10-days-of-grad
cs231n
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30 | 42 | |
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7.8 | 0.0 | |
5 months ago | over 2 years ago | |
Jupyter Notebook | Jupyter Notebook | |
- | MIT License |
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10-days-of-grad
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Is Haskell okay for prototyping machine learning models for research (discovery and exploration)
You might find the Deep Learning From The First Principles tutorials by Bogdan Penkovsky an interesting survey of native Haskell implementations of deep neural networks, and a bit more. It demonstrates some native charting capabilities, and Day 9 uses Hasktorch.
cs231n
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Assignment solutions for Stanford CS231n-Spring 2021
Here's the link to my Repo.
What are some alternatives?
coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
stanford-cs229 - 🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford
Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
stanford-CS229 - Python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng [UnavailableForLegalReasons - Repository access blocked]
monodepth2 - [ICCV 2019] Monocular depth estimation from a single image
deep-learning-v2-pytorch - Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
DeepLearning - Contains all my works, references for deep learning
Reinforcement-Learning-2nd-Edition-by-Sutton-Exercise-Solutions - Solutions of Reinforcement Learning, An Introduction
machinehearing - Machine Learning applied to sound