Deep-neural-networks Libraries

30 Components & Libraries

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Deep-neural-networks Libraries

An Open Source Machine Learning Framework for Everyone

TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the…

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

If you prefer to use latest CNTK bits from master, use one of the CNTK nightly packages: You can learn more about using and contributing to CNTK with the following resources: Dear community, With our…

A flexible, high-performance serving system for machine learning models

To note a few features: The easiest and most straight-forward way of using TensorFlow Serving is with Docker images. We highly recommend this route unless you have specific needs that are not address…

Deep learning with satellite & aerial imagery

Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment In instance segmentation, each individual 'instance' of a segmented area is given a u…

Deep Learning Toolkit for Medical Image Analysis

If you use DLTK in your work please refer to this citation for the current version: Setup a virtual environment and activate it. Although DLTK<=0.2.1 supports and python 2.7, we will not support i…

[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

Python script for illustrating Convolutional Neural Networks (CNN) using Keras-like model definitions

Python script for illustrating Convolutional Neural Networks (CNN). Inspired by the draw_convnet project [1]. python-pptx (if you want to save models as pptx) Keras (if you want to convert Keras sequ…

U-Net: Convolutional Networks for Biomedical Image Segmentation

Arm Machine Learning tutorials and examples

Deploy a quantized TensorFlowLite MobileNet V2 model on Android using the Arm NN SDK. Implement a neural style transfer on Android with Arm NN APIs. CMSIS project showing keyword spotting (KWS) and o…

Understanding emotions from audio files using neural networks and multiple datasets.

This project presents a deep learning classifier able to predict the emotions of a human speaker encoded in an audio file. The classifier is trained using 2 different datasets, RAVDESS and TESS, and …

:book: This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.

Machine Learning (Beginners Hub), information(courses, books, cheat sheets, live sessions) related to machine learning, data science and python is available

Note: These Self prepared Interview Questions will be updated weekly.

Machine Learning Environment - alpha version

Here is how it looks in action: Get VergeML via pip: Verify your installation by typing: Let's create a very simple image classifier which tells apart cats from dogs. First, we create a new project f…

MachineLearning-DeepLearning-Code-for-my-YouTube-Channel

The full collection of all codes for my Youtube Channel segregated as per topic.

ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many related technologies with different real-world projects and become Interview ready.

Extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy Data visualization is the …

Get started with Machine Learning with Python - An introduction with Python programming examples

There are two options. Note: On each of those options, you'll find: No installations needed. Note: On each notebook, click on "Open in Colab", in order to open it on Google Colab Worried about whethe…

An experimental Pytorch implementation of Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

An experimental PyTorch implementation of Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network. Considering the popularity of PyTorch in academia, we hope this repo can h…

A metric for Perceptual Image-Error Assessment through Pairwise Preference (PieAPP at CVPR 2018).

In this repo, we provide the Tensorflow and PyTorch implementations of our evaluation code for PieAPP v0.1 along with the trained models. We also provide a Win64 command-line executable. The input to…

Cluster images based on image content using a pre-trained deep neural network, optional time distance scaling and hierarchical clustering.

Collection of lectures and lab lectures on machine learning and deep learning. Lab practices in Python and TensorFlow.

Python package containing all custom layers used in Neural Networks (Compatible with PyTorch, TensorFlow and MegEngine)

Deep Learning workshop including image classification, face recognition, Object detection, language modelling, image captioning and neural machine translation.

VizSec17: Web-based visualization tool for adversarial machine learning / LiveDemo

This is Andrew Norton's capstone research work. The goal is to perform a similar function to Google's TensorFlow Playground, but for evasion attacks in adversiaral machine learning. It is a web ser…

A weekly workshop series at ITP to teach machine learning with a focus on deep learning

A weekly workshop series at NYU ITP to teach machine learning with a focus on deep learning

The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

My Projects, Kaggle Competitions and implementation of some popularly known machine learning algorithms. I have also included a list of research papers I have curated in the feild of ML/DL/AI. Follow…

Face recognition and analytics library based on deep neural networks and ONNX runtime

C# interface

German-Traffic-Sign-Classification-Using-TensorFlow

In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.

I'll explain each step in details below. The pickled data is a dictionary with 4 key/value pairs: In this step, we will apply several preprocessing steps to the input images to achieve the best…