This video introduces the idea of a convolution and explains why convolutional layers can be preferable to fully connected layers on certain types of data
Part 3: Activation and Loss Functions
There are two missing piece of neural networks that we haven’t yet talked about: activation functions and loss functions. This video explores both in detail.
Part 4: Hyperparameters and Dropout
This video introduces some important hyperparameters before going into detail about Dropout, one of the most important hyperparameters to protect against overfitting
Part 5: Batch Normalisation and Convolution Details
This video explores the need to normalise at different layers of your network, before explaining some of the finer points of convolutional layers
Part 6: Data Augmentation
Deep Neural Networks are hungry for data. This video describes how to make the most out of the available data to improve your performance
Part 7: Putting it all Together
This video takes the concepts introduced over the last two lectures and demonstrates how to combine them into an easy to code Keras model