NNfSiX
examples
NNfSiX | examples | |
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46 | 143 | |
1,359 | 7,776 | |
- | 1.0% | |
0.0 | 5.3 | |
8 months ago | 13 days ago | |
C++ | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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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.
examples
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
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MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
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🔥14 Excellent Open-source Projects for Developers😎
10. TensorFlow - Make Machine Learning Work for You 🤖
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GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
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Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
What are some alternatives?
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
cppflow - Run TensorFlow models in C++ without installation and without Bazel
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.
mlpack - mlpack: a fast, header-only C++ machine learning library
micrograd - A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
deepnet - Educational deep learning library in plain Numpy.
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
Selenium WebDriver - A browser automation framework and ecosystem.
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
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing