4/22 - Class 8

Class notes:

Recurrent Neural Networks - RNN

What is a RNN?

Vanilla neural networks and convolutional neural networks (what we have studied so far) accept a fixed sized vector as input (such as an image) and produce fixed sized vector outputs (such as probabilities of different classes). Recurrent nets allow us to operate over sequences of vectors - sequences in the input, the output, or generally both. This makes them great for applications such as text translation, text generation, image captioning, and sentiment analysis. Andrej Karpathy’s post The Unreasonable Effectiveness of Recurrent Neural Networks is a great resource to learn more about how they work.

Examples of RNN in action:


4/15 - Class 7

Class notes:

“t-SNE is a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.”

t-SNE allows for maintaining the neighbor relationships between points, hence the full name “T-distributed Stochastic Neighbor Embedding”. It can reduce large dimension datasets to any smaller size, but primarily is used for reductions to 2D and 3D for visualization.

Examples of t-SNE in action:

More on t-SNE:

4/8 - Class 6

Midterm presentations

4/1 - Class 5

A guest presentation from Rebecca Ricks - a technologist, writer, an artist thinking about privacy and computational systems. Rebecca was a Ford-Mozilla open web fellow at Human Rights Watch, an organization that investigates and reports on abuses happening in all corners of the world and is now a researcher at the Mozilla Foundation. Her work interrogates the ways social platforms collect and monetize data about their users. Rebecca received her masters at NYU’s ITP program and holds a B.A. in Middle East Studies/Arabic.

3/18 - Class 4

Class notes:

Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object. This usually means detecting keypoint locations that describe the object. (Source)

Some examples:

Pose estimation can be done for single or multiple people.

We can track face position, body position, and even use KNN classification to classify body positions - code. More reading:

3/11 - Class 3

Class notes:

Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned. (Source)

What is feature extraction?

Retraining a network

Linear regression


Examples using transfer learning:

Additional material:

2/25 - Class 2

Class notes:

Additional material:

2/18 - Class 1

Class notes:

Use cases for Machine Learning

Additional relevant resources: