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Lec 1 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008

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Lec 1 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008

Lecture 1: Goals of the course; what is computation; introduction to data types, operators, and variables Instructors: Prof. Eric Grimson, Prof. John Guttag View the complete course at: http://ocw.mit.edu/6-00F08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

6.6M viewsAug 20, 2009

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Lec 01. Introduction to Deep Learning

Lec 01. Introduction to Deep Learning

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MIT OpenCourseWare Site Tour

MIT OpenCourseWare Site Tour

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Lecture 1, Part I: Introduction of the Class

Lecture 1, Part I: Introduction of the Class

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267.4K views9 months ago

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Lec 1 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011

Lec 1 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011

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Lec 1 | MIT 6.042J Mathematics for Computer Science, Fall 2010

Lec 1 | MIT 6.042J Mathematics for Computer Science, Fall 2010

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Lecture 1: Predicates, Sets, and Proofs

Lecture 1: Predicates, Sets, and Proofs

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Lecture 1: Introduction to Individual Decision-Making

Lecture 1: Introduction to Individual Decision-Making

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Lecture 1, Part II: Introduction of Financial Markets, Financial Terms and Concepts

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Lec 1 | MIT 18.01 Single Variable Calculus, Fall 2007

Lec 1 | MIT 18.01 Single Variable Calculus, Fall 2007

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Lec 1 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011

Lec 1 | MIT 6.00SC Introduction to Computer Science and Programming, Spring 2011

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Lec 1 | MIT 6.042J Mathematics for Computer Science, Fall 2010

Lec 1 | MIT 6.042J Mathematics for Computer Science, Fall 2010

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Lecture 1: Predicates, Sets, and Proofs

Lecture 1: Predicates, Sets, and Proofs

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3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch

3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch

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Lecture 2: Contradiction and Induction

Lecture 2: Contradiction and Induction

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Lecture 3: Casework and Strong Induction

Lecture 3: Casework and Strong Induction

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1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

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Class 1 Video: How Do Computers "Hear" Music?

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Misinformation, AI, & Science Photography

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