Deep Learning: Traffic Sign Classifier with TensorFlow

Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebook, you'll be using VGGNet trained on the Ima…

A Jupyter notebook with an example of a Deep Convolutional Neural Network for Facial Landmarks Detection

Scripts + IPython Notebook for performing transfer learning on the Inception pre-trained Convolutional Neural Network Model in TensorFlow for classifying fruits using the FIDS 30 dataset.

Collection of notebooks exploring conv nets in detail.

Notebooks containing examples for different DNN components

Jupyter notebooks for Chainer hands-on

Notebooks prepared while reading the Deep Learning book

This is a repository with various classifier written in jupyter notebook that works on hand written digits images.

Malayalam Character Recognition using CNNs in python

A jupyter notebook is provided for in-depth explanation.

Jupyter Notebook for Deep Features Classification

Visualization of the VGG16 weights in a Jupyer notebook

This repository contains an ipython notebook which implements a Convolutional Neural Network to do a binary image classification. I used this to classify Cats vs Dogs and you can get the dataset from here https://www.kaggle.com/c/dogs-vs-cats/data . (This model trains with thousands of input images so be patient.)

This is a Deep Learning Project to classify German Road Signs using deep neural networks and image processing.

TensorFlow and Deep Learning Tutorials

A set of notebooks and other resources regarding my deep learning experiments

My classnotes, experiments, reproducible notebooks from fast.ai Deep Learning Class (v2)

Jupyter notebooks containing explanation of Machine Learning Algorithms

TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN, ...)