Logistic Regression Neural Network overview Gradient Descent and Back Propagation Deep Neural Networks

Bias and variance, regularization, normalization Gradient checking Optimization algorithms Hyperparameter tuning Batch normalization

Multi class classification, ML strategy Transfer, multi task, end-to-end learning

Convolution, padding, stride Convolution over volumes Pooling, why convolution Classic networks Transfer learning Residual networks Object localization and detection

Code