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I joined Apple as a Principal Software Engineer in early 2024. I've wanted to work here since I was a kid. I switched to OS X as my primary desktop when it was in beta. I've used Macs ever since and have always appreciated how the company approaches product design. Before this I was CTO at UJET, and before that held engineering leadership roles at Wag, GoFundMe, and CrowdRise. The common thread has been building systems that actually work for the people using them...

NOTE: This post is part of my Machine Learning Series where I discuss how AI/ML works and how it has evolved over the last few decades. Autoencoders are a type of neural network architecture used for tasks such as dimensionality reduction, feature extraction, and data denoising. With their ability to learn efficient representations of data, autoencoders have found applications in various fields, from image processing to anomaly detection. In this post, we'll explore the structure and ...

NOTE: This post is part of my Machine Learning Series where I discuss how AI/ML works and how it has evolved over the last few decades. Recurrent Neural Networks (RNNs) are a class of neural networks designed to handle sequential data. Whether it's analyzing time series, understanding natural language, or predicting stock prices, RNNs are powerful tools for capturing temporal dependencies in data. In this post, we'll delve into the structure of RNNs, how they process sequences, and th...

NOTE: This post is part of my Machine Learning Series where I discuss how AI/ML works and how it has evolved over the last few decades. Convolutional Neural Networks (CNNs) have become the go-to architecture for image recognition and computer vision tasks. CNNs excel at identifying patterns in images, such as edges, textures, and shapes, making them a key player in applications like image classification, object detection, and facial recognition. In this post, we'll explore the key com...

NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades. Feedforward Neural Networks (FNNs), also known as Multi-Layer Perceptrons (MLPs), are one of the most fundamental and widely-used neural network architectures in machine learning. FNNs have been employed for a variety of tasks, including classification, regression, and feature extraction. In this post, we'll explore the architecture, training pro...

NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades. Neural networks are the foundation of many artificial intelligence and machine learning applications. There are several types of neural networks, each designed to address specific types of problems. In this post, we'll explore the most common types of neural networks and their applications. [](https:/...

NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades. One of the most transformative developments in the field of artificial intelligence and machine learning was the advent of neural networks. These computational models are designed to mimic the way the human brain processes information and are capable of performing complex tasks such as image recognition, natural language processing, and more....

NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades. Computer vision, the field of AI that enables computers to interpret and understand visual information from the world, has undergone significant advancements over the past decade. The ability to analyze images and videos, recognize objects, and understand visual scenes has opened up a multitude of applications in fields such as healthcare, autono...

NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades. Machine learning has become an integral part of our lives, powering applications from voice assistants to self-driving cars. However, the field has a rich history that spans over five decades, with foundational ideas that date back even further. In this blog post, we'll explore the key milestones and breakthroughs in the history of machine learni...

Machine learning is an exciting and rapidly evolving field that has the potential to transform virtually every industry. From natural language processing to computer vision, machine learning models are becoming an integral part of our daily lives, enabling new levels of automation and understanding. To explore the fascinating world of machine learning and share insights with a broader audience, I am launching a blog series on AI/ML. In this post, I will discuss the topics I will be covering a...