arubisov/cs182
Self-study of CS182 (Spring 2021) at UC Berkeley - Designing, Visualizing and Understanding Deep Neural Networks
This repository is a self-study resource for those looking to understand and implement deep neural networks from the ground up. It provides detailed solutions and explanations for various neural network architectures, including fully-connected, convolutional, RNNs, LSTMs, and Transformers. Aspiring machine learning engineers, data scientists, or researchers who want to deeply grasp the internal workings of deep learning models will find this useful.
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Use this if you are a student or practitioner who wants to build a strong foundational understanding of deep neural network design, visualization, and training, including common architectures and optimization techniques.
Not ideal if you are looking for a plug-and-play deep learning library or a high-level tool to quickly apply pre-built models without diving into the underlying mathematical and algorithmic details.
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Sep 13, 2023
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