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Python

README.md

Repository used for tracking my journey in learning Python programming language

Progress

27.04.2023

02.05.2022

  • languageSummary file created with a summary of Python syntax.
  • fixed markdown lint warnings.
  • I found Neetcode 150, a list of algorithmic coding problems based on the famous Blind 75 list. I will try to use as programming language Python to have some practice and go over as many of the 150 problems from the last as possible.
  • First chapter I started is Arrays & Hashing

07.04.2021

  • Learned about web scrapping with Selenium and a webdriver for chrome. This can be done in a headless environment, like the Ubuntu server where I do most of my development but best is to run it in a desktop environment.

13.03.2021

  • First concept in chapter 4 is about mean squared error. It's a good thing to square an error, because it makes small errors smaller and big errors bigger.
  • page 54

07.03.2021

  • The book makes reference to NumPy. I started to read the User Guide available on the website to try and get a quick overview of what the library offers.
  • The chapter starts to become difficult to follow. I decided to go trough it at a faster rate without focusing so much on taking detailed notes. Hopefully I will be able to begin building a mental image of neural networks then come back to certain chapters for details.
  • Chapter 3 which goes into detail about forward propagation is done.
  • I need to go over a few examples of NumPy dot function. I found an easy to understand explanation of dot function on matrices here which is worth to bookmark for the future.
  • page 48(started chapter 4)

06.03.2021

  • Learned about weighted sum network
  • The interface for a neural network is simple. It accepts as inputs a list of information and a weights as knowledge. It outputs a prediction based on the inpus.
  • Page 34

17.02.2021

  • The neural network outputs a prediction based on the input and the weight.
  • Th network can give false predictions then it should try to adjust the weights in order to predict more accurately the next time it sees the same input
  • Implementing a simple neural network with a single node
  • Network sensitivity is related to the weight. If the weight is very high then even the tiniest input can create a large variation in prediction.
  • page 27

16.02.2021

  • Created support for solving challenged from Hackarrank.com in python. No solutions yet added to the repo
  • I found an interesting series on bdtechtalks called AI education. First article in the series contains a promising approach in self studying Deep Learning using Andrew Trusk Grokking Deep Learning book. The article mentions that this book doesn't need a strong mathematical background in linear algebra and calculus to start learning this field. I decided to give the book a try and see how long before I loose interest in it.
  • So far the book promises to provide an easy approach in teaching what deep learning is which will prepare me for learning one of the existing frameworks(Torch, TensorFlow, Keras and others)
  • 90% of the projects I start, something doesn't work from the first step. In this case, I get a "The folder you are executing pip from can no longer be found." when trying to install numpy with pip. I guess I'm going to start debugging this and by the time I'm done with it I will need a break from the mental exhaustion :(
  • That was easy :) Apparently I run the commands from a directory that I deleted and that's why I was getting the error above. Next step is to figure out if I need Jupyter Notebook or I can code on my code-server instance in plain Python without additional support.
  • It takes too much time to understand the benefits of Jupyter Notebook so I decided to not use it for now. Hopefully I won't regret it later on. Finally I can get back to the book.
  • Deep learning is a subset of methods in machine learning toolbox, primarily using artificial neural networks inspired by the human brain
  • Supervised vs unsupervised learning -> this is about the type of pattern being learned
  • Supervised learning is the direct imitation of a pattern between two datasets. What you know -> supervised learning -> what you want to know
  • Unsupervised learning transforms one data set into another, which is not previously known or understood
  • Parametric vs nonparametric -> this is about the method for learning
  • Parametric learning is characterized by having a fixed number of parameters
  • Nonparametric model is characterized by having an infinite number of parameters, determined by data
  • Page 44

13.10.2020

  • Reworked the existing python files because they were not compiling with with python 3
  • Practiced a bit Markdown syntax by reworking the README.md file in the repository in order to keep a status of activities done. This way it's easier to remember where I left in case I need to take a break and resume this activity later on. Found a great reference online for quickly checking different syntax elements
  • Started going trough Automate the Boring Stuff with Python chapter 8

14.10.2020

19.10.2020

  • Found a new repo on GitHub name Practical Python which seems to be worth going trough since it offers a more compact organization of information. Decided to set on hold the current course and continue with this one.
  • Organized the files in the repo based on the different course or library that I study. So far all the scripts are split into 3 categories: