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coursera-deep-learning-specialization
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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.
coursera-deep-learning-specialization
What are some alternatives?
cs231n - Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
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]
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
Emotion_Detection_CNN_keras - Train and test our algorithm using Convolution Neural Networks and classify emotions in real-time.
start-machine-learning - A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2024 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
stanford-cs229 - 🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford
Soevnn - A neural net with a terminal-based testing program.
Respiratory-Disease-Coughing-Dataset-CNN - A collection of coughing audio files from Coswara, Coughvid, and Virufy as well as generated spectrograms for the use of machine learning
Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
neural-style-transfer - :paintbrush: This repository contains, well-structured Python library and runnable fully prepared Python notebook of the "Neural Style Transfer" algorithm
NeuralTextToImage - Colabs for text prompt steered image generators
NST-AI-to-create-art - NST was first introduced in 2015 paper it took advantage of how convolution neural network works to generate art