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

Logistic Regression Building your Deep Neural Network Deep Neural Network Application Initialization Regularization Gradient Checking Optimization methods Tensorflow Tutorial Convolution model Convolution application Keras tutorial Residual network Recurrent neural network