Deep learning notes and sources

• Keras Tutorial: Deep Learning in Python https://www.datacamp.com/tutorial/deep-learning-python

• An overview of gradient descent optimization algorithms https://ruder.io/optimizing-gradient-descent/index.html

• Hidden Layer: Choosing number of Hidden Layers and number of hidden neurons in Neural Networks https://www.linkedin.com/pulse/choosing-number-hidden-layers-neurons-neural-networks-sachdev/

• Activation: How to Choose an Activation Function for Deep Learning https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning/

• loss: How to Choose Loss Functions When Training Deep Learning Neural Networks https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/

• metrics: Choosing Classification metrics based on True/False positives & negative https://keras.io/api/metrics/classification_metrics/

• Brief information: How Deep Learning Works with Different Neuron Layers https://data-flair.training/blogs/how-deep-learning-works/

• Recurrent Neural Networks (RNN) : LSTM RNN in Keras: Examples of One-to-Many, Many-to-One & Many-to-Many https://wandb.ai/ayush-thakur/dl-question-bank/reports/LSTM-RNN-in-Keras-Examples-of-One-to-Many-Many-to-One-Many-to-Many---VmlldzoyMDIzOTM