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Comps Lessons With Transcripts
YUM E DNF NO LINUX - COMO ATUALIZAR PACOTES RPM NO CENTOS, FEDORA E DISTROS REDHAT TUTORIAL
Playlist Curso de LINUX na Prática https://www.youtube.com/watch?v=hBFdhRPRLwE&list=PL5EmR7zuTn_bZm3-kGdVl6Av0u2pKpLEf Vamos ver os comandos YUM e DNF...
Curso de LINUX na PráticaAlgorithms for Big Data (COMPSCI 229r), Lecture 25
MapReduce: TeraSort, minimum spanning tree, triangle counting.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 24
Competitive paging, cache-oblivious algorithms: matrix multiplication, self-organizing linked list, static B-tree, lazy funnelsort.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 23
External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 22
Matrix completion.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 21
ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 20
Krahmer-Ward proof, Iterative Hard Thresholding.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 19
RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 18
Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 17
Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 16
Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace embeddings.
Algorithms for Big Data (COMPSCI 229r)Algorithms for Big Data (COMPSCI 229r), Lecture 15
Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
Algorithms for Big Data (COMPSCI 229r)Comps Key Concepts
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