In this first episode of Cloud AI Adventures, Yufeng Guo explains machine learning from the ground up with concrete examples.
Time series is the fastest growing category of data out there! It’s a series of data points indexed in time order.
Often, a time series is a sequence taken at successive equally spaced points in time. In this video, Siraj Raval covers 8 different time series techniques that will help us predict the price of gold over a period of 3 years.
Code for this video https://github.com/llSourcell/Time_Series_Prediction
What is Windows Machine Learning and why should you care? In this video, Killian and Rosane walk through these questions and clarify the positioning and capabilities of this powerful AI inference engine.
Siraj Raval breaks down 7 ways that anyone can earn money from anywhere in the world using machine learning. We”ll start by taking a look at whats called the “AI Value Chain” to learn who is currently making money in machine learning so that we can better chart out where we can contribute to the space. From startups, to competitions, to writing books, he covers a lot of material in this one video.
Operationalizing AI and ML models is a common pain point I hear from my customers. In this episode of the AI Show, learn how to deploy machine learning models using the Visual Studio Code Tools for AI extension and Azure Machine Learning service.
Another great video from DeepLearning.TV.
Interpreting what neural networks are doing is a tricky problem. In fact, they are often referred to as a “black box.”
In this video Arxiv dives into the approach of feature visualization. From simple neuron excitation to the Deep Visualization Toolbox and the Google DeepDream project.
Watch to open up that black box!
In this live stream, Siraj Raval attempes to beat a Kaggle Challenge — the $100,000 “TGS Salt Identification Challenge” using a combination of Google Colab, Conditional Random Fields, and neural networks! Expect some colorful exploratory data analysis, then model building and some Q&A.
Here’s a fascinating look at the history and evolution of machine learning
[found via LinkedIn]