Upcoming SciNet Events

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February,2020
Wed 19th Feb
1:00 pm
4:00 pm
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Learn how to write bash scripts, use environment variables, how to control process, and much more. Requires some linux basic command line experience. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 25th Feb
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 26th Feb
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 27th Feb
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 28th Feb
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
March,2020
Tue 3rd Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 4th Mar
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 5th Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 6th Mar
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Tue 10th Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 11th Mar
10:00 am
11:30 am
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A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 11th Mar
11:00 am
12:00 pm
Add event to google
In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Wed 11th Mar
12:00 pm
1:00 pm
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Monthly user meeting at SciNet with pizza, a techtalk (TBA) and user discussion. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 12th Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 13th Mar
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Tue 17th Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 18th Mar
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Wed 18th Mar
1:00 pm
4:00 pm
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Working with many of the HPC systems in Ontario involves using the Linux command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with basic commands in the Linux environment. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 19th Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 20th Mar
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Mon 23rd Mar
1:00 pm
4:00 pm
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A half day session on profiling, performance analysis, and tuning of applications. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 24th Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 25th Mar
11:00 am
12:00 pm
Add event to google
In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 140 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 26th Mar
11:00 am
12:00 pm
Add event to google
This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 27th Mar
11:00 am
12:00 pm
Add event to google
In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 160 (UTSC)
Show in Google map
Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Tue 31st Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
April,2020
Wed 1st Apr
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 140 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Thu 2nd Apr
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Part of Scientific Computing for Physicists, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Fri 3rd Apr
11:00 am
12:00 pm
Add event to google
In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: MW 160 (UTSC)
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Social Sciences Building @ UTSC, Scarborough, M1C 1A4, Canada
Mon 6th Apr
1:00 pm
4:00 pm
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In this three-hour workshop, you will learn advanced MPI techniques such as MPI Datatypes, MPI-IO and one-sided communications. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th Apr
10:00 am
11:30 am
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A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th Apr
12:00 pm
1:00 pm
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Monthly user meeting at SciNet with pizza, a techtalk (TBA) and user discussion. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 14th Apr
10:00 am
11:00 am
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 14th Apr
1:00 pm
2:00 pm
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This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 15th Apr
1:00 pm
4:00 pm
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Increase you Linux (bash) command line productivity. Requires some basic Linux command line experience. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 16th Apr
10:00 am
11:00 am
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 16th Apr
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 21st Apr
10:00 am
11:00 am
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 21st Apr
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 23rd Apr
10:00 am
11:00 am
Add event to google
This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 23rd Apr
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 28th Apr
10:00 am
11:00 am
Add event to google
This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 28th Apr
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 30th Apr
10:00 am
11:00 am
Add event to google
This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 30th Apr
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
May,2020
Mon 4th May
1:00 pm
4:00 pm
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Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 5th May
10:00 am
11:00 am
Add event to google
This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 5th May
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 7th May
10:00 am
11:00 am
Add event to google
This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 7th May
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 12th May
10:00 am
11:00 am
Add event to google
This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 12th May
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 13th May
10:00 am
11:30 am
Add event to google
A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 13th May
12:00 pm
1:00 pm
Add event to google
Monthly user meeting at SciNet with pizza, a techtalk (TBA) and user discussion. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Thu 14th May
10:00 am
11:00 am
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 14th May
1:00 pm
2:00 pm
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This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 19th May
10:00 am
11:00 am
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Tue 19th May
1:00 pm
2:00 pm
Add event to google
This course is an introductory course in programming utilizing the R Statistical Language.The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. Part of BCH2024H Introduction to Programming with R, Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada
Wed 20th May
1:00 pm
4:00 pm
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Working with many of the HPC systems in Ontario involves using the Linux command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with basic commands in the Linux environment. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 21st May
10:00 am
11:00 am
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This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
Show in Google map
661 University Ave., Toronto, M5G 1M1, Canada