Stanford University – Network Security (XAGS255) – Ray314
English | Size: 1.25 GB
Category: Tutorial
Network security is one of the most important computer science issues today. [Read more…]
Stanford University – Network Security (XAGS255) – Ray314
Peter Thiel – Returns to Stanford to Share Business Tips from Zero to One
Peter Thiel – Returns to Stanford to Share Business Tips from “Zero to One”
English | Size: 422.96 MB
Category: Tutorial
Peter Thiel Returns to Stanford to Share Business Tips from “Zero to One”
Venture capitalist Peter Thiel, JD ’92, founded PayPal and was the first major investor in Facebook, shared business tips from his new book, Zero to One: Notes on Startups, or How to Build the Future, with students and alumni at Stanford on Sept. 29, 2014. He also recalled his days at Stanford as an undergraduate and law student. [Read more…]
Stanford Natural Language Processing with Deep Learning 2017
Stanford Natural Language Processing with Deep Learning 2017
English | Size: 7.32 GB
Category: Tutorial
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering.
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Introduction to Mathematical Thinking – Stanford University | Coursera
Introduction to Mathematical Thinking – Stanford University | Coursera
English | Size: 1.77 GB (1,902,737,093 Bytes)
Category: CBTs
Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years.
The goal of the course is to help you develop a valuable mental ability – a powerful way of thinking that our ancests have developed over three thousand years.
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Quantum Mechanics for Scientists and Engineers (Stanford, 2015)
Quantum Mechanics for Scientists and Engineers (Stanford, 2015)
English | Size: 6.59 GB (7,073,526,180 Bytes)
Category: CBTs
Complete course:
– videos;
– subs (separate files);
– slides (separate pdf files);
– quizzes;
– answers for quizzes;
– reference.
[Read more…]
Coursera – Stanford – Algorithms, Design and Analysis, Part 2 [104 Webrips (mp4)]
Coursera – Stanford – Algorithms, Design and Analysis, Part 2 [104 Webrips (mp4)]
English | Size: 1.60 GB (1,720,984,198 bytes)
Category: Theory / Reference / Doc
In this course you will learn several fundamental principles of advanced algorithm design. You’ll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You’ll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments. You’ll learn what NP-completeness and the famous “P vs. NP” problem mean for the algorithm designer. Finally, we’ll study several strategies for dealing with hard (i.e., NP-complete problems), including the design and analysis of heuristics. Learn how shortest-path algorithms from the 1950s (i.e., pre-ARPANET!) govern the way that your Internet traffic gets routed today; why efficient algorithms are fundamental to modern genomics; and how to make a million bucks in prize money by “just” solving a math problem!
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Coursera – Stanford – Algorithms, Design and Analysis, Part 1 [82 Webrips (mp4)]
Coursera – Stanford – Algorithms, Design and Analysis, Part 1 [82 Webrips (mp4)]
English | Size: 1.34 GB (1,439,999,036 bytes)
Category: Theory / Reference / Doc
In this course you will learn several fundamental principles of algorithm design. You’ll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You’ll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we’ll study how allowing the computer to “flip coins” can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?
[Read more…]