future post when doing some regressions for image recognition. Continue reading “Broadcasting in Numpy”

Numpy Vector Notes for Machine Learning

When learning and working with Python on machine learning it is important to make sure that Numpy arrays have the proper dimensions. Using improper dimensions may cause issues / bugs that are hard to track yet it is simple to prevent and we will see in this post. Continue reading “Numpy Vector Notes for Machine Learning”

Numpy Vectorization – Revisited

It is the last Sunday in January 2019 and is relatively cold in the Twin Cities of Minneapolis and St. Paul. The computerized mercury scale indicates -12F not taking into account wind. As usual, get up before 05:00 AM and get in my first 2 hour block of Deep Work. I am in the process of reviewing the last course I took on neural networks and deep learning. Continue reading “Numpy Vectorization – Revisited”

Numpy Vectorization

As you may already know, I have been taking several AI / ML related courses. I am a firm believer in always keep on learning. Some time ago I read a report about people in the USA reading books. The statistic that called my attention was:  42% of college graduates never read another book after college. That seems to me quite disturbing. Another statistic is:  57% of new books are not read to completion. To this indicates that a) readers are not committed to learning and / or books are getting worse. Continue reading “Numpy Vectorization”

BeautifulSoup

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”

Simple Problems in Python

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”

Crash Course in Python – Part II

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”

Crash Course in Python – Part I

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”