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

TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

unet for image segmentation

Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling @ INTERSPEECH 2021 Accepted

S2s2net - Sentinel-2 Super-Resolution Segmentation Network

An implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network"

OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.

Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions

Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021)

AI Summer's complete catalog of articles

Dense Prediction Transformers

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021).

Code for "Causal autoregressive flows" - AISTATS, 2021

Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2021

Normal Learning in Videos with Attention Prototype Network

Statsmodels: statistical modeling and econometrics in Python

Cancer metastasis detection with neural conditional random field (NCRF)

TorchXRayVision: A library of chest X-ray datasets and models.