PowerPoint 2010 Tip: Removing Speakers Notes / Comments / Annotations

Here is a quick tip on how to easily remove speakers notes in Powerpoint. This new feature was not available in previous versions (maybe in 2007). Back then, each note had to be removed manually.

Here are the 5 easy steps to removing speakers notes:

1. Open a copy of your presentation in PowerPoint 2010 so that you don't loose your original version that includes the notes.
2. Click on the File tab in the top left corner and then on Info.
3. Click on the Check for Issues button and then on Inspect Document
4. Make sure the Comments and Annotations and the Presentation Notes boxes are checked and click Inspect.
5. If there are any comments, annotations or notes, clicking the Remove All buttons will remove them all.

Don't forget to save your presentation under a different name if you want to also keep copy containing your comments, annotations and notes.

Grad Req

Major Course Requirements for  Dept. of School of Computing (For Master’s Program)

Thesis Mater’s Degree Program

Please check the common graduation requirements.  ▣ Credit Requirement for Graduation: Required to complete a total of more than 33 credits  ▣ Mandatory General Courses: 3 credits and 1AU - Take 1 course from the following courses: Scientific Writing (CC500), Probability and Statistics (CC511), Introduction to Materials Science and Engineering (CC512), Engineering Economy and Cost Analysis (CC513), Introduction to Instruments (CC522), Entrepreneurship and Business Strategies (CC530), Patent Analysis and Invention Disclosure (CC531), and Collaborative System Design and Engineering (CC532). - CC010 Special Lecture on Leadership (non-credit, this applies to students entering KAIST in 2002 and thereafter; general scholarship students, foreign students are excluded) - CC020 Ethics and Safety I (1AU) ▣ Mandatory Major Courses: none  ▣ Elective Courses: at least 18 credits  - Essential Courses (at least 9 credits): Take one course from each of the following three areas. Theory: Design and Analysis of Algorithms, Theory of Programming Languages, Theory of Formal Languages and Automata, Computational Geometry, Computational Linguistics.  Software: Theory of Programming Languages, Software Engineering, Database System, Database Design (For Database System and Database Design, only one of the courses will be accepted to satisfy the course requirements.), Artificial Intelligence and Machine Learning, Intelligent Robotics, Natural Language Processing I, Computer Vision, Computer Graphics, Semantic Web, Models of Software Systems, Designs for Software and Systems. Computer Systems: Computer Architecture, Operating System, Network Architecture, Internet Systems Technology, Advanced Information Security, Wireless Mobile Internet, Distributed Systems - Elective Courses (at least 9 credits): 3 credits must be from the courses offered by the School of Computing (CSXXX). (Mandatory general courses are not accepted as elective courses.) ▣ Research Courses: minimum 6 credits. (2 credits from seminar courses are required and can be credited towards research credits.) ▣ Miscellaneous: Up to 9 credits from 500-level courses taken as an undergraduate at KAIST are acknowledged.

Coursework Master's Degree Program

Please check the common graduation requirements.  ▣ Credit Requirement for Graduation: Required to complete a total of more than 33 credits  ▣ Mandatory General Courses: 3 credits and 1AU - Take 1 course from the following courses: Scientific Writing (CC500), Probability and Statistics (CC511), Introduction to Materials Science and Engineering (CC512), Engineering Economy and Cost Analysis (CC513), Introduction to Instruments (CC522), Entrepreneurship and Business Strategies (CC530), Patent Analysis and Invention Disclosure (CC531), and Collaborative System Design and Engineering (CC532). - CC010 Special Lecture on Leadership (non-credit, this applies to students entering KAIST in 2002 and thereafter; general scholarship students, foreign students are excluded) - CC020 Ethics and Safety I (1AU) ▣ Mandatory Major Courses: none  ▣ Elective Courses: at least 27 credits - Essential Courses (at least 9 credits): Same as Thesis Master's program requirement. - Elective Courses (at least 18 credits): 12 credits must be from the courses offered by the School of Computing (CSXXX). (Mandatory general courses are not accepted as elective courses.) ▣ Research Courses: minimum 3 credits. (Credits from individual study are required. In addition, 2 credits from seminar courses are required and can be credited towards research credits.)  ▣ Miscellaneous: Up to 9 credits from 500-level courses taken as an undergraduate at KAIST are acknowledged.  ▣ GPA must be over 3.0.

□ Transitional Measures - Students who entered in 2017 or later should fulfill the current degree requirements. However, students who entered in 2016 or before can choose to fulfill the current 2017 degree requirements. ※ Students who entered in 2016 or before can choose to apply Theory of Programming Languages (CS520) to one of the Theory area or Software area. - Students who entered in 1998 or earlier can use credits from Graph Theory towards mandatory major course requirement in theory area. - From the Fall semester 2001, Internet Server (TE628) offered by Cooperative Telecommunication Education Program is counted as an elective major course. - Smart Business Application and Development(CS541) is counted as an elective course from Fall 2012.

Fine tuning

Fine tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.

Assuming the original task is similar to the new task, using a network that has already been designed & trained allows us to take advantage of the feature extraction that happens in the front layers of the network without developing that feature extraction network from scratch. Fine tuning:

• Replaces the output layer, originally trained to recognize (in the case of imagenet models) 1,000 classes, with a layer that recognizes the number of classes you require
• The new output layer that is attached to the model is then trained to take the lower level features from the front of the network and map them to the desired output classes, using SGD
• Once this has been done, other late layers in the model can be set as 'trainable=True' so that in further SGD epochs their weights can be fine-tuned for the new task too.

With respect to the cats vs dogs example:

• The original task would be classifying the images into the 1000s of Imagenet categories.
• The new task would be to classify the images into just 2 categories i.e., cats or dogs.
• From the definition of finetune, the last layer is removed/popped.
• Trainable is set to false for all other lower layers as they have already been trained (as part of the original task)
• In order for the trainable false to take effect, the model needs to be compiled again, as per the last line in the vgg16.finetune() function.