pytorch-seq2seq VS Behavior-Sequence-Transformer-Pytorch

Compare pytorch-seq2seq vs Behavior-Sequence-Transformer-Pytorch and see what are their differences.

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pytorch-seq2seq Behavior-Sequence-Transformer-Pytorch
3 1
5,169 129
- -
5.4 0.0
3 months ago almost 2 years ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

pytorch-seq2seq

Posts with mentions or reviews of pytorch-seq2seq. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-29.

Behavior-Sequence-Transformer-Pytorch

Posts with mentions or reviews of Behavior-Sequence-Transformer-Pytorch. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing pytorch-seq2seq and Behavior-Sequence-Transformer-Pytorch you can also consider the following projects:

Time-Series-Forecasting-Using-LSTM - Time-Series Forecasting on Stock Prices using LSTM

Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

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.

poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)

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, ... 🧠

ru-dalle - Generate images from texts. In Russian

pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

sequitur - Library of autoencoders for sequential data

awesome-speech-recognition-speech-synthesis-papers - Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)

pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

Deep-Learning-Papers-Reading-Roadmap - Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!