- Cell Structure and Viruses
- Enzyme Activity and Cell Respiration
- Cell Cycle, Reproduction and Embryology
- Genetics
- Nervous and Musculoskeletal System
- Endocrine System
- Digestive and Excretory Systems
- Cardiovascular and Respiratory Systems
- Immune System
Google Spotlight Stories is a new form of immersive storytelling made specifically for mobile and VR. In these 360-degree, interactive stories, your phone be...
Use a computer? Game on a PC? Ever wonder how those graphics get so pretty? Let's go inside your high-end graphics card with this animation. Subscribe for mo...
Spinning objects have strange instabilities known as The Dzhanibekov Effect or Tennis Racket Theorem - this video offers an intuitive explanation. Part of th...
This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras.
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- ARM Research
- Hound: Causal Learning for Datacenter-scale Straggler Diagnosis
- Adaptive Resource Management for Mobile CMPs through Self-awareness
- On-the-fly deterministic binary filters and other on-going work in Machine Learning Systems
- Managed Modularity for Deep Neural Networks