This repository contains my learning, practice, and experimentation with Deep Learning.
The files here are not a single project. They represent the concepts, maths behind model, datasets-work and techniques I have been learning and implementing throughout my Deep Learning journey.
- Neural Networks
- Loss, Activation, Sigmoid, relu Function
- Recall, Precision, Accuracy score, confusion matrix, r2_score, mse
- Forward Propagation, Backpropagation
- Convolutional Neural Networks (CNNs)
- Training, Testing, Spliting, Predictions
- Dropout, EarlyStopping, Scalling
- Working with Datasets
- Data Visualization
- Model evaluation, experimentation
This repository will continue to grow as I learn new concepts and apply them through code.
Learning → Implementing → Experimenting → Improving