Posts

Implementing the DBSCAN algorithm for clustering and outlier detection

Frequency Domain Filtering with Butterworth Filter

Implementing point operations and spatial domain filters on images

Exploring methods to reduce bias and variance in a neural network model trained on the Diabetes dataset.

Building and training a Convolutional Neural Network (CNN) on the MNIST dataset, with an exploration of different layers.

Exploring effective strategies for hyperparameter tuning to enhance the performance of neural network models using the IMDB dataset.

Implementing a neural network for image classification on the Fashion MNIST dataset to achieve accurate predictions.

Stock Market Prediction using LSTM Table of Contents Introduction Steps Step 1: Load the dataset in the notebook Step 2: Select the appropriate feature for creating the model from the training data Step 3: Normalize the features and convert it into time stamps of 60 Step 4: Reshape the data (3 D array) for applying to the LSTM model Step 5: Create a sequential LSTM model using Keras Step 6: Compile the model and train it using the training data Step 7: Predict using the test data Introduction This README provides a structured guide for implementing Stock Market Prediction using LSTM (Long Short-Term Memory) neural networks.

A structured guide for implementing Transfer Learning using the VGG 16 model. Transfer learning leverages pre-trained models like VGG 16, trained on large-scale image datasets like ImageNet, and adapts them for specific tasks with relatively smaller datasets.

An introduction to performing basic operations with Tensorflow, covering fundamental concepts and simple implementations.