Effective Deep Learning Resources: A Shortlist

Jessica YungArtificial Intelligence, Data Science, Education, Machine Learning, Studying

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

Jessica YungArtificial Intelligence, Highlights

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

Code, Explained: Training a model in TensorFlow

Jessica YungArtificial Intelligence, Self-Driving Car ND

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

Questions to ask when deciding how to approach predictive problems

Jessica YungData Science

Is the situation stochastic or deterministic? Is it time-inhomogeneous? (Different across time?) How much data do you have available? What limitations are there with respect to computational cost (compute and time), both for training and predicting? Do you need to try actions to learn about situations? (If so, consider Reinforcement Learning.) Do your actions have an impact on the environment? … Read More