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

A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

Implementation of "The Power of Scale for Parameter-Efficient Prompt Tuning"

LibFewShot: A Comprehensive Library for Few-shot Learning.

Code for ViTAS_Vision Transformer Architecture Search

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

A module for solving and visualizing Schrödinger equation.

Redash reset for python

Semantic similarity computation with different state-of-the-art metrics

Manifold-Mixup implementation for fastai V2

Keras code and weights files for popular deep learning models.

GAT - Graph Attention Network (PyTorch) 💻 + graphs + 📣 = ❤️

novel deep learning research works with PaddlePaddle

Paper Code：A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection

State-to-Distribution (STD) Model

LightNet++: Boosted Light-weighted Networks for Real-time Semantic Segmentation

Human Dynamics from Monocular Video with Dynamic Camera Movements

The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

SuRE Evaluation: A Supplementary Material

Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021]

Rethinking Portrait Matting with Privacy Preserving