Lesson 9: Next Steps

Table of Contents Introduction Natural Language Processing (NLP) Reinforcement Learning (RL) Deep Unsupervised Learning, Generative Models Decision Trees, Random Forests, Gradient Boosting Social Network Analysis Introduction If you’ve gone through our previous lessons while retaining most of the information presented,...

Lesson 8 Supplemental Material

Word2Vec A common task for RNNs is to work with word sequences. One option for doing this is to simply encode the words in our sequence using a one-hot encoding, which is a representation of words as binary vectors. Each...

Lesson 8: Recurrent Neural Networks

RNN Introduction Previously, you learned how convolutional neural networks can be trained to learn certain spatial features and representations from raw data such as pixel values. However, what if we had a problem that involves temporal or sequential data, such...

Lesson 7 Supplemental Material

A Formal Definition of Convolution To gain a more in depth understanding of what convolution is let's take a look at it through a more rigorous definition. Convolution is a mathematical operation that has roots in signal processing. Say we...

Lesson 7: Convolutional Neural Networks

Introduction Before reading this lesson I would highly recommend first checking out the lesson on regular neural networks. The topics in this lesson will build off of that lesson. At this point, we have learned how artificial neural networks can...