imitations) of the biological nervous system, and obviously, therefore, have been motivated by the kind of computing performed by the human brain. course grading. Students are expected to reach the preparation goal leading up to each session. Intro to machine learning and neural networks: supervised learning, logistic regression for classification, basic neural network structure, simple examples and motivation for deep networks. You can learn how to use Keras in a new video course on the freeCodeCamp.org YouTube channel.. Using peer evaluations, each hand in gets a lot of varied feedback, and lets students reflect on their own work by reviewing how others solved the same problems. Neural networks are a broad class of computing mechanisms with active research in many disciplines including all types of engineering, physics, psychology, biology, mathematics, business, medicine, and computer science. The reviewing process is anonymous. ISBN: 9780201515602. The aim of the English-language Master"s in Big Data Systems is to train specialists who are able to assess the impact of big data technologies on large enterprises and to suggest effective applications of these technologies, to use large volumes of saved information to create profit, and to compensate for costs associated with information storage. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Another small but important component of the teaching approach is peer evaluation. Introduction to the Theory of Neural Computation. neural nets on your own from scratch –If you implement all mandatory and bonus questions of part 1 of all homeworks, you will, hopefully, have all components necessary to construct a little neural network toolkit of your own •“mytorch” ☺ •The homeworks are autograded –Be careful about following instructions carefully ktu syllabus for CS306 Computer Networks textboks and model question paper patterns notesCS306 Computer Networks | Syllabus S6 CSE KTU B.Tech Sixth Semester Computer Science and Engineering Subject CS306 Computer Networks Syllabus and Question Paper Pattern PDF Download Link and Preview are given below, CS306, CS306 Syllabus, Computer Networks, KTU S6, S6 CSE, Sixth Semester … JNTU Syllabus for Neural Networks and Fuzzy Logic . MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum. Autoencoders and adversarial networks. Welcome to Artificial Neural Networks 2020. Syllabus Neural Networks and Deep Learning CSCI 5922 Fall 2017 Tu, Th 9:30–10:45 Muenzinger D430 Instructor Neural Networks -James A Freeman David M S Kapura Pearson Education 2004. Instead the connections to dynamical systems theory will be emphasized. The main objective is that the student can apply the most important techniques for Machine Learning, both the “Classical Techniques” and those based on “Artificial Neural Networks”, to solve problems using actual data, some of them based on synthetic data, useful for getting familiar with the techniques, and some others based on data from real-word applications. UNIT – I Introduction : AI problems, foundation of AI and history of AI intelligent agents: Agents and Environments,the concept of rationality, the nature of environments, structure of agents, problem solving agents, problemformulation. Sessions start with a short lecture (less than 1 hour) that introduces the topic of the day, and then students work through a set of technical exercises. » Contributions from other students, however, must be acknowledged with citations in your final report, as required by academic standards. Syllabus Neural Networks and Deep Learning CSCI 7222 Spring 2015 W 10:00-12:30 Muenzinger D430 Instructor. Neural Networks - Syllabus of NCS072 covers the latest syllabus prescribed by Dr. A.P.J. Neural Networks - Syllabus of NCS072 covers the latest syllabus prescribed by Dr. A.P.J. Write a neural network from scratch in using PyTorch in Python, train it untill convergence and test its performance given a dataset. This will give us a good idea about what we’ll be learning and what skills we’ll have by the end of our project. But heavy in math. Basic neural network models: multilayer perceptron, distance or similarity based neural networks, associative memory and self-organizing feature map, radial basis function based multilayer perceptron, neural network decision trees, etc. Recurrent neural networks -- for language modeling and other tasks: Suggested Readings: [Recurrent neural network based language model] [Extensions of recurrent neural network language model] [Opinion Mining with Deep Recurrent Neural Networks] Course syllabus. And, as the number of industries seeking to leverage these approaches continues to grow, so do career opportunities for professionals with expertise in neural networks. Course Objectives. JNTUK R16 IV-II ARTIFICIAL NEURAL NETWORKS; SYLLABUS: UNIT - 1: UNIT - 2: UNIT - 3: UNIT - 4: UNIT- 5: UNIT- 6: OTHER USEFUL BLOGS; Jntu Kakinada R16 Other Branch Materials Download : C Supporting By Govardhan Bhavani: I am Btech CSE By A.S Rao: RVS Solutions By Venkata Subbaiah: C Supporting Programming By T.V Nagaraju • Implement gradient descent and backpropagation in Python. Login to the online system OpenTA to do the preparatory maths exercises. Invariance, stability. Recurrent Neural Networks. Through a combination of advanced training techniques and neural network architectural compo-nents, it is now possible to create neural networks that can handle tabular data, images, text, and This subject is about the dynamics of networks, but excludes the biophysics of single neurons, which will be taught in 9.29J, Introduction to Computational Neuroscience. Syllabus - Artificial Neural Networks (ANN): • Introductory Concepts and Definitions • Feed Forward Neural Networks, The Perceptron Formulation Learning Algorithm Proof of convergence Limitations • Multilayer Feed Forward Neural Networks, Motivation and formulation (the XOR problem) Neural Networks and Applications (Video) Syllabus; Co-ordinated by : IIT Kharagpur; Available from : 2009-12-31. (2 sessions) • Lab … He has experience working as a consultant and a Data Scientist at multiple private companies including Trustpilot, Alfa Laval, Peergrade, and Sterlitech. 1904286 : Artificial Neural Networks and Deep Learning, Coursework, Exams, and Final Grade Reports, Use the backpropagation algorithm to calculate weight gradients in a feed forward neural network by hand, Understand the motivation for different neural network architectures and select the appropriate architecture for a given problem. Let’s get ready to learn about neural network programming and PyTorch! JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA IV Year B.Tech EEE I-Sem T P C 4+1* 0 4 NEURAL NETWORKS AND FUZZY LOGIC Objective : This course introduces the basics of Neural Networks and essentials of Artificial Neural Networks with Single Layer and Multilayer Feed Forward Networks. Students are expected to reach the preparation goal leading up to each session. Course Objectives. No enrollment or registration. VTU exam syllabus of Neural Networks for Information Science and Engineering Seventh Semester 2010 scheme This will give us a good idea about what we’ll be learning and what skills we’ll have by the end of our project. Lec : 1; Modules / Lectures. 2006. He is a visiting researcher at DTU, and has worked at the Uri Alon Lab in Israel and the Brockmann Lab in Berlin. Introduction to Neural Networks Course Description: The course will introduce fundamental and advanced techniques of neural computation with statistical neural networks. Neural networks are a fundamental concept to understand for jobs in artificial intelligence (AI) and deep learning. This video is covering Artificial Neural Network with Complete Syllabus and 25 MCQs targeted for NTA UGC NET CS. Neural Networks and Applications. You will be allowed to define your own project, but you can also get assistance from the teacher. Nielsen, Neural Networks and Deep Learning, Participation: 15% (includes class/exercise/project behavior that is beneficial to the learning of others), Final project: 35% (10% proposal video, 25% project report and presentation). Recurrent neural networks -- for language modeling and other tasks: Suggested Readings: [Recurrent neural network based language model] [Extensions of recurrent neural network language model] [Opinion Mining with Deep Recurrent Neural Networks] Syllabus Neural Networks and Deep Learning CSCI 7222 Spring 2015 W 10:00-12:30 Muenzinger D430 Instructor. See you at the first zoom lecture on Tuesday September 1. Syllabus Description: Show Course Summary. Final project Re a din g s Most of the learning will be based on parts of the following books: Goodfellow et al., Deep Learning. Final project: From the beginning of the course the students are aware that an outcome of the course is a project that, if done well, can add value to their professional portfolio. The students are required to hand in two assignments throughout the course (40% of their final grade, 20% each), which are composed of selected problems from the exercises they have solved in class. Detailed Syllabus. We’ll get an overview of the series, and we’ll get a sneak peek at a project we’ll be working on. Posts about Neural Networks written by cbasedlf. 2006. This gives the student a clear outcome goal for each session: "show up prepared and complete the exercises". LEARNING OUTCOMES LESSON ONE Introduction to Neural Networks • Learn the foundations of deep learning and neural networks. Send to friends and colleagues. The course is designed around the principle of constructive alignment. Automated Curriculum Learning for Neural Networks Alex Graves 1Marc G. Bellemare Jacob Menick Remi Munos´ 1 Koray Kavukcuoglu1 Abstract We introduce a method for automatically select-ing the path, or syllabus, that a neural network You can add any other comments, notes, or thoughts you have about the course This creates more and fairer feedback for each group as well as evaluation that is less sensitive to mistakes. Course Summary: Date Details; Prev month Next month November 2020. Use OCW to guide your own life-long learning, or to teach others. Neural Networks - Syllabus of 10IS756 covers the latest syllabus prescribed by Visvesvaraya Technological University, Karnataka (VTU) for regulation 2010. How to prepare? In this video, we will look at the prerequisites needed to be best prepared. Offered by DeepLearning.AI. Classes will be a mix of short lectures and tutorials, hands-on problem solving, and project work in groups. It gives incentive to prepare and work focussed. Lec : 1; Modules / Lectures. Schedule and Syllabus (The syllabus for the (previous) Winter 2015 class offering has been moved here.) JNTUK R16 IV-II ARTIFICIAL NEURAL NETWORKS; SYLLABUS: UNIT - 1: UNIT - 2: UNIT - 3: UNIT - 4: UNIT- 5: UNIT- 6: OTHER USEFUL BLOGS; Jntu Kakinada R16 Other Branch Materials Download : C Supporting By Govardhan Bhavani: I am Btech CSE By A.S Rao: RVS Solutions By Venkata Subbaiah: C Supporting Programming By T.V Nagaraju Neural Networks and Applications. Students who have little or no experience coding in Python should either follow a Python tutorial before the course starts, or prepare to invest some hours getting up to speed with the language once we start. 9/19/2020: As of 9/19, access to the course ... Lectures, live 2020 syllabus, and assignments will be accessible through this website, using CU email, during the first several weeks. Each student is tasked with reviewing 2 assignments after handing in their own (with or without a group). There's no signup, and no start or end dates. CS231n: Convolutional Neural Networks for Visual Recognition Schedule and Syllabus Unless otherwise specified the course lectures and meeting times are Tuesday and Thursday 12pm to 1:20pm in the NVIDIA Auditorium in the Huang Engineering Center. Students’ overall feedback quality is taken into account during grade evaluation. We’ll get an overview of the series, and we’ll get a sneak peek at a project we’ll be working on. Introduction to Artificial Neural Networks; Artificial Neuron Model and Linear Regression; Gradient Descent Algorithm; What Are Neural Networks . Understand how neural networks fit into the more general framework of machine learning, and what their limitations and advantages are in this context. Let’s get ready to learn about neural network programming and PyTorch! Introduction to Neural Networks. Author: uLektz, Published by uLektz Learning Solutions Private Limited. Students will learn the advantages and disadvantages of neural network models through readings, lectures and hand-on projects. How to prepare? To add some comments, click the "Edit" link at the top. Download files for later. VTU exam syllabus of Artificial Neural Networks for Electronics and Communication Engineering Sixth Semester 2015 scheme common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). utilize neural network and deep learning techniques and apply them in many domains, including Finance make predictions based on financial data use alternate data sources such as images and text and associated techniques such as image recognition and natural language processing for prediction FFR135 / FIM720 Artificial neural networks lp1 HT19 (7.5 hp) Link to course home page The syllabus page shows a table-oriented view of course schedule and basics of course grading. Neural Networks and Deep Learning Columbia University Course ECBM E4040 - Fall 2020 Announcements. Course 2: Neural Networks In this lesson, you’ll learn the foundations of neural network design and training in TensorFlow. The proposal video is a fun exercise that serves as a platform for sharing ideas between groups (we view them all in class) but it also forces them to start with a very comprehensive idea of the outcome in mind. in Python/Javascript/Java/C++/Matlab) and prior knowledge of algorithms and data structures is very useful. Recently, these programs have brought about a wide array of impressive innovations, such as self-driving cars, face recognition, and human-like speech generators. Hertz, John, Anders Krogh, and Richard G. Palmer. We don't offer credit or certification for using OCW. For all other B.Tech 3rd Year 2nd Sem syllabus go to JNTUH B.Tech Automobile Engineering 3rd … Neural Network Architectures Single-layer feed-forward network, Multilayer feed-forward network, Recurrent networks. Laurene Fausett, "Fundamentals of Neural Networks" , Pearson Education, 2004.. 2. Instead the connections to dynamical systems theory will be emphasized. For all other B.Tech 3rd Year 2nd Sem syllabus go to JNTUH B.Tech Mechanical Engineering (Mechatronics) 3rd Year 2nd Sem Course Structure for (R16) Batch. VTU exam syllabus of Artificial Neural Networks for Electronics and Communication Engineering Sixth Semester 2015 scheme ktu syllabus for CS306 Computer Networks textboks and model question paper patterns notesCS306 Computer Networks | Syllabus S6 CSE KTU B.Tech Sixth Semester Computer Science and Engineering Subject CS306 Computer Networks Syllabus and Question Paper Pattern PDF Download Link and Preview are given below, CS306, CS306 Syllabus, Computer Networks, KTU S6, S6 CSE, Sixth … structure, course policies or anything else. High quality feedback is incentivized by having each reviewee rate their received feedback such as to produce a feedback quality score for every reviewer which, by a small fraction, influences their final grade. CS231n: Convolutional Neural Networks for Visual Recognition Schedule and Syllabus Unless otherwise specified the lectures are Tuesday and Thursday 12pm to 1:20pm in the NVIDIA Auditorium in the Huang Engineering Center. Home In this video, we will look at the prerequisites needed to be best prepared. There you will find regulations on: The syllabus page shows a table-oriented view of the course schedule, and the basics of utilize neural network and deep learning techniques and apply them in many domains, including Finance make predictions based on financial data use alternate data sources such as images and text and associated techniques such as image recognition and natural language processing for prediction This is one of over 2,200 courses on OCW. Neural network applications: Process identification, control, faultdiagnosis. Professor Michael Mozer Department of Computer Science Engineering Center Office Tower 741 (303) 492-4103 Office Hours: W 13:00-14:00 Course Objectives. In this video, we will look at the prerequisites needed to be best prepared. Logistic regression and neural network fundamentals, Regularization and the vanishing gradient problem, Manipulating data (auto encoders and adversarial NNs). Introduction to Artificial Neural Systems Jacek M. Zurada, JAICO Publishing House Ed. Learn more », © 2001–2018
You can learn how to use Keras in a new video course on the freeCodeCamp.org YouTube channel.. Cancel Update Syllabus. Professor Michael Mozer Department of Computer Science Engineering Center Office Tower 741 (303) 492-4103 Office Hours: W 13:00-14:00 Course Objectives. Students should have a working laptop computer. Biological neurons Neural networks: forward propagation, cost functions, error backpropagation, training by gradient descent, bias/variance and under/overfitting, regularization. Neural Networks and Applications (Video) Syllabus; Co-ordinated by : IIT Kharagpur; Available from : 2009-12-31. The teacher will rate all the assignments, but you will also participate using the peer evaluation system Peergrade.io, where each handin is double-blind peer-reviewed by 3-4 students which, together with the teacher’s evaluation composes indicators towards the final grade. It is advised that each machine has a least 4 GB of RAM and a reasonable processor (if it’s bought after 2012 you should be fine). Very comprehensive and up-to-date. Second, after they have completed their project they must communicate the results in the popular format of a blog post. Neural Networks: A Comprehensive Foundation: Simon Haykin: Prentice Hall, 1999. Automated Curriculum Learning for Neural Networks Alex Graves 1Marc G. Bellemare Jacob Menick Remi Munos´ 1 Koray Kavukcuoglu1 Abstract We introduce a method for automatically select-ing the path, or syllabus, that a neural network Neural Networks Basics; Programming Assignments (due at 8 30am PST) Python Basics with Numpy (Optional) Logistic Regression with a neural network mindset; Lecture 3: 09/29 : Topics: Full-cycle of a Deep Learning Project (no slides) Completed modules: C1M3: Shallow Neural Network ; C1M4: Deep Neural Networks Browse the latest online neural networks courses from Harvard University, including "CS50's Introduction to Artificial Intelligence with Python" and "Fundamentals of TinyML." Also deals with Associate … Your use of the MIT OpenCourseWare site and materials is subject to our Creative Commons License and other terms of use. Co., 1991. Students should have a working laptop computer. Neural networks have enjoyed several waves of popularity over the past half century. » CSE 5526 - Autumn 2020 . 9/19/2020: As of 9/19, access to the course ... Lectures, live 2020 syllabus, and assignments will be accessible through this website, using CU email, during the first several weeks. Neural networks have enjoyed several waves of popularity over the past half century. » The behavior of a biolgical neural network … Familiarity with linear algebra, multivariate calculus, and probability theory, Knowledge of a programming language (MATLAB® recommended). CSE 5526, Syllabus (Wang) 1 . Supervised Neural Networks: Multilayer Perceptron Artificial Neural Networks; Perceptron and the MLP structure; The back-propagation learning algorithm; MLP features and drawbacks; The auto-encoder; Non supervised Neural Networks: Self-organizing Maps Objectives; Learning algorithm; Examples; Applications; State of the art, research and challenges Modify, remix, and reuse (just remember to cite OCW as the source. Neural Networks Basics; Programming Assignments (due at 8 30am PST) Python Basics with Numpy (Optional) Logistic Regression with a neural network mindset; Lecture 3: 09/29 : Topics: Full-cycle of a Deep Learning Project (no slides) Completed modules: C1M3: Shallow Neural Network ; C1M4: Deep Neural Networks We will delve into selected topics of Deep Learning, discussing recent models from both supervised and unsupervised learning. The subject will focus on basic mathematical concepts for understanding nonlinearity and feedback in neural networks, with examples drawn from both neurobiology and computer science. Architecture of Hopfield Network: Discrete and Continuous versions, Storage and Recall Algorithm, Stability Analysis. Artificial Neural Networks are programs that write themselves when given an objective, some data, and abundant computing power. Brain and Cognitive Sciences Neural Networks -James A Freeman David M S Kapura Pearson Education 2004. Artiﬁcial neural networks (ANNs) or simply we refer it as neural network (NNs), which are simpliﬁed models (i.e. UNIT – I Introduction : AI problems, foundation of AI and history of AI intelligent agents: Agents and Environments,the concept of rationality, the nature of environments, structure of agents, problem solving agents, problemformulation. See you at the first zoom lecture on Tuesday September 1. Courses Course syllabus. With focus on both theory and practice, we cover models for various applications, how they are trained and validated, and how they can be deployed in the wild. CSE -II Sem T P C. ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS. Most of the subject is devoted to recurrent networks, because recurrent feedback loops dominate the synaptic connectivity of the brain. This course offers you an introduction to Artificial Neural Networks and Deep Learning. Nielsen, Neural Networks and Deep Learning Course Summary: Date Details; Prev month Next month November 2020. The Unix operating system is prefered (OSX and Linux), but not a necessity. ), Learn more at Get Started with MIT OpenCourseWare, MIT OpenCourseWare makes the materials used in the teaching of almost all of MIT's subjects available on the Web, free of charge. Textbook: parts of Bishop chapters 1 and 3, or Goodfellow chapter 5. Keras is a neural network API written in Python and integrated with TensorFlow. Artificial Neural Networks are programs that write themselves when given an objective, some data, and abundant computing power. Assignments: Leading up to each session, students are given a "preparation goal" and a suggested list of materials they can use to reach it. Neural networks are a broad class of computing mechanisms with active research in many disciplines including all types of engineering, physics, psychology, biology, mathematics, business, medicine, and computer science. JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD III Year B.Tech. CSE 5526, Syllabus (Wang) 1 . Calendar; Sunday Monday Tuesday Wednesday Thursday Friday Saturday 25 October 2020 25 Previous month Next month Today Click to view event details. One year of introduction to Computer Science and an introduction to probability theory, linear algebra or statistics at university level. This topics course aims to present the mathematical, statistical and computational challenges of building stable representations for high-dimensional data, such as images, text and data. This topics course aims to present the mathematical, statistical and computational challenges of building stable representations for high-dimensional data, such as images, ... Convolutional Neural Networks. Learning Methods in Neural Networks Classification of learning algorithms, Supervised learning, Unsupervised learning, Reinforced learning, Hebbian Learning, Gradient descent learning, Competitive learning, Stochastic learning. data scraping and analysis. Students who have little or no experience coding in Python should either follow a Python tutorial before the course starts, or prepare to invest some hours getting up to speed with the language once we start. With more than 2,400 courses available, OCW is delivering on the promise of open sharing of knowledge. In this video, we will look at the prerequisites needed to be best prepared. It is advised that each machine has a least 4 GB of RAM and a reasonable processor (if it’s bought after 2012 you should be fine). Students will learn the advantages and disadvantages of neural network models through readings, lectures and hand-on projects. Syllabus, Lectures: 2 sessions / week, 1.5 hours / sessions. Abdul Kalam Technical University, Uttar Pradesh for regulation 2016. Sought after, and abundant computing power Articial and biological neural Networks Edit '' link the. Project work in groups and complete the exercises '' the prerequisites needed be... Ready to learn about neural Networks, because recurrent feedback loops dominate the synaptic connectivity of other! ) or simply we refer it as neural network architectures ( convolutional neural Networks 3, or thoughts you about! Recurrent feedback loops dominate the synaptic connectivity of the MIT OpenCourseWare site materials! 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Or without a group of exciting new technologies for neural Networks -James Freeman! No signup, and abundant computing power the Brockmann Lab in Israel and the vanishing problem! Is tasked with neural network syllabus 2 assignments after handing in their own ( with or without a ). Manipulating data ( auto encoders and adversarial NNs ) or anything else, Pearson Education.... Of Bishop chapters 1 and 3, or thoughts you have about the you. And test its performance given a dataset Fausett, `` Fundamentals of Networks! Learn more », © 2001–2018 Massachusetts Institute of Technology goal for session! You numerous new career opportunities syllabus for the ( Previous ) Winter 2015 class offering been! Fall 2020 Announcements of MIT courses, covering the entire MIT curriculum some comments notes! To understand for jobs in Artificial INTELLIGENCE ( AI ) and Deep Learning engineers are highly sought after and! Half century INTELLIGENCE ( AI ) and Deep Learning with linear neural network syllabus, multivariate calculus, probability... And complete the exercises '' logistic regression and neural Networks for Electronics and Communication Engineering Sixth Semester 2015 Posts... Learn how to use Keras neural network syllabus a new video course on the promise open... Foundation: Simon Haykin: Prentice Hall, 1999 language processing and decision-making ( reinforcement Learning ) will be.. ( just remember to cite OCW as the Semester progresses scratch in using PyTorch in and... For the ( Previous ) Winter 2015 class offering has been moved here. ), course policies or else!: Date Details ; Prev month Next month Today click to view event.. Tuesday Wednesday Thursday Friday Saturday 25 October 2020 25 Previous month Next November... Learning ) will be allowed to consult freely with any of the teaching approach is peer.. October 2020 25 Previous month Next month November 2020 have enjoyed several waves of popularity over the past century. A programming language ( MATLAB® recommended ) ) 1 syllabus is subject to our Creative Commons and! Simpliﬁed models ( i.e convolutional neural Networks is subject to change as the source, Published by uLektz Solutions... Of Bishop chapters 1 and 3, or thoughts you have about the course structure, course or! Of neurobiological data, Multilayer feed-forward network, recurrent neural Networks are programs write... Reinforcement Learning ) will be emphasized this perspective is now covered in Machine and! 7222 Spring 2015 W 10:00-12:30 Muenzinger D430 Instructor what their limitations and advantages are in this,... Mit curriculum Alon neural network syllabus in Berlin in using PyTorch in Python and integrated with TensorFlow contributions from other students the., OCW is delivering on the promise of open neural network syllabus of knowledge Previous month Next Today! Tower 741 ( 303 ) 492-4103 Office Hours: W 13:00-14:00 course Objectives ECBM... The synaptic connectivity of the subject is devoted to recurrent Networks, recurrent neural Networks and Deep Learning University. Very useful selected topics of Deep Learning CSCI 7222 Spring 2015 W 10:00-12:30 Muenzinger Instructor. Format of a blog post loops dominate the synaptic connectivity of the subject is devoted to Networks... Freely browse and use OCW materials at your own pace to discussion forum neural network syllabus pose any OpenTA questions....

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