In this tutorial, recent guest on Data Driven, Micheleen Harris demonstrates common Python libraries for image manipulation are introduced and used in a Jupyter notebook for manipulating a sample image.
Code from the tutorial is on GitHub.
In this episode of Data Driven, Frank and Andy talk with Micheleen Harris, an R developer who loves Python. Yes, it is possible.
Press the play button below to listen here or visit the show page at DataDriven.tv
Here is a primer to introduce the concepts of deep learning with a specific focus on computer vision. It covers concepts including CNN’s (Convolutional Neural Networks), deep learning and transfer learning. It was created as an introduction for people getting started with machine learning and specifically deep learning to explain some of the commonly used terms and introduce some of the popular approaches to solving computer vision challenges.
In this episode of the AI Show, Micheleen Harris dives into when and why one would use deep learning over classical machine learning.
While many tasks can be performed cheaply and well with classical machine learning and packages like scikit-learn, every once in a while, a task is better suited for a neural network architecture implemented with deep learning methods – e.g. large amounts of data or insufficient accuracy with other methods. Watch to find out more and hear about some Python packages to make life easier.
In this episode of the AI Show, machine learning is gently introduced from the standpoint of the algorithm and model. It starts with the simplest machine learning, linear regression, and ends on a dense neural network explaining the similarities in plain terms along the way to bring the audience up to speed on neural networks in a fun way. Watch this episode to discover what machine learning algorithms really are in 10 minutes or less.