Rede Neural Convolucional para reconhecimento de gestos em LIBRAS (Alfabeto) Projeto 01/2019 - Ciência da Computação (Universidade Anhembi Morumbi)
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Updated
May 18, 2020 - Python
Rede Neural Convolucional para reconhecimento de gestos em LIBRAS (Alfabeto) Projeto 01/2019 - Ciência da Computação (Universidade Anhembi Morumbi)
The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.
Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio
Avoiding the vanishing gradients problem by adding random noise and batch normalization
Deep Learning Projects
This package is a Tensorflow2/Keras implementation for Graph Attention Network embeddings and also provides a Trainable layer for Multihead Graph Attention.
Creating your own custom layers(Leaky ReLU) with Keras
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)
Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.
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.
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
Deep Learning concepts practice using Cifar-10 dataset
Advance Machine Learning (CSL 712) Course Lab Assignments
INTRODUCTION OF DEEP LEARNING
Using the features in the provided dataset, creating a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
PyTorch implementation of normalization-free LLMs investigating entropic behavior to find desirable activation functions
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