106 Components & Libraries
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Every company wants to be sure that its current and future AI tasks are solvable. The main issue with most solutions on the market is that they build as products. It's a black box developing by some …
Pine is an aimbot powered by real-time object detection with neural networks. This software can be tweaked to work smoothly in CS:GO, Fortnite, and Overwatch. Pine also has built-in support for Nvidi…
Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet. Linear & Multiple Regression Logistic Regression Regularization
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…
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 …
Note: These Self prepared Interview Questions will be updated weekly.
A collection of different Machine Learning models predicting the time series, concretely the market price for given the currency chart and target. Tested with python versions: 2.7.14, 3.6.0. By defau…
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…
SeaLion is designed to teach today's aspiring ml-engineers the popular machine learning concepts of today in a way that gives both intuition and ways of application. We do this through concise algori…
Machine learning for microcontroller and embedded systems. Train in Python, then do inference on any device with a C99 compiler. Embedded-friendly Inference Convenient Training Supporting libraries C…
A neural networks toolbox with a focus on medical image analysis in tensorflow/keras for now. If you use this code, please cite: If you are using any of the sparse/imputation functions, please cite:
Hi, I’m Juan Diego Toscano. Thanks for stopping by. This repository will help you to get involved in the physics-informed machine learning world. In particular, it includes several step-by-step guide…
To classify videos into various classes using keras library with tensorflow as back-end. I have taken 5 classes from sports 1M dataset like unicycling, marshal arts, dog agility, jetsprint and clay p…
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 …
All the functionalities are grouped under a user-friendly API. To ensure proper installation, run unit tests as: Running tests on cpu can take up to 20mn. The output can be then visualized with: If y…
All of the Keras built in models are made available: This project has the following dependencies: The only thing you have to do to get started is set up the folders in the following structure:
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. Considering the popularity of PyTorch in academia, we hope this repo can h…
Remo is a web-based application to organize, annotate and visualize Computer Vision datasets. Remo runs on Windows, Linux, Mac or directly in Google Colab Notebooks. It can also be served on a privat…
Check out corresponding Medium article: Structure your data as follows: For binary classifications you are good to go! For non-binary classifications:
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…
AI projects in python, mostly Jupyter notebooks.
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