A Review of Neural Networks

John Wise

Nov 28, 2023 — 1 min read

A Review of Deep Learning Architectures and Application of Transfer Learning for Image Classification

Abstract: Deep Learning is a vast field within computer science which has made tremendous progress in its application and abilities in the past decade. Some applications of deep learning include image recognition, language translation, neural audio effects, self-driving automobiles, and even cancer diagnosis. In this paper we will explore some foundational architectures of deep learning as well as their various applications. We will then go on to discuss our hands-on experience with transfer learning using ResNet, an architecture for the task of image classification.

A Review of Neural Networks

A Review of Neural Networks.pdf

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How DSP Filters Work

In this post I will be diving into how DSP filters work. We will cover fundamental concepts such as frequency and phase response plots. Once we have this ground work laid we will implement our knowledge by studying First Order Feed-Forward and First Order Feed-Back Filters. This information was gathered

Anatomy of an Audio Effect Plugin

In this post I will be diving into the separate components that make up the anatomy of an audio plugin. This information was gathered again from the book Designing Audio Effect Plugins in C++ For AAX, AU, and VST3 with DSP Theory by Will C. Pirkle (ISBN 13: 978-1138591936). Using

Introduction to Digital Signal Processing

As I begin my dive into Digital Signal Processing (DSP) for audio applications, I thought it would be good to share what I am learning along the way. A few years ago I sought resources to learn DSP from a professor of mine. He recommended that I check out the

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How DSP Filters Work

Anatomy of an Audio Effect Plugin

Introduction to Digital Signal Processing