Keras documentation: Layer activation functionsLayer activation functions Usage of activations Activations can either be used through an Activation layer, or through the activation argument supported by all forward layers:
GitHub - MineDojo/Voyager: An Open-Ended Embodied Agent with Large Language Models
We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behavio
GitHub - tloen/alpaca-lora: Instruct-tune LLaMA on consumer hardware
🤗 Try the pretrained model out here, courtesy of a GPU grant from Huggingface! Users have created a Discord server for discussion and support here 4/14: Chansung Park's GPT4-Alpaca adapters: #340 This repository contains code for reproducing the Stanford Alpaca results using low-rank adaptation (LoRA). We provide an Instruct model of similar quality to text-davinci-003 that can run on a Raspberry
Replicate ChatGPT Training Quickly and Affordable with Open Source Colossal-AI
February 14, 2023 Replicate ChatGPT Training Quickly and Affordable with Open Source Colossal-AI We recently released new open source code for Colossal-AI, which enables you to use it as a framework for replicating the training process of OpenAI’s popular ChatGPT application optimized for speed and efficiency. With Colossal-AI's efficient implementation of RLHF (Reinforcement Learning with Human F
GitHub - google-deepmind/alphafold: Open source code for AlphaFold.
This package provides an implementation of the inference pipeline of AlphaFold v2. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. We also provide: An implementation of AlphaFold-Multimer. This represents a work in progress and AlphaFold-Multimer isn't expected to be as stable as our monomer AlphaFold system. Read the guide for how to upgrade and update co
Coreレイヤー - Keras Documentation
[source] Dense keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) 通常の全結合ニューラルネットワークレイヤー． Denseが実行する操作：output = activation(dot(input, kernel) + bias)ただし，activationはactivation引数として渡される要素単位の活性化関数で，kernelはレイヤーによって
Caffeで超軽量な "Semantic Segmentation" のモデルを生成する Sparse-Quantized CNN 512x1024_10MB_軽量モデル_その１ - Qiita
Caffeで超軽量な "Semantic Segmentation" のモデルを生成する Sparse-Quantized CNN 512x1024_10MB_軽量モデル_その１PythonDeepLearningCaffeSemanticSegmentationセグメンテーション ◆ はじめに 先月ようやく、今時のGPU付きのノートパソコンを購入できたので、本格的に趣味でDeepLearningに取り組めるようになった。 ただ、セマンティック・セグメンテーションに興味を持ってあれこれとモデルを作り始めてみたものの、最終生成されたモデルのサイズが 500MB とか、200MB とか、ロースペック端末では現実的に利用不可能な巨大なサイズとなることが分かり、途方に暮れていた。 都合上、 Pure Caffe や Pure Tensorflow 、あるいは、それらの派生フレームワーク で実装でき
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Amazon.co.jp: 実践Data Scienceシリーズ PythonではじめるKaggleスタートブック (KS情報科学専門書): 石原祥太郎, 村田秀樹: 本
Keras documentation: Model training APIs
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GitHub - hwchase17/langchain-hub
GitHub - hpcaitech/ColossalAI: Making large AI models cheaper, faster and more accessible
Amazon.co.jp: Pythonによるディープラーニング (Compass Booksシリーズ): François Chollet (著), 株式会社クイープ (翻訳), 巣籠悠輔 (その他): 本
Keras documentation: Dense layer
GitHub - TachibanaYoshino/AnimeGANv2: [Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime![GitHub - TachibanaYoshino/AnimeGANv2: [Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime](https://cdn-ak-scissors.b.st-hatena.com/image/square/72026b843da18e773cf3ddb171021433be16d149/height=288;version=1;width=512/https%3A%2F%2Fopengraph.githubassets.com%2F5dc0718db026dc77d58bcaad9a9f9733516d9b1839edfc29d9bc5abcd35871c7%2FTachibanaYoshino%2FAnimeGANv2)
https://keras.io/layers/normalization/
https://keras.io/layers/convolutional/
https://keras.io/optimizers/
Keras documentation: Core layers
Webコンテンツ抽出のvision-based手法: TextMaps - Qiita