The Emperor Has No Clothes: How to Code Claude Code in 200 Lines of Code
The core of tools like Claude Code, Cursor, and Warp isn't magic. It's about 200 lines of straightforward Python. Let's build one from scratch.
I Built an AI Hitch
I built an AI hitch called Rizzotto, a dating copilot that I've been using to have better dates with my girlfriend.
How Alexa Dropped the Ball on Being the Top Conversational System on the Planet
I discuss why Alexa missed the opportunity to take the lead and become the dominant player in the conversational AI market.
A Software Engineer Does 100 Standup Comedy Open Mics
I describe my adventures as a software engineer going into standup comedy and what I've learned after 100 standup open mics.
A Complete Introduction to Prompt Engineering For Large Language Models
I provide a comprehensive review of the most interesting research, techniques, and use-cases in prompt engineering as applied to large language models.
Honey, I Sold My First Bootstrapped SaaS Company
I discuss the recent acquisition of Confetti AI, an education company I bootstrapped, and my lessons going through this process.
How to Finetune GPT3
I discuss how to finetune GPT3, a state-of-the-art large language model that is revolutionizing natural language processing and understanding systems.
MLOps Is a Mess But That's to be Expected
I discuss the messy state of MLOps today and how we are still in the early phases of a broader transformation to bring machine learning value to enterprises globally.
Overview of the United States Financial Regulatory Ecosystem
I describe the motivation, organization, and main agencies of the US financial regulatory system, including their history and what their various responsibilities are.
What is the Data Science Life Cycle?
I describe the data science life cycle, a methodology for effectively developing and deploying data-driven projects.
A Complete Machine Learning Project From Scratch: Model Deployment and Continuous Integration
In this fifth post in a series on how to build a complete machine learning product from scratch, I describe how to deploy our model and set up a continuous integration system.
A Complete Machine Learning Project From Scratch: Error Analysis And Model V2
In this fourth post in a series on how to build a complete machine learning product from scratch, I describe how to error analyze our first model and work toward building a V2 model.
A Complete Machine Learning Project From Scratch: Model V1
In this third post in a series on how to build a complete machine learning product from scratch, I describe how to build an initial model with an associated training/evaluation pipeline and functionality tests.
A Complete Machine Learning Project From Scratch: Exploratory Data Analysis
In this second post in a series on how to build a complete machine learning product from scratch, I describe how to acquire your dataset and perform initial exploratory data analysis.
A Complete Machine Learning Project From Scratch: Setting Up
In this first post in a series on how to build a complete machine learning product from scratch, I describe how to setup your project and tooling.
We Don't Need Data Scientists, We Need Data Engineers
After analyzing 1000+ Y-Combinator Companies, I discover there's a huge market need for more engineering-focused data practitioner roles.
Deploying a State-of-the-Art Question Answering System With 60 Lines of Python Using HuggingFace and Streamlit
I show how you can deploy a performant question-answering system with just 60 lines of Python.
A Complete 4-Year Course Plan for an Artificial Intelligence Undergraduate Degree
I describe in-depth the courses necessary for a 4-year undergraduate degree in artificial intelligence, assuming you step onto campus tomorrow.
Trends in Machine Learning: NeurIPS 2019 In Review
Following an eventful week at NeurIPS 2019, I summarize some key trends in machine learning today.
The Birth of Venus: Building a Deep Learning Computer From Scratch
In which I describe how I built a deep learning computer starting from nothing but a pile of hardware components and a dream.
A Comprehensive Introduction to Machine Learning Fundamentals
I provide an aggregated and comprehensive list of tutorials I have made for fundamental concepts in machine learning.
Market Basket Analysis
I discuss market basket analysis, an unsupervised learning technique for understanding and quantifying the relationships between sets of items.
Decision Trees
I discuss decision trees which are very powerful, general-purpose models that are also interpretable.
Your Closest Neighbors
I discuss the k-nearest neighbors algorithm, a remarkably simple but effective machine learning model.
Model Evaluation
In which I describe commonly used techniques for evaluating machine learning models.
Why Did You Choose This Model?
I discuss strategies such as cross-validation which are used for selecting best-performing machine learning models.
Join the Ensemble
In which I discuss the technique of ensembling which is used to improve the performance of a single machine learning model by combining the power of several other models.
Model Regularization
In which I discuss regularization, a strategy for controlling a model's generalizability to new datasets.
Principal Components Analysis
In which I give a primer on principal components analysis, a commonly used technique for dimensionality reduction in machine learning.
What Features Do You Want?
In which I discuss feature selection which is used in machine learning to help improve model generalization, reduce feature dimensionality, and do other useful things.
Controlling Your Model's Bias
In which I describe the bias-variance tradeoff, one of the most important concepts underlying all of machine learning theory.
Trends in Natural Language Processing: ACL 2019 In Review
Following an eventful week at ACL 2019, I summarize key trends and takeaways to look for in NLP.
What K-Means
In which we investigate K-means clustering, a common unsupervised clustering technique for analyzing data.
Recurrent Neural Networks
I describe recurrent neural networks, a deep learning technique geared for tasks such as natural language processing.
Dance Dance Convolution
We discuss convolutional neural networks, a deep learning model inspired by the human visual system that has rocked the state-of-the-art in computer vision tasks.
Neural Network Grab Bag
We investigate a motley of interesting properties and goodies of neural networks.
Deep Dive Into Neural Networks
We begin a deep dive into deep learning by investigating feedforward neural networks.
Basics of Support Vector Machines
We discuss support vector machines, a very powerful and versatile machine learning model.
Naive Bayes Classifier Tutorial
I describe Naive Bayes, a commonly-used generative model for a variety of classification tasks.
Logistic Regression in Machine Learning Tutorial
I describe logistic-regression, one of the cornerstone algorithms of the modern-day machine learning toolkit.
Fundamentals of Linear Regression
I describe the basics of linear regression, one of the most common and widely used machine learning techniques.
Transformers: Attention in Disguise
We discuss the Transformer, a purely attention-based architecture that is more performant, more efficient, and more parallelizable than recurrent network-based models.
Deep Contextualized Word Representations with ELMo
I describe ELMo, a recently released set of neural word representations that are pushing the state-of-the-art in natural language processing pretraining methodology.
Fast Object Detection with Fast R-CNN
I dive into the details of Fast R-CNN, an extension to the original R-CNN model that boasted 9x speedup over its predecessor as well as state-of-the-art object detection results.
Object Detection with R-CNN
A discussion of R-CNN, a historic object detection architecture combining region proposals with convolutional neural networks.
Fundamental Deep Learning Algorithms To Learn
A discussion of fundamental deep learning algorithms people new to the field should learn along with a recommended course of study.
Why All The Excitement About Artificial Intelligence
Given all the recent buzz around artificial intelligence, I discuss three reasons for why we are seeing such widespread interest in the field today.
Being a Good Machine Learning Engineer/Data Scientist
A discussion of the most important skills necessary for being an effective machine learning engineer or data scientist.
A Software Interview Challenge
In light of a particularly exhausting software job interview cycle, I discuss the general format of such interviews and propose a playful challenge for any people going through a similar experience.
Review of SIGDial/SemDial 2017
Following my attendance at the 18th Annual Meeting on Discourse and Dialogue, I summarize the most promising directions for future dialogue research, as gleaned from discussions with other researchers at the conference.
A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset
To help spur conversational assistant research, we release a corpus of 3,031 grounded, multi-turn dialogues in three distinct domains appropriate for an in-car assistant: calendar scheduling, weather information retrieval, and point-of-interest navigation.