Mouneshgouda/Deep-learning

This repository contains Jupyter Notebooks showcasing different techniques for price prediction using Artificial Neural Networks (ANNs). Topics covered include early stopping, batch normalization, dropout regularization, and deep neural networks. These notebooks offer practical implementations and insights for improving model training

https://medium.com/analytics-vidhya/activation-functions-all-you-need-to-know-355a850d025e

Cnn:- https://medium.com/@mouneshpatil001/e2b458307dfb

Data Augumentation:

https://medium.com/analytics-vidhya/data-augmentation-techniques-using-opencv-657bcb9cc30b

https://research.aimultiple.com/data-augmentation-techniques/

Optimization:- https://towardsdatascience.com/deep-learning-optimizers-436171c9e23f

https://www.codingninjas.com/studio/library/nesterov-accelerated-gradient

Hyperparameter:

https://medium.com/analytics-vidhya/hyperparameter-tuning-using-keras-tuner-72a8cb394d0f

Pre-traineed Model:-https://medium.com/@mouneshpatil001/transfer-learning-using-pre-trained-models-for-image-classification-resnet50-in-keras-b96967c5f124

Lenet https://medium.com/codex/lenet-5-complete-architecture-84c6d08215f9