Machine Learning3221 Videos
11-785, Fall 22 Lecture 20: Learning Representations
This is going to be the first of a few lectures that will be on Zoom. Today we'll be talking about what neural networks learn. We'll talk about how neural networks are universal approximators.
Fastest way to initialize a List in Python? (comparison of 3 options)
Three ways to create a Python list with a specific value. One is to create an empty list, then use a for loop and append the items. Option number two is list comprehension, where we create the list right away in one line. And option number three is to multiply the number of times we want this.
Faster dataset annotation and model evaluation with Roboflow – Made with TensorFlow.js
Brad is working on a product that empowers developers to build their own computer vision applications, no matter what their skill set or experience may be. Brad: "If I was to describe myself in one word, it's a hacker".
11-785, Fall 22 Lecture 23: Generative Adversarial Networks (Part 1)
The first lecture on GANs was the first lecture of the semester on Generative models. We have seen discriminator models which Model the conditional distribution. Discriminative models find and it aims to find a decision boundary which separates this data from this set of data. So in in in generator models your aim is to just find the distribution of the data and not just to find the boundary.
11-785, Fall 22 Lecture 21: Variational Auto Encoders (redo)
Now we're speaking of how to use neural networks as generative models to model the distribution of any data so that we can draw samples from it.
11-785, Fall 22 Lecture 20: Representation Learning
Today we're going to be talking about what neural networks learn. So what we've seen so far is this neural networks are universal approximators. They can model any Boolean category color real value function.
11-785, Fall 22 Lecture 21: Variational Auto Encoders
We're going to start our new sequence of lectures on neural networks for modeling distributions. So what we've seen so far is that neural networks are universal approximators. They can model Boolean functions, classification functions.
11-785 Deep Learning Recitation 11: Transformers Part 1
This is the first part of the transformers recitation where we will quote from scratch. This one will be more about the basics of transformers and how we coded so that for the future recitations we can just go over what researchers in the community have done and what the architectures are.
PyTorch Community Voices: Interview with Refik Anadol Studio (Full Video)
Rafik and Christian from the Rafik & Adoles studio have joined us to discuss PyTorch. Suraj, Justin and Suraj are both developers and developers at Pytorch.
