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This repository is for a project that involves the construction of a basic neural network (MLP), and the performance comparison against the commonly used libraries (Py Torch and Tensorflow Keras)
The final project is mainly based on convolutional neural networks and deep learning built with TensorFlow, and developed both LSTM and shallow/deep CNN.
CIFAR-10 is an established computer-vision dataset used for object recognition. It is a subset of the 80 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes, with 6000 images per class. Using this we develop a model that can classify images
Neural networks are a type of machine learning algorithm modeled after the structure and function of the human brain. They are used for tasks such as image and speech recognition, natural language processing, and decision making. Neural networks consist of layers of interconnected nodes, called artificial neurons, that process information
This is an activity in Neural Networks and Deep Learning Models using TensorFlow and Pandas libraries in Python to preprocess datasets and create a predictive binary classifier. Topics
Used machine learning and neural networks, particularly TensorFlow, to analyze a CSV of 34,000+organizations to predict funding success, requiring skills in deep machine learning, neural networks, pandas, and Python.