Detect file content types with deep learning
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Updated
Aug 4, 2025 - Python
Detect file content types with deep learning
Collection of Keras models used for classification
Keras implementation of a ResNet-CAM model
Distributed Keras Engine, Make Keras faster with only one line of code.
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.
Object classification with CIFAR-10 using transfer learning
Códigos Python com diferentes aplica??es como técnicas de machine learning e deep learning, fundamentos de estatística, problemas de regress?o de classifica??o. Os vídeos com as explica??es teóricas est?o disponíveis no meu canal do YouTube
Classify movie posters by genre
We pit Keras and PyTorch against each other, showing their strengths and weaknesses in action. We present a real problem, a matter of life-and-death: distinguishing Aliens from Predators!
Implemented two papers for offline signature verification. Both use different deep learning techniques - Convolutional network and Siamese network.
Classifying 10 different categories of Sound using Deep Learning.
Source code for the paper "Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches".
Make a graph network of your followers. Based on username and gender
Source code for the paper "Color-aware two-branch DCNN for efficient plant disease classification".
Classification of Time-series data with RNN
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch
Multiple Handwritten Digit Recognition app Using Deep Learing - CNN from Canvas build on tkinter- GUI
RNN classifier built with Keras to classify MNIST dataset
Dataset + convolutional neural network for recognizing Italian Sign Language (LIS) fingerspelling gestures
Identify and classify objects in real-time video streams using TensorFlow and OpenCV. This project is designed for applications like security systems, robotics, and interactive installations, combining the power of TensorFlow for deep learning with OpenCV's webcam interaction.
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