Table of Contents
The 2D convolution is a fairly simple operation at heart: you start with a kernel, which is simply a small matrix of weights.
This kernel “slides” over the 2D input data, performing an elementwise multiplication with the part of the input it is currently on, and then summing up the results into a single output pixel.
Gender Classification using CNN with PyTorch with Accuracy : 96%
Notebook (gender-classification-pytorch-CNN-project.ipynb) Jovian Link : https://jovian.ai/sanketec87/gender-classification-pytorch-cnn-project
The dataset is of cropped images of male and female. It is split into training and validation directory. Training contains ~23,000 images of each class and the validation di
rectory contains ~5,500 images of each class.
Dataset is from Kaggle. Link => https://www.kaggle.com/cashutosh/gender-classification-dataset
Read more about CNN:
https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1
https://en.wikipedia.org/wiki/Convolutional_neural_network
Famous Fashion MNIST Dataset Analysis using Deep learning Keras.
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.
Location for Dataset 'diabetes.csv' : https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database?resource=download