I describe recurrent neural networks, a deep learning technique geared for tasks such as natural language processing.
We discuss convolutional neural networks, a deep learning model inspired by the human visual system that has rocked the state-of-the-art in computer vision tasks.
We investigate a motley of interesting properties and goodies of neural networks.
We begin a deep dive into deep learning by investigating feedforward neural networks.
We discuss support vector machines, a very powerful and versatile machine learning model.
I describe Naive Bayes, a commonly-used generative model for a variety of classification tasks.
I describe logistic-regression, one of the cornerstone algorithms of the modern-day machine learning toolkit.
I describe the basics of linear regression, one of the most common and widely used machine learning techniques.
I dive into the details of Fast R-CNN, an extension to the original R-CNN model that boasted 9x speedup over its predecessor as well as state-of-the-art object detection results.
A discussion of R-CNN, a historic object detection architecture combining region proposals with convolutional neural networks.
A discussion of fundamental deep learning algorithms people new to the field should learn along with a recommended course of study.
Given all the recent buzz around artificial intelligence, I discuss three reasons for why we are seeing such widespread interest in the field today.
A discussion of the most important skills necessary for being an effective machine learning engineer or data scientist.