Neural networks
-
Updated
May 26, 2022 - Python
Neural networks
Deep learning Simple models
Uses deep learning in Python with Keras, Pandas, Numpy, Tensorflow, ScIkit-Learn libraries.
batch_normalization through PyTorch
Demonstrate how to do backpropagation using an example of BatchNorm-Sigmoid-MSELoss network with a detailed derivation of gradients and custom implementations.
Deep Learning models
ba
Skin cancer can be broadly classified into two major categories: Melanoma (Malignant) and non-melanoma (Benign). Melanoma is one of the deadliest kinds of cancer. However, the detection of this cancer at an early stage can help in improving the chances of survival.
Developing deep learning models with only numpy and panda and without using high level libraries such as Tensorflow, Keras and PyTorch
Model Optimization using Batch Normalization and Dropout Techniques
Exoplanet Hunting in Deep Space.
Image classifiaction done on cifar 10 using deep learning (CNN)
Fundamentals of Artificial Intelligence and Deep Learning Frameworks
This repository contains my project for computer vision.
Charts and Fake image (OCR on Charts)
As part of a bigger work, this work focuses on implementing MLPs and Batch Normalization with Numpy and Python only.
Batch normalization from scratch on LeNet using tensorflow.keras on mnist dataset. The goal is to learn and characterize batch normalization's impact on the NN performance.
Demo on performing multiclass image classification using Convolutional Neural Network (CNN) in Tensorflow 2. Techniques such as earlystopping, batchnormalizing and dropout are explored to prevent overfitting
Developed CNN model with 93% validation accuracy using techniques like Dropouts & Batch Normalization. Using haar cascade of Computer Vision It is then used to detect sunglasses on real time basis
Add a description, image, and links to the batchnormalization topic page so that developers can more easily learn about it.
To associate your repository with the batchnormalization topic, visit your repo's landing page and select "manage topics."