Deep-Learning-Experiments VS nn

Compare Deep-Learning-Experiments vs nn and see what are their differences.

Deep-Learning-Experiments

Videos, notes and experiments to understand deep learning (by roatienza)
Deep Learning deep-learning-tutorial Artificial intelligence Pytorch Vision Speech NLP
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nn

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠 (by lab-ml)
Deep Learning deep-learning-tutorial Pytorch Gan Transformers reinforcement-learning Optimizers neural-networks Transformer Machine Learning attention literate-programming
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nn.labml.ai
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Deep-Learning-Experiments nn
1 26
1,081 48,430
- 4.5%
8.3 7.7
about 1 month ago about 1 month ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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Deep-Learning-Experiments

Posts with mentions or reviews of Deep-Learning-Experiments. We have used some of these posts to build our list of alternatives and similar projects.
  • EEE 197 - Deep Learning
    1 project | /r/peyups | 25 Aug 2022
    Hello, took the course last sem. Maraming napa-drop sa amin dahil sa difficulty nung assignments pero doable naman. Open-source mismo yung course, available sya sa GitHub: https://github.com/roatienza/Deep-Learning-Experiments

nn

Posts with mentions or reviews of nn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-09.

What are some alternatives?

When comparing Deep-Learning-Experiments and nn you can also consider the following projects:

conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN

adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.

labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

DeepLearning - Contains all my works, references for deep learning

functorch - functorch is JAX-like composable function transforms for PyTorch.

python_autocomplete - Use Transformers and LSTMs to learn Python source code

ZoeDepth - Metric depth estimation from a single image

pytorch-deepdream - PyTorch implementation of DeepDream algorithm (Mordvintsev et al.). Additionally I've included playground.py to help you better understand basic concepts behind the algo.

onnx-simplifier - Simplify your onnx model

TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)

Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

Deep-Learning-Experiments vs conformal_classification nn vs GFPGAN-for-Video-SR Deep-Learning-Experiments vs adaptnlp nn vs labml Deep-Learning-Experiments vs DeepLearning nn vs functorch Deep-Learning-Experiments vs python_autocomplete nn vs ZoeDepth Deep-Learning-Experiments vs pytorch-deepdream nn vs onnx-simplifier Deep-Learning-Experiments vs TTS nn vs Basic-UI-for-GPT-J-6B-with-low-vram