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Hand Gesture Recognition and Modification was based on transfer learning Inception v3 model using Keras with Tensorflow backend trained on 4 classes - rock, paper, scissors, and nothing hand signs. The final trained model resulted in an accuracy of 97.05%. The model was deployed using Streamlit on Heroku Paas.
EEG data collection and processing in matlab. Proposed data collection algorithm and Processing pipeline for evoked potentials of EEG signals or regular EEG signals. Furthermore
This repository includes my Chest X-Ray Deep Learning-Flatiron School Module 4 Project. For this project, I made use of OS to access the data. The Pandas, NumPy, Matplotlib, Seaborn, and Plotly libraries were used to explore the data. Keras was used to build the image classifier.
The main concentration of this project lies on image calssification using traditional CNN(Convolution Neural Networks), and also a couple of "BASE MODELS" such as "RestNet50", "DenseNet121" and "EfficientNetB0" that upgraded the performance of our CNN, followed by the Fully Connected NN, that we are using to train our model on.
As the learning rate is one of the most important hyper-parameters to tune for training convolutional neural networks. In this paper, a powerful technique to select a range of learning rates for a neural network that named cyclical learning rate was implemented with two different skewness degrees. It is an approach to adjust where the value is c…
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates …