In this blog post we examine the growing technology of weakly supervised learning, in the context of other machine/deep learning techniques, and discuss some of the potential applications and frameworks that make use of them.
In this blog post we take an in depth look at the Transformer model architecture, and demo its functionality by rebuilding the model from scratch in Python.
In this blog post we explore the differences between deed-forward and feedback neural networks, look at CNNs and RNNs, examine popular examples of Neural Network architectures, and their use cases.
Autonomous vehicles are on of the most exciting, up-and-coming applications of deep learning to hit the public. In this guide, you will learn about the theory behind these vehicles and the relevant ML tools leveraged to make them work.
In the first part of this new series we'll explore basics of audio analysis and signal processing and we'll learn to apply basic machine learning techniques to audio.
This blogpost is an in-depth discussion of the Google Brain paper titled "Searching for activation functions" which has since revived research into activation functions.
