Crash Course: Artificial Intelligence

Reinforcement learning is useful in situations where we want to train AIs to have certain skills we don’t fully understand. We’re going to explore these ideas, introduce a ton of new terms like value, policy, agent, environment, actions, and states and we’ll show you how we can use strategies like exploration and exploitation to train John Green Bot to find things more efficiently next time.

We're going to code a program that takes a one word prompt and then completes the sentence that sounds like something John Green would say. We’re going to collect transcription files from Vlogbrothers episodes, do some preprocessing, then set up a recurrent neural network (RNN), train our model, and test it!

Follow along: https://colab.research.google.com/drive/1f8ik5kSPEvDCcM7R_-Wb3AjifizVEs…

We’re moving on from artificial intelligence that needs training labels, called Supervised Learning, to Unsupervised Learning which is learning by finding patterns in the world. We’ll focus on the performing unsupervised clustering, specifically K-means clustering, and show you how we can extract meaningful patterns from data even when you don't know where those patterns are.

John Green Bot wrote his first novel! We’re going to program a neural network to recognize handwritten letters to convert the first part of John Green Bot’s novel into typed text.

Follow along: https://colab.research.google.com/drive/1NyYH1EPpaJlMBLK0fcKYz4icaD1SNS…

We’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks by finding the best combinations of weights to minimize error.

Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning.

Supervised learning is the process of learning WITH training labels, and is the most widely used kind of learning with it comes to AI - helping with stuff like tagging photos on Facebook and filtering spam from your email. We’re going to start small today and show how just a single neuron (or perceptron) is constructed, and explain the differences between precision and recall.

Artificial intelligence is everywhere and it's already making a huge impact on our lives. It's autocompleting texts on our cellphones, telling us which videos to watch on YouTube, beating us at video games, recognizing us in photos, ordering products in stores, driving cars, scheduling appointments, you get the idea.