AI · alternatives · 2026
24 alternatives to Layered – Neural Networks in Python 3
Clean implementation of feed forward neural networks - danijar/layered
Layered is a Python 3 library that provides a clean implementation of feed-forward neural networks. It is designed for developers and researchers who need a straightforward way to build and work with neural networks.… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Layered – Neural Networks in Python 3 launched in 2015; newer entries below may have overtaken it.
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Neural network inference the Unix way. Contribute to cloudkj/layer development by creating an account on GitHub.
2019 · github.com · its alternatives →
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Thinc is a lightweight type-checked deep learning library for composing models, with support for layers defined in frameworks like PyTorch and TensorFlow.
2020 · thinc.ai · its alternatives →
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Purely functional artificial neural network library implemented in Haskell. - jbarrow/LambdaNet
2014 · github.com · its alternatives →
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I replicated David Ng's RYS method (https://dnhkng.github.io/posts/rys/) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn't expect. Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer. The results on standard benchmarks (lm-evaluation-harness, n=50): Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76…
Mar 2026 · github.com · its alternatives →
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Python library for creating data pipelines with chain functional programming - EntilZha/PyFunctional
2017 · github.com · its alternatives →
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2016 · blog.otoro.net · its alternatives →
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Graph Neural Networks with Keras and Tensorflow 2. - danielegrattarola/spektral
2019 · github.com · its alternatives →
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I trained a transformer in HyperCard. 1,216 parameters. 1989 Macintosh. And yes, it took a while. MacMind is a complete transformer neural network, embeddings, positional encoding, self-attention, backpropagation, and gradient descent, implemented entirely in HyperTalk, the scripting language Apple shipped with HyperCard in 1987. Every line of code is readable inside HyperCard's script editor. Option-click any button and read the actual math. The task: learn the bit-reversal permutation, the opening step of the Fast Fourier Transform. The model has no formula to follow. It discovers the…
Apr 2026 · github.com · its alternatives →
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BlockDL▲12Hi everyone. Designing neural network architectures is inherently a visual process. Every time I train a new model, I find myself sketching it out on paper before translating it into code (and still running into shape mismatches no matter how many networks I've built). So I built BlockDL: a free and open-source tool to design neural networks visually in your browser. It updates live Python/Keras code as you build, and automatically checks for shape and connectivity errors in real time. It supports: - Most common layer types (Conv2D, Dense, LSTM, etc.) - Multiple input/output…
2025 · blockdl.com · its alternatives →
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Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js - GitHub - tensorspace-team/tensorspace: Neural network 3D visualization framework, build interactive and intuitive model in br
2018 · github.com · its alternatives →
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Hi HN: I published part one of my free NLP course. The course is intended to help anyone who knows Python and a bit of math go from the very basics all the way to today's mainstream models and frameworks. I strive to balance theory and practice and so every module consists of detailed explanations and slides along with a Colab notebook (in most modules) putting the theory into practice. In part one, we cover text preprocessing, how to turn text into numbers, and multiple ways to classify and search text using "classical" approaches. And along the way, we'll pick up useful bits on how to use…
2022 · nlpdemystified.org · its alternatives →
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I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com · its alternatives →
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A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
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The simplest form of an artificial neural network explained and demonstrated. - GitHub - gokadin/ai-simplest-network: The simplest form of an artificial neural network explained and demonstrated.
2020 · github.com · its alternatives →
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Hello and happy new year! We're excited to introduce the Pyper package for concurrency & parallelism in Python. Pyper is a flexible framework for concurrent / parallel data processing, following the functional paradigm. Source code can be found on [github](https://github.com/pyper-dev/pyper) Key features: Intuitive API: Easy to learn, easy to think about. Implements clean abstractions to seamlessly unify threaded, multiprocessed, and asynchronous work. Functional Paradigm: Python functions are the building blocks of data pipelines. Let's you write clean, reusable…
2025 · github.com · its alternatives →
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2020 · nn-512.com · its alternatives →
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Open-source machine learning library by Google
2018 · tensorflow.org · its alternatives →
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Discussions: Hacker News (63 points, 8 comments), Reddit r/programming (312 points, 37 comments) Translations: Arabic, French, Spanish Update: Part 2 is now live: A Visual And Interactive Look at Basic Neural Network Math Motivation I’m not a machine learning expert. I’m a software engineer by train
2016 · jalammar.github.io · its alternatives →
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Absolute minimalistic implementation of a GPT-like transformer using only numpy (<650 lines). - joennlae/tensorli
2023 · github.com · its alternatives →
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I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com · its alternatives →
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A graph-based functional API for building complex scikit-learn pipelines. - alegonz/baikal
2020 · github.com · its alternatives →
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A general-purpose encoder-decoder framework for Tensorflow - google/seq2seq
2017 · github.com · its alternatives →
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Explains programming language concepts in plain language.
2016 · fullstackpython.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →