Best Computer Science podcasts — Computer theory and programming (Updated September 2017; image)
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Machine Learning
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Machine Learning
 
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Computer Systems Colloquium (Spring 2010)
 
Computer Systems Colloquium (Spring 2010)
 
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Max Seybold discusses how his most recent company, Cherrypal, has utilized new hardware and software design principles to make inexpensive and low-power consuming internet access to everyone. (June 2, 2010)By Max Seybold.
 
Carl Taussig discusses the advantages of paper-like displays and the approach that Hewlett-Packard Laboratories has taken to make them a reality for consumers. (May 19, 2010)By Carl Taussig.
 
Marc Raibert gives an overview of BigDog, a quadruped robot designed to operate in rough terrain, describes progress so far and outlines plans for what is coming next. (May 12, 2010)By Marc Raibert.
 
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Computer Systems Colloquium (Spring 2010)
 
Haiping Zhao discusses how PHP, an easy to use programming language, can be transformed into semantically equivalent C++ to solve performance problems associated with the language and speed up PHP execution. (May 5, 2010)By Haiping Zhao.
 
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Computer Systems Colloquium (Spring 2010)
 
Rob Pike discusses possible reasons why new computer languages keep appearing and why they led Google engineers to define yet another language, Go. (April 28, 2010)By Rob Pike.
 
Jeff Dozier discusses how computational modeling and new environmental technology applications can be used to reliably predict snowmelt runoff and other environmental changes due to climate change and other factors. (April 21, 2010)By Jeff Dozier.
 
Guido Jouret, CTO of the Emerging Technology Group at Cisco, Inc., discusses how video and other emerging technologies are greatly increasing network traffic and how Cisco is attempting to address this through their Medianet initiative. (April 7, 2010)By Guido Jouret.
 
Venkat Rangan, a hardware engineer at Qualcomm Incorporated, discusses hardware, software, and networking challenges that humans will face in a creating a neuromorphic computer. (March 31, 2010)By Venkat Rangan.
 
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Programming Massively Parallel Processors with CUDA
 
Michael Garland, of NVIDIA Research, discusses sorting methods in order to make searching, categorization, and building of data structures in parallel easier. (April 20, 2010)By Michael Garland.
 
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Programming Massively Parallel Processors with CUDA
 
John Nicholis discusses how to optimize with Parallel GPU Performance. (May 20, 2010)By John Nicholis.
 
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Programming Massively Parallel Processors with CUDA
 
Avi Bleiweiss delivers a lecture on the path planning system on the GPU. (May 18, 2010)By Avi Bleiweiss.
 
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Programming Massively Parallel Processors with CUDA
 
Students are taught how to effectively program massively parallel processors using the CUDA C programming language. Students also develop familiarity with the language itself and are exposed to the architecture of modern GPUs. (April 15, 2010)By Jared Hoberock.
 
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Programming Massively Parallel Processors with CUDA
 
Steven Parker, Director of High Performance Computing and Computational Graphics at NVIDIA, speaks about ray tracing. (May 11, 2010)By Steven Parker.
 
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Programming Massively Parallel Processors with CUDA
 
William Dally guest-lectures on the end of denial architecture and the rise of throughput computing. (May 13, 2010)By William Dally.
 
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Programming Massively Parallel Processors with CUDA
 
Michael C Shebanow, Principal Research Scientist with NVIDIA Research, talks about the new Fermi architecture. This next generation CUDA architecture, code named "Fermi" is the most advanced GPU computing architecture ever built. (May 6, 2010)By Michael C. Shebanow.
 
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Programming Massively Parallel Processors with CUDA
 
Jonathan Cohen, a Senior Research Scientist at NVIDIA Research, talks about solving partial differential equations with CUDA. (May 4, 2010)By Jonathan Cohen.
 
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Programming Massively Parallel Processors with CUDA
 
Nathan Bell from NVIDIA Research talks about sparse matrix-vector multiplication on throughput-oriented processors. (April 29, 2010)By Nathan Bell.
 
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Programming Massively Parallel Processors with CUDA
 
Nathan Bell of NVIDIA Research talks about Thrust, a productivity library for CUDA. (April 27, 2010)By Nathan Bell.
 
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Programming Massively Parallel Processors with CUDA
 
David Tarjan continues his discussion on parallel patterns. (April 22, 2010)By David Tarjan.
 
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Programming Massively Parallel Processors with CUDA
 
Lukas Biewald of Delores Labs, discusses performance considerations including: memory coalescing, shared memory bank conflicts, control-flow divergence, occupancy, and kernel launch overheads. (April 13, 2010)By Lukas Biewald.
 
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Programming Massively Parallel Processors with CUDA
 
Jared Hoberock of NVIDIA lectures on CUDA memory spaces for CS 193G: Programming Massively Parallel Processors. (April 8, 2010)By Jared Hoberock.
 
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Programming Massively Parallel Processors with CUDA
 
Atomic operations in CUDA and the associated hardware are discussed. (April 6, 2010)By David Tarjan.
 
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Programming Massively Parallel Processors with CUDA
 
science, technology, computer science, CS, software engineering, programming, parallel processors, CUDA, language, code, Computers, coding, MP0, MP1, hardware, software, memory management, GPU, CPU, memory, parallel code, kernel, threads, launch, thread bBy David Tarjan.
 
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Programming Massively Parallel Processors with CUDA
 
Jared Hoberock of NVIDIA gives the introductory lecture to CS 193G: Programming Massively Parallel Processors. (March 30, 2010)By Jared Hoberock.
 
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Machine Learning
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.By Andrew Ng.
 
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Machine Learning
 
science, math, engineering, computer, technology, robotics, algebra, locally, weighted, logistic, regression, linear, probabilistic, interpretation, Gaussian, distribution, digression, perceptronBy Andrew Ng.
 
M
Machine Learning
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning.By Andrew Ng.
 
M
Machine Learning
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning.By Andrew Ng.
 
M
Machine Learning
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine.By Andrew Ng.
 
M
Machine Learning
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals.By Andrew Ng.
 
M
Machine Learning
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs and discretization.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses state action rewards and linear dynamical systems in the context of linear quadratic regulation.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on the debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian.By Andrew Ng.
 
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning.By Andrew Ng.
 
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