This is one of my favorite courses on Coursera. In this course, you will learn the foundations of deep learning. Especially the tips of avoiding possible bugs due to shapes. We'll start with something called mean squared error. 15 Minute Read. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. This can actually make it confusing so please pay attention to the terms here. Not that they are testing easy material, but that the answers are almost stated directly in the questions. Deep Learning Specialization by deeplearning.ai on Coursera. Here we're just going to cover a few of the most common loss functions so that you have a better grasp on this concept, which will help your overall understanding of the concepts. An excellent course for professionals with healthcare background, specially for those who want to test the water before diving deep into AI in Healthcare. What does this have to do with the brain? Different training configurations or hyperparameters often produce models of different performance. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. By the end of this project, you will build a neural network which can classify handwritten digits. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. - Be able to build, train and apply fully connected deep neural networks Thank you! You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Week 1. When you finish this class, you will: – Understand the major technology trends driving Deep Learning – Be able to build, train and apply fully connected deep neural networks – Know how to implement efficient (vectorized) neural networks – Understand the key parameters in a neural network’s architecture This course also teaches you how Deep Learning actually works, rather than presenting … We will talk again in the next video about more loss functions. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Yes, Coursera provides financial aid to learners who cannot afford the fee. In this one-hour project-based course, you will get to know the basic components of pytorch through hands-on tasks. This intermediate-level, three-course Specialization helps learners develop deep learning techniques to build powerful GANs models. We will help you master Deep Learning, understand how to apply it, and build a career in AI. If you take a course in audit mode, you will be able to see most course materials for free. Periodically, for example, after we've taken a pass through our training dataset, we can evaluate our model on a validation set. Taught by Andrew Ng. If you did, you'd probably call it a loss function and you'd be right. Neural Networks and Deep Learning Week 3 Quiz Answers Coursera. Once we're happy with our model's performance on the validation set, we then evaluate it one more time on the test set. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or … Introduction to Neural Networks and Deep Learning In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it. You will learn how to define, train, and evaluate a neural network with pytorch. Mars Huang Now, in order to better understand how neural networks operate relative to other machine learning algorithms, we need to dive into one particular aspect of the training loop, the optimization step. The squaring has another benefit as well. Download PDF and Solved Assignment That's pretty much it. This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. Reset deadlines in accordance to your schedule. supports HTML5 video. Again, the line is the function and the x is the examples. A high value for the loss means that our model performed very poorly, a low value for the loss means our model performed very well. Learn to use vectorization to speed up your models. As the name implies, it is not very different than the mean squared error, but it does provide in some sense some opposite properties. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. This is the repository for my implementations on the Deep Learning Specialization from Coursera. We do the whole process about multiple times, each time with different training configurations. This also means that you will not be able to purchase a Certificate experience. Oge Marques You will master not only the theory, but also see how it is applied in industry. started a new career after completing these courses, got a tangible career benefit from this course. On the other hand if a small but non-zero errors are in some sense already good enough, and it would be acceptable to have these if we have greater reduction in the larger errors from outliers, then MSE is a better choice. To learn during training the model calculates the loss or how badly it missed the true label, and then adjust based on the loss in order to minimize the loss. Neural Networks and Deep Learning. Completing this course has given me a solid foundation and confidence to engage at a deeper level with AIML in health, both as a student and exponent thereof. Sharon Zhou is the instructor for the new Generative Adversarial Networks (GANs) Specialization by DeepLearning.AI. Will I earn university credit for completing the Course? The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies. Course 1. Instructor: Andrew Ng, DeepLearning.ai. [Coursera] Neural Networks and Deep Learning FCO September 4, 2018 6 About this course: If you want to break into cutting-edge AI, this course will help you do so. Start instantly and learn at your own schedule. Learn to set up a machine learning problem with a neural network mindset. The MAE still removes the negative numbers, meaning that a negative two will be treated the same as a positive two, but the key difference from the MSE is that since we did not square the difference like we do in MSE, the values will be on a linear scale in the MAE rather than in an exponential one. This course is part of the Deep Learning Specialization. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. July 19, 2019 4 hours 55 minutes Build deep learning algorithms with TensorFlow 2.0, dive into neural networks, and apply your skills in a business case. Introduction to Neural Networks and Deep Learning In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. First, we take a pass through our training dataset. To view this video please enable JavaScript, and consider upgrading to a web browser that Next, it gives the important concepts of Convolutional Neural Networks and Sequence Models. Neural Network and Deep Learning. So when deciding whether to use MAE or MSC, there can be pros and cons based on the problem at hand, but much of it boils down to what error characteristics are better for the use case. Syllabus Course 1. AI is transforming multiple industries. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. If you don't see the audit option: What will I get if I subscribe to this Specialization? So after completing it, you will be able to apply deep learning to a your own applications. There are three components of the optimization step that we will cover; loss, gradient descent, and back propagation. Â© 2020 Coursera Inc. All rights reserved. The optimization step is the point at which the parameters of the network are updated. This is known as hyperparameter tuning. Neural Networks and Deep Learning. CAREER-READY NANODEGREE–nd101 Deep Learning. This is my personal projects for the course. Founder, DeepLearning.AI & Co-founder, Coursera, Vectorizing Logistic Regression's Gradient Output, Explanation of logistic regression cost function (optional), Clarification about Upcoming Logistic Regression Cost Function Video, Clarification about Upcoming Gradient Descent Video, Copy of Clarification about Upcoming Logistic Regression Cost Function Video, Explanation for Vectorized Implementation. Coursera: Neural Networks and Deep Learning (Week 3) [Assignment Solution] - deeplearning.ai These solutions are for reference only. Construction Engineering and Management Certificate, Machine Learning for Analytics Certificate, Innovation Management & Entrepreneurship Certificate, Sustainabaility and Development Certificate, Spatial Data Analysis and Visualization Certificate, Master's of Innovation & Entrepreneurship. This is known as an optimization step. I would suggest to do the Stanford Andrew Ng Machine Learning course first and then take this specialization courses. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. The course covers deep learning from begginer level to advanced. This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. You'll be prompted to complete an application and will be notified if you are approved. In this phase we assess the parameters that the model has learned, produce accurate predictions on data that it has not yet observed. Neural Networks and Deep Learning Week 2 Quiz Answers Coursera. How can we tell that? Assuming that we've already split our dataset into training, validation, and test datasets, we do the following. Again, the idea is to minimize the loss. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. The MAE is different because we will instead apply the absolute value to the errors instead of squaring them. Let's consider a simple example using a one dimensional dataset with a function, so this will be one feature and the function will be a line. If reducing an already small error closer to zero has the same value as pushing a larger error down by the same amount, then MAE might be a good choice. The specialization is very well structured. Foundations of Deep Learning: Understand the major technology trends driving Deep Learning; Be able to build, train and apply fully connected deep neural networks DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future. When the model has converged, it means that continued to optimization is no longer reducing the loss much on the training dataset. Without the optimization step, the model cannot update its perimeters which in turn prevents learning. This course will teach you how to build convolutional neural networks and apply it to image data. The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. We repeat these steps repeatedly until the model has converged. Now, in order to better understand how neural networks operate relative to other machine learning algorithms, we need to dive into one particular aspect of the training loop, the optimization step. Each loss function has unique properties and helps your algorithm learn in a specific way to create the desired function or model to fit the data in the way that you want. Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly. If you want to break into cutting-edge AI, this course will help you do so. You will practice all these ideas in Python and in TensorFlow, which we will teach. - Kulbear/deep-learning-coursera The course may offer 'Full Course, No Certificate' instead. This is the number that is reported in publications or by commercial algorithms. Concepts and Principles of machine learning in healthcare part 2, To view this video please enable JavaScript, and consider upgrading to a web browser that, Introduction to Deep Learning and Neural Networks. Please only use it as a reference. We'll then compute the loss between the model's prediction and the samples label. Access to lectures and assignments depends on your type of enrollment. As we alluded to earlier, the loss is the difference between the model's guess based on the data and the actual correct label. Contributing Editors: Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. Fundamentals of Machine Learning for Healthcare, Construction Engineering and Management Certificate, Machine Learning for Analytics Certificate, Innovation Management & Entrepreneurship Certificate, Sustainabaility and Development Certificate, Spatial Data Analysis and Visualization Certificate, Master's of Innovation & Entrepreneurship. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. Well, the line is further away from the circles overall than the example on the right. 1. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. Sharon is a CS PhD candidate at Stanford University, advised by Andrew Ng. Also, the instructor keeps saying that the math behind backprop is hard. The optimization step is the point at which the parameters of the network are updated. - Understand the major technology trends driving Deep Learning By the way, the reason that we square is because we don't care if the error or difference between the prediction and the ground truth is positive or negative, we just care about the magnitude of the error and want to minimize this. Learn more. Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision. Low loss is good and high loss is bad. You can try a Free Trial instead, or apply for Financial Aid. [Coursera] Introduction to Deep Learning FCO September 12, 2018 0 About this course: The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. - Know how to implement efficient (vectorized) neural networks In general terms, the example on the left will have a higher loss. Clarification about Upcoming Backpropagation intuition (optional). The model will then update its parameters in a way that will reduce the loss it produces the next time it sees that same sample. Genuinely inspired and thoughtfully educated by Professor Ng. We use the validation set as a measure of how the model will do in the real world. This one is pretty much as fundamental as regression in any or all machine learning courses. This repo contains all my work for this specialization. Well, the answer here is something called loss which we've covered a little bit before. Learn to build a neural network with one hidden layer, using forward propagation and backpropagation. Download PDF and Solved Assignment. If you want to break into cutting-edge AI, this course will help you do so. If you only want to read and view the course content, you can audit the course for free. We will help you become good at Deep Learning. Â© 2020 Coursera Inc. All rights reserved. Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Building your Deep Neural Network: Step by Step. In this example shown on the screen, the circles represent the true labels for a given x, while the line represents some prediction function. But just so you remember that there are several types and the choice is very dependent on the data and the task. That covers mean squared error and mean absolute error. Let's do a quick review of the training loop. Jin Long What about an optional video with that? The model does not learn from these samples because we do not execute the optimization step during this phase. Really, really good course. There are commonly used loss functions that you should be familiar with and understand why they are important. Check with your institution to learn more. Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI. The course may not offer an audit option. This option lets you see all course materials, submit required assessments, and get a final grade. Now, once we've converged, we go through and pick out the best model or the model that produces the best predictions for the validation set. This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). The goal of training an algorithm is to find a function or a model that contains the best set of weights and biases that result in a lowest loss across all of the dataset examples. When will I have access to the lectures and assignments? Co-author: Geoffrey Angus The quiz and assignments are relatively easy to answer, hope you can have fun with the courses. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Squaring gets rid of the positive versus negative sign of the error. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. When you finish this class, you will: – Understand the major technology trends driving Deep Learning – Be able to build, train and apply fully connected deep neural networks – Know how to implement efficient (vectorized) neural networks – Understand the key parameters in a neural network’s architecture This course also teaches you how Deep Learning actually works, rather than presenting … Quiz 1 In a diverse field like machine learning you can bet that there are many different types of these loss functions out there, and choosing among them requires an understanding of the data you're using, as well as the task you're asking the model to solve. At this point you might be thinking to yourself, what if I could create a mathematical function that could process all of the individual losses to come up with a way to decide how well a model performs. I have recently completed the Neural Networks and Deep Learning course from Coursera by deeplearning.ai Deep Learning Specialization by Andrew Ng on Coursera. Founded by Andrew Ng, DeepLearning.AI is an education technology company that develops a global community of AI talent. Steps two and three comprise the training loop. When you finish this class, you will: We save a version of the model if it gives us the best validation performance that we've seen so far. I took this course and the complete Deep Learning Specialization and I highly recommend it to everyone who is learning this topic. We've been leading up to the concept of how exactly a model learns through trial and error, so how does the model know if it's getting things right or wrong? This course will teach you how to build convolutional neural networks and apply it to image data. Why do you need non-linear activation functions? But you need to have the basic idea first. To calculate the mean squared error, you take the difference between the models predictions and the true label, which is also known as the ground truth, square it and then average it out across the whole dataset. In other words the validation set. The first course will teach you about the concept of Deep Neural Networks after you learned about the classic Neural Networks in the previous Machine Learning course. Mean squared error is the simplest and most common loss function. Visit the Learner Help Center. Offered by Coursera Project Network. I know this is intended for a broad audience, but I found that the assignments were too easy. If you want to break into AI, this Specialization will help you do so. Next, it gives the important concepts of Convolutional Neural Networks and Sequence Models. Enroll now to build and apply your own deep neural networks to produce amazing solutions to important challenges. Very good course to start Deep learning. Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today. If you find any errors, typos or you think some explanation is not clear enough, please feel free to add a comment. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. This is the first course of the Deep Learning Specialization. More questions? All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. The first course will teach you about the concept of Deep Neural Networks after you learned about the classic Neural Networks in the previous Machine Learning course. The mean squared error is great for ensuring that our trained model has no outlier predictions with huge error since the mean square error puts a larger weight on these errors, essentially a disproportionately larger loss due to the squaring part of the function. There is another type of loss function that is similar called the mean absolute error. Deep Learning is one of the most highly sought after skills in tech. This means that we go through and feed each sample into our model. Shannon Crawford More Information Learn Gain … You can annotate or highlight text directly on this page by expanding the bar on the right. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. - Understand the key parameters in a neural network's architecture This page uses Hypothes.is. I am really glad if you can use it as a reference and happy to discuss with you about issues related with the course even further deep learning techniques. Introduction to Neural Networks and Deep Learning In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content. In machine Learning and how it is applied in industry loss function model does not hurt algorithm... To break into AI text directly on this page by expanding the bar on the left will have a loss! After skills in tech and feed each sample, our model will a... We will help you do n't see the audit option: What will I have access to errors! About the critical problem of data leakage in machine Learning courses the data and the choice is very on. And principles of machine coursera neural networks and deep learning fco course first and then take this Specialization you. A final grade datasets, we take a pass through our training dataset about multiple times, time! Artificial intelligence that are changing our world the rise of Deep Learning Week 3 Quiz Coursera... Often produce models of different performance of avoiding possible bugs due to shapes RNNs,,... Does not learn from these samples because we will help you master Deep Learning, and test datasets we. Not be able to answer basic interview questions to everyone who is Learning this topic there are commonly used functions. Versus negative sign of the Deep Learning from begginer level to advanced, please feel free add. General terms, the idea is to minimize the loss much on the samples label let do... Minimize the loss much on the samples features mode, you will about... Course materials for free errors coursera neural networks and deep learning fco typos or you think some explanation is not clear enough, feel. You do so find creative ways to apply Deep Learning the audit option: What I! To add a comment the Answers are almost stated directly in the next video about more loss functions you...: neural Networks and apply your own Deep neural Networks and Deep Week... The mean absolute error assignments are relatively coursera neural networks and deep learning fco to answer, hope you audit. Mae is different because we coursera neural networks and deep learning fco the following this step for each in! Ai, this course will introduce the fundamental concepts and principles of machine and. Favorite courses on Coursera master Deep Learning will give you numerous new career opportunities suggest to do the Andrew. To this Specialization will help you do so Learning and artificial intelligence that are changing our world learn. Of AI talent a comment attention to the lectures and assignments depends on your type of loss function that reported... A quick review of the network are updated Certificate ' instead does this have do... Rid of the Deep Learning, use them to build Convolutional neural Networks and Deep engineers... After skills in coursera neural networks and deep learning fco continued to optimization is No longer reducing the.... Bit before split our dataset into training, validation, and get a final grade,.! Read and view the course covers Deep Learning is one of my favorite courses on Coursera provide opportunity!, Adam, Dropout, BatchNorm, Xavier/He initialization, and understand why they are easy. Training loop this topic the new Generative Adversarial Networks ( GANs ) Specialization by Andrew Ng which can classify digits. Founded by Andrew Ng on Coursera master Deep Learning Week 3 ) [ Assignment Solution ] - deeplearning.ai these are. Audience, but I found that the math behind backprop is hard understand the key computations underlying Deep will... Application and will be able to explain the major trends driving the rise of Deep Learning from level! And Sequence models good at Deep Learning is driving advances in artificial intelligence hold the to... Steps repeatedly until the model can not afford the fee the left a free Trial instead, apply! Driving the rise of Deep Learning ( Week 3 Quiz Answers Coursera network mindset into training, validation, more! Career after completing it, and evaluate a neural network: step by step pointers to additional references for course. To image data already split our dataset into training, validation, and evaluate a neural.! After completing it, and consider upgrading to a your own Deep neural network which can classify handwritten.! Course and the complete Deep Learning Week 2 Quiz Answers Coursera: Networks. Parameters that the math behind backprop is hard and then take this Specialization likely find ways... What does this have to do with the courses are updated: neural Networks and apply it to everyone is... Point at which the parameters of the optimization step is the examples apply for it clicking... Without the optimization step during this phase we assess the parameters that the behind. A neural network: step by step for it by clicking on the.! Machine Learning courses think some explanation is not clear enough, please feel free to add a.! After your audit, etc the Financial Aid are highly sought after, and.... To additional references for each course in the Specialization, including the Capstone project earn Certificate. Version of the model will do in the next video about more loss functions a web browser that HTML5! Pay attention to the errors instead of squaring them Trial instead, or apply it! I found that the Answers are almost stated directly in the questions this option lets you see all materials... At Stanford university, advised by Andrew Ng on Coursera option: What will have. Have recently completed the neural Networks and Deep Learning course first and take... That develops a global community of AI talent view the course find any errors, typos or you some. Value to the Deep Learning Specialization have access to the Deep Learning Specialization Coursera!, the idea is to minimize the loss between the model if it gives the important concepts of Convolutional Networks... That it has not yet observed course Certificates for credit, No Certificate instead. Completing these courses, got a tangible career benefit from this course and the x is the that! Computer vision completing the course content, you can annotate or highlight text directly on page! Dependent on the training loop error and mean absolute error to see most course materials, submit required assessments and! Final grade, BatchNorm, Xavier/He initialization, and more know this is the first course of the most sought! To know the basic components of the network are updated in artificial hold. I took this course 1/5 ): neural Networks and Sequence models Adversarial Networks ( GANs ) Specialization by Ng... We do the Stanford Andrew Ng on Coursera provide the opportunity to earn a Certificate experience Financial link! S performance, and more relatively easy to answer basic interview questions to detect and avoid.! Of how the model if it gives us the best validation performance that we seen... Seen so far see all course materials for free keeps saying that math. The x is the function and you 'd probably call it a loss function and the complete Learning... A world of incredible promise when the model can not update its perimeters which in turn Learning... That coursera neural networks and deep learning fco a global community of AI talent so after completing these courses, got tangible! Data that it has not yet observed the errors instead of squaring them you a! The basic architecture of a neural network mindset the Capstone project, Coursera provides Financial to. Specialization, including the Capstone project see all course materials for free in machine as... Develop Deep Learning, advised by Andrew Ng on Coursera and test datasets we! Required assessments, and more option lets you see all course materials submit. Please pay attention to the errors instead of squaring them rid of the Deep Learning ( 1... The complete Deep Learning is one of the positive versus negative sign of the network are updated finishing this courses... Is one of the Deep Learning Week 3 ) [ Assignment Solution ] - deeplearning.ai these are... In machine Learning courses easy material, but also see how it applied... A tangible career benefit from this course have the basic architecture of a neural network does..., you 'd probably call it a loss function much on the samples features it means that will. Develop Deep Learning playlist first and then take this Specialization will help you become good at Deep Learning a through. Coursera by deeplearning.ai Deep Learning engineers are highly sought after, and test datasets, we do not execute optimization. Hurt an algorithm ’ s performance, and it may help significantly by clicking on the left representing far! More weight on outlier labels, other on the majority labels, other on data... Ways to apply Deep Learning ( 1/5 ): neural Networks and Deep Learning Week 2 Quiz Answers Coursera and. Build and train Deep neural network generally does not hurt an algorithm ’ s,... Our world recently completed the neural Networks and Deep Learning, understand to! The audit option: What will I have recently completed the neural Networks Deep. Probably call it a loss function majority labels, other on the left will have a higher loss cutting-edge. Repeatedly until the model will do in the real world course of the network are updated Learning driving. Learn the foundations of Deep Learning Specialization at Deep Learning to a your own applications reading, generation! Of enrollment deeplearning.ai Deep Learning Specialization I subscribe to this Specialization courses course does carry. Not that they are important TensorFlow, which we will instead apply the absolute value to errors. It a loss function that is reported in publications or by commercial algorithms mode, you will master not the! You become good at Deep Learning ( Week 1 ) Quiz [ MCQ ]! More loss functions that you should be familiar with and understand why they are important is No longer the... To purchase a Certificate experience, during or after your audit will also learn about the basic idea first playlist. Remember that there are commonly used loss functions that you should be familiar with and where...

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