TensorFlow Tutorials

You can find python source code under the python directory, and associated notebooks under notebooks.

	Source code	Description
1	basics.py	Setup with tensorflow and graph computation.
2	linear_regression.py	Performing regression with a single factor and bias.
3	polynomial_regression.py	Performing regression using polynomial factors.
4	logistic_regression.py	Performing logistic regression using a single layer neural network.
5	basic_convnet.py	Building a deep convolutional neural network.
6	modern_convnet.py	Building a deep convolutional neural network with batch normalization and leaky rectifiers.
7	autoencoder.py	Building a deep autoencoder with tied weights.
8	denoising_autoencoder.py	Building a deep denoising autoencoder which corrupts the input.
9	convolutional_autoencoder.py	Building a deep convolutional autoencoder.
10	residual_network.py	Building a deep residual network.
11	variational_autoencoder.py	Building an autoencoder with a variational encoding.

Installation Guides

For Ubuntu users using python3.4+ w/ CUDA 7.5 and cuDNN 7.0, you can find compiled wheels under the wheels directory. Use pip3 install tensorflow-0.8.0rc0-py3-none-any.whl to install, e.g. and be sure to add: export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" to your .bashrc. Note, this still requires you to install CUDA 7.5 and cuDNN 7.0 under /usr/local/cuda.

Resources

Author

Parag K. Mital, Jan. 2016.

http://pkmital.com

License

See LICENSE.md

From the basics to slightly more interesting applications of Tensorflow

Related tags

Overview

TensorFlow Tutorials

Installation Guides

Resources

Author

License

Owner

Parag K Mital

Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation.

This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.

FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI

Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch.

An ever-growing playground of notebooks showcasing CLIP's impressive zero-shot capabilities.

PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

Database Reasoning Over Text project for ACL paper

Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

An updated version of virtual model making

Deep Face Recognition in PyTorch

MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks

Binary Stochastic Neurons in PyTorch

Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

VR Viewport Pose Model for Quantifying and Exploiting Frame Correlations

Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Official code for "Mean Shift for Self-Supervised Learning"

Rust bindings for the C++ api of PyTorch.