Deep Learning concepts practice using Cifar-10 dataset
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Updated
Jan 31, 2022 - Jupyter Notebook
Deep Learning concepts practice using Cifar-10 dataset
Creating your own custom layers(Leaky ReLU) with Keras
Deep Learning Projects
This repository summarizes the basic concepts, types and usage scenarios of activation functions in deep learning.
Image Classification, Python, Feedforward-Neural-Network, Tensorflow, Keras, CNN, ReLu
Building Generative Adversarial Networks
Neural Network implemented with different Activation Functions i.e, sigmoid, relu, leaky-relu, softmax and different Optimizers i.e, Gradient Descent, AdaGrad, RMSProp, Adam. You can choose different loss functions as well i.e, cross-entropy loss, hinge-loss, mean squared error (MSE)
Advance Machine Learning (CSL 712) Course Lab Assignments
This package is a Tensorflow2/Keras implementation for Graph Attention Network embeddings and also provides a Trainable layer for Multihead Graph Attention.
Using the features in the provided dataset, creating a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
The code implements a neural network model, PricePredictor, trained on historical stock price data to predict future stock prices, visualizing the predictions alongside historical prices and calculating the average of the predicted prices.
Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.
INTRODUCTION OF DEEP LEARNING
Jupyter notebooks to create random file transfer data on an ElasticSearch Cluster in order to train a neural network to predict the file transfer duration.
CNeuron is a simple singular neural network neuron implementation in C, designed for easy integration into C projects.
Deep Learning concepts practice using Cifar-10 dataset
Avoiding the vanishing gradients problem by adding random noise and batch normalization
Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio
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