In the past few years, Neural Architecture Search (NAS) is an attractive technique that promised automatic design for high-performance neural networks. However, NAS methods considerably vary in search ...

Implement basic-to-advanced deep learning algorithms; Master the mathematics behind deep learning algorithms; Become familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, ...

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), ...

Deep learning defined. Deep learning is a form of machine learning that models patterns in data as complex, multi-layered networks. Because deep learning is the most general way to model a problem ...

Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive ...

This project presents a full-scale exploration of modern deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), ...

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically ...