Alternatives

Products that do what The Impact of Noise on Recurrent Neural Networks does

Series on reservoir computing, focusing on echo state networks. Covers GPU simulations, addressing variable training lengths and data processing. Analyzes Gaussian noise impact on network performance, with practical demonstrations and theoretical discussions. Relevant for those interested in neural network behavior in noisy environments.

1. 1BM
   Boltzmann Machines in TensorFlow with Examples: RBM, DBM, AIS▲201

   2017 · github.com

2. 2DL
   Deep Learning in TensorFlow – The Roadmap for Study and Learning▲146

   2019 · github.com

3. 3CT
   Comprehensive Tutorials in Deep Learning Using TensorFlow▲327

   2018 · github.com

4. 4
   Neural Networks and Deep Learning▲95

   Core concepts behind neural networks and deep learning

   2015

5. 5HI
   How I topped the HuggingFace open LLM leaderboard on two gaming GPUs▲495

   I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants. The weird finding: single-layer duplication does nothing. Too few layers, nothing. Too many, it gets worse. Only circuit-sized blocks of ~7 layers work. This suggests pretraining carves out discrete functional circuits in the layer stack that only work when preserved whole. The whole thing was developed on 2x RTX 4090s in my basement. I'm…

   Mar 2026 · dnhkng.github.io

6. 6SA
   Simple and comprehensive tutorials in Deep Learning using TensorFlow▲157

   2018 · github.com

7. 7IT
   Introduction to Recurrent Networks in TensorFlow▲6

   2016 · danijar.github.io

8. 8AS
   Automatic Speech Recognition in TensorFlow▲131

   2017 · github.com

9. 9AT
   Applying the Unix philosophy to neural networks▲251

   2019 · github.com

10. 10DG
    Dockerized GPU Deep Learning Solution (Code and Blog and TensorFlow Demo)▲80

    2016 · github.com

11. 11
    TensorFire▲99

    Blazing-fast in-browser neural networks

    2017

12. 12
    Sonnet▲134

    A new library for constructing neural networks from DeepMind

    2017

13. 13CG
    Cloud GPUs for Deep Learning – At 1/3 the Cost of AWS/GCP▲168

    2021 · gpu.land

14. 14AV
    A Visual and Interactive Guide to the Basics of Neural Networks▲63

    2016 · jalammar.github.io

15. 15AG
    A Go implementation of TensorFlow's streaming quantiles estimator▲98

    2018 · github.com

16. 16
    TensorFlow 1.0▲80

    Open-source machine learning library by Google

    2018

17. 17AP
    A perceptron with stochastic gradient descent in Go▲94

    2017 · github.com

18. 18
    Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21

    Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining

    Aug 2026 · github.com

19. 19SN
    Single neuron neural network written in COBOL▲94

    2020 · github.com

20. 20IV
    I've made a GPU-based wavelet spectrogram tool for birdsongs▲34

    2022 · github.com

21. 21AG
    A general-purpose encoder-decoder framework for Tensorflow▲54

    2017 · github.com

22. 22AC
    A Course on Deep Learning Using Google's TensorFlow▲39

    2019 · github.com

23. 23RE
    Recurrent Entity Networks with TensorFlow▲37

    2016 · github.com

24. 24DL
    Deep Learning Using TensorFlow – Tutorials and Source Code▲31

    2017 · github.com

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