Binary and Categorical Focal loss implementation in Keras.
-
Updated
Dec 20, 2024 - Python
Binary and Categorical Focal loss implementation in Keras.
A loss function for categories with a hierarchical structure.
Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images
Two ensemble models made from ensembles of LightGBM and CNN for a multiclass classification problem.
This project is about building a artificial neural network using pytorch library. I am sharing the code and output for my project.
Computer Vision and Deep Learning
Real-time driver distraction detection using time-distributed convolutional LSTM network for mobile platforms
A CNN Architecture classifies 14 kinds of automobile parts.
A deep learning project based on TensorFlow that recognizes color patterns of brick.
Lightweight neural network library written in ANSI-C supporting prediction and backpropagation for Convolutional- and Fully Connected neural networks
This script trains a convolutional neural network (CNN) to classify handwritten digits.
A neural network model based on TensorFlow that predicts shape of brick
Deep Learning Nanodegree Project : Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied with an image of a human, the code will identify the resembling dog breed.
Detecting Pneumonia in Chest X-ray Images using CNNs and Pre-trained Models in Tensorflow
Understanding the performance of different neural network architectures on the MNIST handwritten digits dataset, implemented in Tensorflow.
Transform TV control with Gesture Recognition! Enable intuitive interaction with smart TVs using gestures built using Conv3D, CNN & RNN
AIND Jupyter Notebook to predict student admissions using Keras Neural Networks
Applying Categorical Exploratory Data Analysis (CEDA) methods to study audio quality perception
Add a description, image, and links to the categorical-cross-entropy topic page so that developers can more easily learn about it.
To associate your repository with the categorical-cross-entropy topic, visit your repo's landing page and select "manage topics."