machine.academy
NNfSiX
| machine.academy | NNfSiX | |
|---|---|---|
| 1 | 46 | |
| 11 | 1,354 | |
| - | - | |
| 7.6 | 0.0 | |
| 5 months ago | 8 months ago | |
| C# | C++ | |
| GNU General Public License v3.0 only | MIT License |
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machine.academy
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Neural Network from Scratch
Nice! I made a gpu accelerated backpropagation lib a while ago to learn about NNs, if you are interested check it out here: https://github.com/zbendefy/machine.academy
NNfSiX
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Are there any books I should read to learn machine learning from scratch?
I've been rather enjoying "Neural Networks from Scratch" (https://nnfs.io/)
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Ask HN: Those learning about neural networks, what do you find most difficult?
I haven't gotten super deep into it yet, but https://nnfs.io/ has been good in my opinion. The book slowly replaces written and explained code with numpy equivalents to keep the examples fast. Plus the accompanying animations are also useful. I would be curious what others think on it too.
- Gutes Einführungsbuch zu KI
- [Deep Learning] Neural Networks from Scratch in Python
- What do I get a programming obsessed high school boy for his birthday? I actually need advice
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GPT in 60 Lines of NumPy
For those curious to writing "gradient descent with respect to some loss function" starting from an empty .py file (and a numpy import, sure), can't recommend enough Harrison "sentdex" Kinsley's videos/book Neural Networks from Scratch in Python [1].
[1] https://youtu.be/Wo5dMEP_BbI?list=PLQVvvaa0QuDcjD5BAw2DxE6OF... https://nnfs.io
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Ask HN: What are the foundational texts for learning about AI/ML/NN?
Not sure if foundational (quite a tall order in such a fast-moving field), but for sure a nice introduction into neural networks, and even mathematics in general (because it's nice to see numbers in action beyond school-level algebra):
Harrison Kinsley, Daniel Kukiela, Neural Networks from Scratch, https://nnfs.io, https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0Qu...
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Ask HN: How to get back into AI?
Have you had a look at https://nnfs.io/ ? I bought the book and am gearing up to start working through it, I would be interested to know your thoughts. Generally I want to chart a personal curriculum from data engineer to practical application of modern AI to real business problems.
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Programming an AI as a beginner
You can check out Neural Networks from Scratch in Python for an introduction to neural networks, which can be used for image classification. Please be forewarned that you'll need the mathematics necessary to read through this book - however, I'm assuming that since you've selected writing such an algorithm(s) in Python for your final school project that you're aware of such.
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Moved to amd today and holy it's amazing
I am planning on working my way through Neural Networks From Scratch (https://nnfs.io/) in a few months just to build my understanding. After that I'm hoping to be able to figure out the best path for a couple of projects I have in mind.
What are some alternatives?
ML-From-Scratch - Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
micrograd - A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
GazeTracker - Artificial intelligence tracker for OpenTrack
Numerical-Realization - An English-based natural language generator for numerical inputs.
deepnet - Educational deep learning library in plain Numpy.
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
SoftGround - High performance runtime collider deformation in Unity
ProjectOne - The project is to build a neural network from scratch. The motivation for this project is from nnfs.io a website build by @Sentdex. Nnfs.io is actually meant for a book that teaches the fundamentals of neural network and help us to build our own network. Let's build a new neural network where we can learn the fundamentals and make a great hands-on work space for aspiring machine learning engineers and the GitHub community