Deep neural networks topic
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing.
sednn
deep learning based speech enhancement using keras or pytorch, make it easy to use
speech_dataset
The dataset of Speech Recognition
NeuroNLP2
Deep neural models for core NLP tasks (Pytorch version)
distiller
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
neupy
NeuPy is a Tensorflow based python library for prototyping and building neural networks
signatory
Differentiable computations of the signature and logsignature transforms, on both CPU and GPU. (ICLR 2021)
Tracking-with-darkflow
Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow
flow-forecast
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
SCINet
The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)
Deep-Learning-for-Time-Series-Forecasting
This repository is designed to teach you, step-by-step, how to develop deep learning methods for time series forecasting with concrete and executable examples in Python.