AI Rank
EN
← 开源
gabrieleangeletti

Deep-Learning-TensorFlow

Ready to use implementations of various Deep Learning algorithms using TensorFlow.

TutorialsML/AI fundamentalsPython
在 GitHub 打开
增长势头
+024 小时新增 Star0.0%
954
Star
364
Fork
+0
本周
6
贡献者
创建于 2015-08-17 · 更新于 2026-10-06 · 今日第 12411 名
主要开发者
gabrieleangelettiabyoussefJakobovski

gabrieleangeletti/Deep-Learning-TensorFlow 排名第 12,411，有 954 个星标。

同类别比较

gabrieleangeletti/Deep-Learning-TensorFlow 排名第 12,411，此处有 8 个同类条目；PanJinquan/tensorflow_models_learning 排名第 12,405。

同类别比较
名称排名指标
DLSchool/deep-learning-school12,373
语言:
Jupyter Notebook
星标数:
960
分叉数:
532
简介:
Официальный репозиторий курса Deep Learning (2018-2021) от Deep Learning School при ФПМИ МФТИ
movidius/ncappzoo12,378
语言:
Python
星标数:
960
分叉数:
371
简介:
Contains examples for the Movidius Neural Compute Stick.
llSourcell/Machine_Learning_Journey12,394
星标数:
957
分叉数:
245
简介:
This is the Curriculum for "Machine Learning Journey" By Siraj Raval on Youtube
PanJinquan/tensorflow_models_learning12,405
语言:
Python
星标数:
954
分叉数:
286
简介:
tensorflow GoogleNet inception V1 V2 V3 V4
MorvanZhou/NLP-Tutorials12,422
语言:
Python
星标数:
952
分叉数:
315
简介:
Simple implementations of NLP models. Tutorials are written in Chinese on my website https://mofanpy.com
llSourcell/LearnML12,433
星标数:
951
分叉数:
178
简介:
This is the Study Guide for Learn Machine Learning in 3 Months (PyTorch Curriculum) by Siraj Raval on Youtube
weaviate/recipes12,442
语言:
Jupyter Notebook
星标数:
950
分叉数:
199
简介:
This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!
lansinuote/Huggingface_Toturials12,453
语言:
Jupyter Notebook
星标数:
948
分叉数:
247
简介:
bert-base-chinese example
关键参数与规格
关键参数与规格
参数值
语言Python
类别Tutorials
子类别ML/AI fundamentals
星标数954
分叉数364
贡献者6
创建日期2015-08-17
更新日期2026-10-06
1 天星标数0
7 天星标数0
排名12,411
上次排名12,337
排名变化-74
7 天星标排名13,406
上次 7 天星标排名13,421
7 天星标排名变化15
总星标排名11,560
上次总星标排名11,543
总星标排名变化-17
国家Portugal
README 摘录

Deep Learning algorithms with TensorFlow This repository is a collection of various Deep Learning algorithms implemented using the TensorFlow library. This package is intended as a command line utility you can use to quickly train and evaluate popular Deep Learning models and maybe use them as benchmark/baseline in comparison to your custom models/datasets. If you want to use the package from ipython or maybe integrate it in your code, I published a pip package named : Yet Another Deep

Learning Tool. 0 List of available models: Convolutional Network Restricted Boltzmann Machine Deep Belief Network Deep Autoencoder as stack of RBMs Denoising Autoencoder Stacked Denoising Autoencoder Deep Autoencoder as stack of Denoising Autoencoders MultiLayer Perceptron Logistic Regression Installation Through pip: You can learn the basic usage of the models by looking at the command_line/ directory. Or you can take a look at the documentation. **Note**: the documentation is still a work in progress for the pip package, but the

package usage is very simple. The classes have a sklearn-like interface, so basically you just have to create the object (e. ) and call the fit/predict methods, and the pretrain() method if the model supports it (e. , and ) Through github: cd in a directory where you want to store the project, e. /home/me clone the repository: git clone cd Deep-Learning-TensorFlow now you can configure the software and run the models (see the documentation)! Documentation: You can

find the documentation for this project at this link.

常见问题

记录了多少星标和分叉？

gabrieleangeletti/Deep-Learning-TensorFlow 有 954 个星标和 364 个分叉。

仓库排名是多少？

gabrieleangeletti/Deep-Learning-TensorFlow 排名第 12,411。

记录的语言和类别是什么？

gabrieleangeletti/Deep-Learning-TensorFlow 有 954 个星标，语言为 Python，类别为 Tutorials。

哪个仓库排名最接近？

gabrieleangeletti/Deep-Learning-TensorFlow 与 PanJinquan/tensorflow_models_learning 比较，后者排名第 12,405，有 954 个星标。

README

Deep Learning algorithms with TensorFlow

This repository is a collection of various Deep Learning algorithms implemented using the TensorFlow library. This package is intended as a command line utility you can use to quickly train and evaluate popular Deep Learning models and maybe use them as benchmark/baseline in comparison to your custom models/datasets. If you want to use the package from ipython or maybe integrate it in your code, I published a pip package named yadlt: Yet Another Deep Learning Tool.

Requirements:

  • tensorflow >= 1.0

List of available models:

  • Convolutional Network
  • Restricted Boltzmann Machine
  • Deep Belief Network
  • Deep Autoencoder as stack of RBMs
  • Denoising Autoencoder
  • Stacked Denoising Autoencoder
  • Deep Autoencoder as stack of Denoising Autoencoders
  • MultiLayer Perceptron
  • Logistic Regression

Installation

Through pip:

pip install yadlt

You can learn the basic usage of the models by looking at the command_line/ directory. Or you can take a look at the documentation.

Note: the documentation is still a work in progress for the pip package, but the package usage is very simple. The classes have a sklearn-like interface, so basically you just have to create the object (e.g. sdae = StackedDenoisingAutoencoder()) and call the fit/predict methods, and the pretrain() method if the model supports it (e.g. sdae.pretrain(X_train, y_train), sdae.fit(X_train, y_train) and predictions = sdae.predict(X_test))

Through github:

  • cd in a directory where you want to store the project, e.g. /home/me
  • clone the repository: git clone https://github.com/blackecho/Deep-Learning-TensorFlow.git
  • cd Deep-Learning-TensorFlow
  • now you can configure the software and run the models (see the documentation)!

Documentation:

You can find the documentation for this project at this link.

Models TODO list

  • Recurrent Networks (LSTMs)
  • Variational Autoencoders
  • Deep Q Reinforcement Learning
排名相邻
同类项目