Chapters

In this topic, we give breief introductory about representation learning, single neuron, XOR Problem, single hidden layer as well as multi-layer neural networks. Moreover, we discuss the multiclass classification, matrix notation and universal approximation.

This chapter describes computational graph, basic training of neural networks and backpropagation.

This chapter introduces the concept of regularization and discusses common regularization techniques in more depth.

This chapter introduce advanced techniques for optimization of neural networks and deep learning.

This chapter introduce –convolution neural networks (CNNs)– one of the most popular component of deep learning architecture. CNNs are widely applied in other domains such as natural language processing (NLP), audio, and time-series data. In this part, we introduce the CNNs, properties and component of CNN, differences between CNN and FCN as well as math behind the CNNs.

This chapter introduces recent popular convolutional neural network architecture.

We explain another popular family of neural network which made many success for NLP data.

This chapter introduces modern recurrent neural networks.

This chapter first defines the untouched-test-set principle and proceeds to explain the concepts of train-validation-test split and nested resampling.

This chapter introduces GANs, application of deep generative models and challenges in optimization.