Preface Section 1" Getting Started with Deep Learning Chapter 1: Introduction to Deep Learning What is deep learning? Biological and artificial neurons ANN and its layers Input layer Hidden layer Output layer Exploring activation functions The sigmoid function The tanh function The Rectified Linear Unit function The leaky ReLU function The Exponential linear unit function The Swish function The softmax function Forward propagation in ANN How does ANN learn? Debugging gradient descent with gradient checking Putting it all together Building a neural network from scratch Summary Questions Further reading Chapter 2: Getting to Know TensorFIow What is TensorFIow? Understanding computational graphs and sessions Sessions Variables, constants, and placeholders Variables Constants Placeholders and feed dictionaries Introducing TensorBoard Creating a name scope Handwritten digit classification using TensorFIow Importing the required libraries Loading the dataset Defining the number of neurons in each layer Defining placeholders Forward propagation Computing loss and backpropagation Computing accuracy Creating summary Training the model Visualizing graphs in TensorBoard Introducing eager execution Math operations in TensorFIow TensorFIow 2.0 and Keras Bonjour Keras Defining the model Defining a sequential model Defining a functional model Compiling the model Training the model Evaluating the model MNIST digit classification using TensorFIow 2.0 Should we use Keras or TensorFIow? Summary Questions Further reading Section 2: Fundamental Deep Learning Algorithms Chapter 3: Gradient Descent and Its Variants Demystifying gradient descent Performing gradient descent in regression " Importing the libraries Preparing the dataset Defining the loss function Computing the gradients of the loss function Updating the model parameters Gradient descent versus stochastic gradient descent Momentum-based gradient descent Gradient descent with momentum Nesterov accelerated gradient Adaptive methods of gradient descent Setting a learning rate adaptively using Adagrad Doing away with the learning rate using Adadelta Overcoming the limitations of Adagrad using RMSProp Adaptive moment estimation Adamax - Adam based on infinity-norm Adaptive moment estimation with AMSGrad …… Section 3 Advanced Deep Learning Algorithms
