cs231n VS DeepLearning

Compare cs231n vs DeepLearning and see what are their differences.

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cs231n DeepLearning
1 1
42 3
- -
0.0 0.0
over 2 years ago almost 2 years ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

cs231n

Posts with mentions or reviews of cs231n. We have used some of these posts to build our list of alternatives and similar projects.

DeepLearning

Posts with mentions or reviews of DeepLearning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-06.

What are some alternatives?

When comparing cs231n and DeepLearning you can also consider the following projects:

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

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stanford-cs229 - 🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford

conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

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.

analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico

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]

weightless_NN_decompression - Proof of concept for neural network decompression without storing any weights

monodepth2 - [ICCV 2019] Monocular depth estimation from a single image

Deep-Learning-Experiments - Videos, notes and experiments to understand deep learning

deep-learning-v2-pytorch - Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101