A lot of people ask me how to get started with deep learning. In this post I’ve listed a few resources I recommend for getting started. I’ve only chosen a few because I’ve found precise recommendations to be more helpful. Let me know if you have any comments or suggestions! Prelude: If you’re new to machine learning Deep learning is … Read More
AlphaGo Zero: An overview of the algorithm
In this post I go through the algorithms presented in the groundbreaking AlphaGo Zero paper using pseudocode. The objective is to provide a high-level idea of what the model does. Why AlphaGo Zero matters Last week, Google DeepMind published their final iteration of AlphaGo, AlphaGo Zero. To say its performance is remarkable is an understatement. AlphaGo Zero made two breakthroughs: … Read More
Explaining Tensorflow Code for a Convolutional Neural Network
In this post, we will go through the code for a convolutional neural network. We will use Aymeric Damien’s implementation. I recommend you have a skim before you read this post. I have included the key portions of the code below. If you’re not familiar with TensorFlow or neural networks, you may find it useful to read my post on multilayer … Read More
Code, Explained: Training a model in TensorFlow
In a previous post, we went through the TensorFlow code for a multilayer perceptron. Now we will discuss how we train the model with TensorFlow, specifically in a TensorFlow Session. We will use Aymeric Damien’s implementation in this post. I recommend you skim through the code first and have the code open in a separate window. I have included the key portions … Read More
Bugger! Detecting Lane Lines
This week I’ve been working on the first project of Udacity’s Self Driving Car Engineer Nanodegree – annotating lane lines in images and videos recorded from a front-facing camera mounted on the front of a car. There are plenty of blog posts floating around explaining the concepts (here’s my project code with a walkthrough of the process of annotating images), … Read More