## Posts

### Neural Nets - Part 1: Perceptrons

Over the past decade or so, neural networks have shown amazing success across a wide variety of tasks. In this post, I'll introduce the grandfather of modern neural networks: the perceptron.### Language Modeling - Part 2: Embeddings

Moving beyond n-grams, embeddings let us better represent the meaning of words and quantify their relationships to other words.### Language Modeling - Part 1: n-gram Models

We'll start our language modeling journey starting at classical language modeling using n-gram language models.### Lie Groups - Part 2

I'll continue the discussion of Lie Groups into the realm of calculus on Lie Groups; we'll finish applying them to the robotic state estimation.### Lie Groups - Part 1

I'll introduce concept of Lie Groups and how they can be useful for working with constrained surfaces like rotations; we'll also apply them to the problem of accurate robotic state estimation.### Manifolds - Part 3

In the last part, I'll show how we can define curvature on a manifold by extending calculus to work on manifolds with the covariant derivative!### Manifolds - Part 2

In the second part, I'll construct a manifold from scratch and redefine vectors, dual vectors, and tensors on a manifold.### Manifolds - Part 1

As the first in a multi-part series, I'll introduce manifolds and discuss how vectors, dual vectors, and tensors work in a flat, Euclidean space.### Particle Filters for Robotic State Estimation

Going beyond EKFs, I'll motivate particle filters as a more advanced state estimator that can compensate for the limitations of EKFs.### Extended Kalman Filtering for Robotic State Estimation

I discuss a fundamental building block for state estimation for a robot: the extended kalman filter (EKF).### Deep Reinforcement Learning: Policy-based Methods

I discuss state-of-the-art deep RL techniques that use policy-based methods.### Deep Reinforcement Learning: Value-based Methods

I overview some of the fundamental deep reinforcement learning algorithms used as the basis for many of the more advanced techniques used in practice and research.### Reinforcement Learning

I describe the fundamental algorithms and techniques used in reinforcement learning.### Undergraduate Research

I chronicle some lessons learned from my 2.5 years as an undergrad student researcher.### Sequence-to-Sequence Models

I discuss the magic behind attention-based sequence-to-sequence models, the very same models used in tasks such as machine translation.### Undergraduate Tips and Tricks

After finishing finish my undergrad, I'll share some pointers (pun absolutely intended) that I've learned over the past few years.### Restricted Boltzmann Machines

I'll explain one of the more difficult unsupervised neural networks in detail using examples and intuition.### Understanding Backpropagation

I'll discuss the backpropagation algorithm on various levels using concrete examples.### Depth Perception: The Next Big Thing in Computer Vision

Devices like the HoloLens and Tango have depth-sensing capabilities, allowing for a completely different level of augmented reality.