In this post I’ll be working up, analyzing, visualizing, and doing Gradient Descent for Linear Regression. It’s a Jupyter notebook with all the code for plots and functions in Python available on my github account.
Machine Learning and Data Science: Linear Regression Part 3
In Part 3 of this series on Linear Regression I will go into more detail about the Model and Cost function. Including several graphs that will hopefully give insight into the their nature and serve as a reference for developing algorithms in the next post.
Machine Learning and Data Science: Linear Regression Part 2
In Part 2 of this series on Linear Regression I will pull a data-set of house sale prices and “features” from Kaggle and explore the data in a Jupyter notebook with pandas and seaborn. We will extract a good subset of data to use for our example analysis of the linear regression algorithms.
Machine Learning and Data Science: Linear Regression Part 1
Linear regression could possibly be considered the “Hello World” problem of Machine Learning. It’s implementation touches on many of the fundamental ideas and problems in this field. I’ll give you some guidance for understanding and implementation of this fundamental idea.
Machine Learning and Data Science: Introduction
This is the start of a series of posts on Machine Learning and Data Science. I’ll be exploring the algorithms and tools of Machine Learning and Data Science. It will be tutorials, guides, how-to, reviews and “real world” application. The post will be done using Juypter notebooks and the notebooks will be available on GitHub.
Docker and NVIDIA-docker on your workstation: Integration with your Desktop
I’ve been doing this series of posts about setting up Docker for your desktop system, so why not literally add containers to your desktop! The way we have Docker configured, containers are the same as other applications you run. In this post I’ll show you how to add icons and menu items to launch containers.
Docker and Nvidia-Docker on your workstation: Common Docker Commands Tutorial
Docker can be complex but for use on single-user-workstation you can get a lot done with just a few commands. This post will go through some commands to manage your images and containers. We will also go through the process of building a docker image for CUDA development that includes OpenGl support.
How to Install Anaconda Python and First Steps for Linux and Windows
A few weeks ago I wrote a blog post titled Should You Learn to Program with Python. If you read that and decided the answer is yes then this post is for you.
Docker and NVIDIA-docker on your workstation: Using Graphical Applications
You can use graphical application with Docker and NVIDIA-Docker by attaching your X-Window server socket to a container. And, it can be done in a relatively safe and secure way. I will take advantage of the Docker security and usability enhancements from the configuration with User-Namespaces that we setup in the previous post and show you how to run a CUDA application with OpenGL output support.
Docker and NVIDIA-docker on your workstation: Setup User Namespaces
In this third post on Docker and Nvidia-Docker we will configure “kernel user namespaces” for use with Docker. This will increase the security and usability of Docker on your desktop. This is a relatively new feature in Docker and is a key component for the viability of “Docker on your workstation”.
