- 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
- Atomic Structure and Molecules
- Kinetic Molecular Theory and Chemical Reactions
- Thermodynamics
- Calorimetry and Colligative Properties
- Solutions, Solvents and Solubility
- Acid and Base Reactions
- Electrochemistry
- Special Relativity
- Quantum Theory
- Quantum Mechanics
- Quantum-Mechanical Theory of Atoms
- Chemical Bonds and Solid-State Physics
- Nuclear Physics
- Particle Physics
- 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
- survey several important computational problems for which the traditional worst-case analysis of algorithms is ill-suited
- study systematically alternatives to worst-case analysis
This course covers the design and implementation of distributed systems. Students will gain an understanding of the principles and techniques behind the design of modern, reliable, and high-performance distributed systems. Topics include server design, network programming, naming, concurrency and locking, consistency models and techniques, security, and fault tolerance. Modern techniques and systems employed at some of the largest Internet sites (e.g., Google, Facebook, Amazon) will also be covered. Through programming assignments, students will gain practical experience designing, implementing, and debugging real distributed systems.
A paper by DeepMind scientist triggered much debate about the path to artificial intelligence. Here, we'll try to draw the line between theory and practice.
This archive holds videos of past Fields lectures. Lectures are archived in two formats.The interactive format, viewed in a flash-player-enabled desktop web browser, allows you to zoom in and out on specific areas of the blackboards or screens (providing a viewing experience more like being present in the room). The static format, although it does not allow for zooming in to read small blackboard writing, is downloadable and compatible with a wide variety of desktop and mobile video players.
Learn to make the most of the tools that hackers have been using for decades.
As hackers, we spend a lot of time on our computers, so it makes sense to make that experience as fluid and frictionless as possible. In this class, we’ll help you learn how to make the most of tools that productive programmers use.
We’ll show you how to navigate the command line, use a powerful text editor, use version control efficiently, automate mundane tasks, manage packages and software, configure your desktop environment, and more.
The textbook An Introduction to the Analysis of Algorithms by Robert Sedgewick and Phillipe Flajolet overviews the primary techniques used in the mathematical analysis of algorithms.
The textbook Analytic Combinatorics by Philippe Flajolet and Robert Sedgewick enables precise quantitative predictions of the properties of large combinatorial structures.
The PUNLAG seminar is intended to supplement the numerical linear algebra course sequence at Purdue. The standard course CS515 doesn't have room for a number of interesting problems -- we hope to cover some in this seminar!
Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 Notebook: http://deeplearning.cs.cmu.edu/document/recitation/recitation...
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.
Spinning objects have strange instabilities known as The Dzhanibekov Effect or Tennis Racket Theorem - this video offers an intuitive explanation. Part of th...