dvanced-Deep-Learning-with-Keras This is the code repository for Advanced Deep Learning with TensoFlow 2 and Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. Please note that the code examples have been updated to support TensorFlow 2.0 Keras API only. About the Book Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available […]

Hailo Model Zoo The Hailo Model Zoo provides pre-trained models for high-performance deep learning applications. Using the Hailo Model Zoo you can measure the full precision accuracy of each model, the quantized accuracy using the Hailo Emulator and measure the accuracy on the Hailo-8 device. Finally, you will be able to generate the Hailo Executable Format (HEF) binary file to speed-up development and generate high quality applications accelerated with Hailo-8. The models are optimized for high accuracy on public datasets […]

pynm NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) in Python using Pytorch. pynm is an open source, low-code library in python to build neuromorphic predictive models (Classification & Regression problems) using [Spiking Neural Networks (SNNs)] (https://en.wikipedia.org/wiki/Spiking_neural_network) at ease. It allows you to go from preparing your data to deploying your spiking model within minutes. SNNs are neural networks that mimics the biological brain. In the case of SNNs, the neurons accumulate the input activation until a threshold is reached, […]

deeply A library for Deep Learning Implementations and utils. Features Installation $ pip install git+https://github.com/achillesrasquinha/deeply.git Usage Application Interface >>> import deeply Command-Line Interface $ deeply Usage: deeply [OPTIONS] COMMAND [ARGS]… A Deep Learning library Options: –version Show the version and exit. -h, –help Show this message and exit. Commands: help Show this message and exit. version Show version and exit. GitHub https://github.com/achillesrasquinha/deeply    

Automatic_Background_Remover A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku. CNN Architecture – U-Net with Residual connections Parameters – 2.2M Trained on – 153,947 Images validated on – 2693 Images batch_size = 32 img_size = (256,256) Trained for – 4 epochs Training time – 80min/epoch on GPUs by Google Colab. Datasets used for training: The model is trained using modified version of U-NET (https://arxiv.org/abs/1505.04597) Architecture first presented by Olaf Ronneberger, […]

collie A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch. Collie is a library for preparing, training, and evaluating implicit deep learning hybrid recommender systems, named after the Border Collie dog breed. Collie offers a collection of simple APIs for preparing and splitting datasets, incorporating item metadata directly into a model architecture or loss, efficiently evaluating a model’s performance on the GPU, and so much more. Above all else though, Collie is built with […]

NU-Wave NU-Wave: A Diffusion Probabilistic Model for Neural Audio UpsamplingJunhyeok Lee, Seungu Han @ MINDsLab Inc., SNU Paper(arXiv): https://arxiv.org/abs/2104.02321 (Accepted to INTERSPEECH 2021)Audio Samples: https://mindslab-ai.github.io/nuwave Official Pytorch+Lightning Implementation for NU-Wave. Requirements Preprocessing Before running our project, you need to download and preprocess dataset to .pt files Download VCTK dataset Remove speaker p280 and p315 Modify path of downloaded dataset data:dir in hparameters.yaml run utils/wav2pt.py $ python utils/wav2pt.py Training Adjust hparameters.yaml, especially train section. train: batch_size: 18 # Dependent on GPU […]

beyond-image-to-depth We address the problem of estimating depth with multi modal audio visual data. Inspired by the ability of animals, such as bats and dolphins, to infer distance of objects with echolocation, we propose an end-to-end deep learning based pipeline utilizing RGB images, binaural echoes and estimated material properties of various objects within a scene for the task of depth estimation. Requirements The code is tesed with – Python 3.6 – PyTorch 1.6.0 – Numpy 1.19.5 Dataset Replica-VisualEchoes can be […]

Deep-Detail-Enhancement-for-Any-Garment This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021 Ref. to [http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/paper_docs/DeepDetailEnhance.pdf] We provide Google drive links for downloading the training data, the network checkpoint and two multi-layer garment models (Marvelouse Desigener): Training data Checkpoint MD Model ./network_train_and_run This folder contains the pytorch implemetation of deep detail enhancement network and the material classifier. In order to generalize our approach across different 2D parameterizations, we adopt a patch-based approach. Instead of operating with […]

Machine Learning Mindmap / Cheatsheet A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning. Machine Learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. Machine Learning is as fascinating as it is broad in scope. It spans over multiple fields in Mathematics, Computer Science, and Neuroscience. This is an attempt to […]