Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
-
Updated
Jan 13, 2024 - Python
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
A Keras port of Single Shot MultiBox Detector
My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano
Detect file content types with deep learning
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
PyTorch to Keras model convertor
An open-source platform for automating tasks using machine learning models
To classify video into various classes using keras library with tensorflow as back-end.
K-CAI NEURAL API - Keras based neural network API that will allow you to create parameter-efficient, memory-efficient, flops-efficient multipath models with new layer types. There are plenty of examples and documentation.
Collection of Keras models used for classification
Keras implementation of a ResNet-CAM model
Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis".
Serving a keras model (neural networks) in a website with the python Django-REST framework.
Extracting relevant information from resume using deep learning.
Simple keras chat bot using seq2seq model with Flask serving web
A very simple project to create a rest backend for serving a neural network model based on keras
Bidirectional Attention Flow for Machine Comprehension implemented in Keras 2
A tutorial exploring multiple approaches to deploy a trained TensorFlow (or Keras) model or multiple models for prediction.
Extending Keras to support tfrecord dataset
This repository will contain the example detailed codes of Tensorflow and Keras, This repository will be useful for Deep Learning staters who find difficult to understand the example codes
Add a description, image, and links to the keras-models topic page so that developers can more easily learn about it.
To associate your repository with the keras-models topic, visit your repo's landing page and select "manage topics."