andrew ng deep learning

These algorithmic improvements have allowed researchers to iterate throughout the IDEA -> EXPERIMENT -> CODE cycle much more quickly, leading to even more innovation. The intuition I had before taking the course was that it forced the weight matrices to be closer to zero producing a more “linear” function. Get Free Andrew Ng Deep Learning Book now and use Andrew Ng Deep Learning Book immediately to get % off or $ off or free shipping Ng stresses the importance of choosing a single number evaluation metric to evaluate your algorithm. He also gave an interesting intuitive explanation for dropout. Coursera has the most reputable online training in Machine Learning (from Stanford U, by Andrew Ng), a fantastic Deep Learning specialization (from deeplearning.ai, also by Andrew Ng) and now a practically oriented TensorFlow specialization (also from deeplearning.ai). Andrew Yan-Tak Ng (Chinese: 吳恩達; born 1976) is a British-born American businessman, computer scientist, investor, and writer.He is focusing on machine learning and AI. If you don’t care about the inner workings and only care about gaining a high level understanding you could potentially skip the Calculus videos. He co-founded Coursera and Google Brain, launched deeplearning.ai, Landing.ai, and the AI fund, and was the Chief Scientist at Baidu. This allows your team to quantify the amount of avoidable bias your model has. Highly recommend anyone wanting to break into AI. Ng then explains methods of addressing this data mismatch problem such as artificial data synthesis. In NIPS*2010 Workshop on Deep Learning and Unsupervised Feature Learning. For example, you could transfer image recognition knowledge from a cat recognition app to a radiology diagnosis. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. I recently completed all available material (as of October 25, 2017) for Andrew Ng’s new deep learning course on Coursera. No. I recently completed Andrew Ng’s Deep Learning Specialization on Coursera and I’d like to share with you my learnings. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. Spammy message. Level- Intermediate. Andrew Ng: Deep learning has created a sea change in robotics. With it you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. Machine Learning and Deep Learning are growing at a faster pace. An example of a control which lacks orthogonalization is stopping your optimization procedure early (early stopping). There are currently 3 courses available in the specialization: I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng discusses the importance of orthogonalization in machine learning strategy. Abusive language . This book is focused not on teaching you ML algorithms, but on how to make them work. My only complaint of the course is that the homework assignments were too easy. Most machine learning problems leave clues that tell you what’s useful to try, and what’s not useful to try. Andrew Ng | Palo Alto, California | Founder and CEO of Landing AI (We're hiring! - Andrew Ng, Stanford Adjunct Professor Deep Learning is one of the most highly sought after skills in AI. I’ve seen teams waste months or years through not understanding the principles taught in this course. As a result, DNN’s can dominate smaller networks and traditional learning algorithms. The materials of this notes are provided from Ng explains that the approach works well when the set of tasks could benefit from having shared lower-level features and when the amount of data you have for each task is similar in magnitude. I have recently completed the Neural Networks and Deep Learning course from Coursera by deeplearning.ai The basic idea is that you would like to implement controls that only affect a single component of your algorithms performance at a time. Andrew Ng, the main lecturer, does a great job explaining enough of the math to get you started during the lectures. The exponential problem could be alleviated simply by adding a finite number of additional layers. For example, in face detection he explains that earlier layers are used to group together edges in the face and then later layers use these edges to form parts of faces (i.e. Prior to taking the course I thought that dropout is basically killing random neurons on each iteration so it’s as if we are working with a smaller network, which is more linear. Since dropout is randomly killing connections, the neuron is incentivized to spread it’s weights out more evenly among its parents. Implementing transfer learning involves retraining the last few layers of the network used for a similar application domain with much more data. As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people. In summary, transfer learning works when both tasks have the same input features and when the task you are trying to learn from has much more data than the task you are trying to train. You should only change the evaluation metric later on in the model development process if your target changes. , Founder of deeplearning.ai and Coursera, Natural Language Processing Specialization, Generative Adversarial Networks Specialization, DeepLearning.AI TensorFlow Developer Professional Certificate program, TensorFlow: Advanced Techniques Specialization, Download a free draft copy of Machine Learning Yearning. I. MATLAB AND LINEAR ALGEBRA TUTORIAL Matlab tutorial (external link) Linear algebra review: What are matrices/vectors, and how to add/substract/multiply them. Why does a penalization term added to the cost function reduce variance effects? We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Machine Learning (Left) and Deep Learning (Right) Overview. Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks Richard Socher, Christopher Manning and Andrew Ng. The basic idea is to manually label your misclassified examples and to focus your efforts on the error which contributes the most to your misclassified data. This ensures that your team is aiming at the correct target during the iteration process. nose, eyes, mouth etc.) This is my personal projects for the course. For example, switching from a sigmoid activation function to a RELU activation function has had a massive impact on optimization procedures such as gradient descent. I was not endorsed by deeplearning.ai for writing this article. Andrew Ng and Kian Katanforoosh (updated Backpropagation by Anand Avati) Deep Learning We now begin our study of deep learning. Before taking the course, I was aware of the usual 60/20/20 split. Timeline- Approx. Ng explains how human level performance could be used as a proxy for Bayes error in some applications. Ng’s early work at Stanford focused on autonomous helicopters; now he’s working on applications for artificial intelligence in health care, education and manufacturing. 90% of all data was collected in the past 2 years. They will share with you their personal stories and give you career advice. He also explains that dropout is nothing more than an adaptive form of L2 regularization and that both methods have similar effects. Machine Learning (Left) and Deep Learning (Right) Overview. Making world-class AI education accessible | DeepLearning.AI is making a world-class AI education accessible to people around the globe. Course 1. I have recently completed the Neural Networks and Deep Learning course from Coursera by deeplearning.ai For example, you may want to use examples that are not as relevant to your problem for training, but you would not want your algorithm to be evaluated against these examples. Lernen Sie Andrew Ng online mit Kursen wie Nr. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. About the Deep Learning Specialization. Neural Networks and Deep Learning Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner. • Other variants for learning recursive representations for text. Recall the housing … This book will tell you how. Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI. ); Founder of deeplearning.ai | 500+ connections | View Andrew's homepage, profile, activity, articles For example, Ng makes it clear that supervised deep learning is nothing more than a multidimensional curve fitting procedure and that any other representational understandings, such as the common reference to the human biological nervous system, are loose at best. The solution is to leave out a small piece of your training set and determine the generalization capabilities of the training set alone. He also discusses Xavier initialization for tanh activation function. Ng explains how to implement a neural network using TensorFlow and also explains some of the backend procedures which are used in the optimization procedure. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. The course covers deep learning from begginer level to advanced. This book will tell you how. You’re put in the driver’s seat to decide upon how a deep learning system could be used to solve a problem within them. End-to-end deep learning takes multiple stages of processing and combines them into a single neural network. We’ll use this information solely to improve the site. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. This is the lecture notes from a ve-course certi cate in deep learning developed by Andrew Ng, professor in Stanford University. Beautifully drawn notes on the deep learning specialization on Coursera, by Tess Ferrandez. A Probabilistic Model for Semantic Word Vectors Andrew Maas and Andrew Ng. در این پست ما دوره یادگیری عمیق Deep Learning Specialization از پروفسور NG را در قالب 5 فایل دانلودی برای شما تهیه کردیم. This is the lecture notes from a ve-course certi cate in deep learning developed by Andrew Ng, professor in Stanford University. The best approach is do something in between which allows you to make progress faster than processing the whole dataset at once, while also taking advantage of vectorization techniques. Print. In this course, you'll learn about some of the most widely used and successful machine learning techniques. — Andrew Ng, Founder of deeplearning.ai and Coursera Deep Learning Specialization, Course 5 Ng’s deep learning course has given me a foundational intuitive understanding of the deep learning model development process. Machine Learning: Stanford UniversityDeep Learning: DeepLearning.AIAI For Everyone: DeepLearning.AIStructuring Machine Learning Projects: DeepLearning.AIIntroduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: DeepLearning.AI Furthermore, there have been a number of algorithmic innovations which have allowed DNN’s to train much faster. Deep Learning Samy Bengio, Tom Dean and Andrew Ng. This article is part of the series: The Robot Makers . Multi-task learning forces a single neural network to learn multiple tasks at the same time (as opposed to having a separate neural network for each task). Andrew Ng • Deep Learning : Lets learn rather than manually design our features. Andrew Ng is one of the most impactful educators, researchers, innovators, and leaders in artificial intelligence and technology space in general. Deep Learning and Machine Learning. What should I do? He explains that in the modern deep learning era we have tools to address each problem separately so that the tradeoff no longer exists. Ng explains how techniques such as momentum and RMSprop allow gradient descent to dampen it’s path toward the minimum. Ng gives reasons for why a team would be interested in not having the same distribution for the train and test/dev sets. My inspiration comes from deeplearning.ai, who released an awesome deep learning specialization course which I have found immensely helpful in my learning journey. Take a look. O SlideShare utiliza cookies para otimizar a funcionalidade e o desempenho do site, assim como para apresentar publicidade mais relevante aos nossos usuários. Email this page. This allows your algorithm to be trained with much more data. You would like these controls to only affect bias and not other issues such as poor generalization. By Taylor Kubota. and then further layers are used to put the parts together and identify the person. I learned the basics of neural networks and deep learning, such as forward and backward progradation. He also addresses the commonly quoted “tradeoff” between bias and variance. As for machine learning experience, I’d completed Andrew’s Machine Learning Course on Coursera prior to starting. And if you are the one who is looking to get in this field or have a basic understanding of it and want to be an expert “Machine Learning Yearning” a book by Andrew Y. Ng is your key. This is the fourth course of the deep learning specialization from the Andrew Ng series. This course has 4 weeks of materials and all the assignments are done in NumPy, without any help of the deep learning frameworks. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Learning to read those clues will save you months or years of development time. Follow. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations H Lee, R Grosse, R Ranganath, AY Ng Proceedings of the 26th annual international conference on machine learning … Make learning your daily ritual. Deep neural networks (DNN’s) are capable of taking advantage of a very large amount of data. In NIPS*2010 Workshop on Deep Learning and Unsupervised Feature Learning. Or how the current deep learning system could be improved. 20 hours to complete. Without a benchmark such as Bayes error, it’s difficult to understand the variance and avoidable bias problems in your network. After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. — Andrew Ng, Founder of deeplearning.ai and Coursera Transfer learning allows you to transfer knowledge from one model to another. I created my own YouTube algorithm (to stop me wasting time), All Machine Learning Algorithms You Should Know in 2021, 5 Reasons You Don’t Need to Learn Machine Learning, 7 Things I Learned during My First Big Project as an ML Engineer, Building Simulations in Python — A Step by Step Walkthrough, Improving Deep Neural Networks: Hyperparamater tuning, Regularization and Optimization. The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. Don’t Start With Machine Learning. After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. This is due to the fact that the dev and test sets only need to be large enough to ensure the confidence intervals provided by your team. A Probabilistic Model for Semantic Word Vectors Andrew Maas and Andrew Ng. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. This is because it simultaneously affects the bias and variance of your model. Page 7 Machine Learning Yearning-Draft Andrew Ng Ng explains the idea behind a computation graph which has allowed me to understand how TensorFlow seems to perform “magical optimization”. — Andrew Ng Andrew Ng and Kian Katanforoosh (updated Backpropagation by Anand Avati) Deep Learning We now begin our study of deep learning. If that isn’t a superpower, I don’t know what is. Want to Be a Data Scientist? These algorithms will also form the basic building blocks of deep learning algorithms. For example, to address bias problems you could use a bigger network or more robust optimization techniques. This post is explicitly asking for upvotes. 25. We will help you become good at Deep Learning. Deep Learning is one of the most highly sought after skills in AI. Take the newest non-technical course from deeplearning.ai, now available on Coursera. In my opinion, however, you should also know vector calculus to understand the inner workings of the optimization procedure. The basic idea is to ensure that each layer’s weight matrices has a variance of approximately 1. Course Description . By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. But it did help with a few concepts here and there. For example, for tasks such as vision and audio recognition, human level error would be very close to Bayes error. Instructor: Andrew Ng, DeepLearning.ai. Deep Learning is a superpower. In addition to the lectures and programming assignments, you will also watch exclusive interviews with many Deep Learning leaders. The guidelines for setting up the split of train/dev/test has changed dramatically during the deep learning era. This also means that if you decide to correct mislabeled data in your test set then you must also correct the mislabelled data in your development set. One of the homework exercises encourages you to implement dropout and L2 regularization using TensorFlow. These algorithms will also form the basic building blocks of deep learning algorithms. It may be the case that fixing blurry images is an extremely demanding task, while other errors are obvious and easy to fix. In summary, here are 10 of our most popular machine learning andrew ng courses. Week 1 — Intro to deep learning Week 2 — Neural network basics. Making world-class AI education accessible | DeepLearning.AI is making a world-class AI education accessible to people around the globe. Ng stresses that for a very large dataset, you should be using a split of about 98/1/1 or even 99/0.5/0.5. Either you can audit the course and search for the assignments and quizes on GitHub…or apply for the financial aid. Founded by Andrew Ng, DeepLearning.AI is an education technology company that develops a global community of AI talent. Deep Learning Specialization, Course 5. Deep Learning is a superpower. Ng gives an intuitive understanding of the layering aspect of DNN’s. Notes from Coursera Deep Learning courses by Andrew Ng By Abhishek Sharma Posted in Kaggle Forum 3 years ago. … We will help you become good at Deep Learning. Using contour plots, Ng explains the tradeoff between smaller and larger mini-batch sizes. Either you can audit the course and search for the assignments and quizes on GitHub…or apply for the financial aid. This way we get a solid foundation of the fundamentals of deep learning under the hood, instead of relying on libraries. Is it 100% required? You'll have the opportunity to implement these algorithms yourself, and gain practice with them. According to MIT, in the upcoming future, about 8.5 out of every 10 sectors will be somehow based on AI. In this article, I will be writing about Course 1 of the specialization, where the great Andrew Ng explains the basics of Neural Networks and how to implement them. 1 Neural Networks We will start small and slowly build up a neural network, step by step. To the contrary, this approach needs much more data and may exclude potentially hand designed components. He demonstrates several procedure to combat these issues. The first course actually gets you to implement the forward and backward propagation steps in numpy from scratch. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. It has been empirically shown that this approach will give you better performance in many cases. AI, Machine Learning, Deep learning, Online Education. Ng gives an example of identifying pornographic photos in a cat classification application! Both the sensitivity and approximate work would be factored into the decision making process. Then you could compare this error rate to the actual development error and compute a “data mismatch” metric. Ng gave another interpretation involving the tanh activation function. He explicitly goes through an example of iterating through a gradient descent example on a normalized and non-normalized contour plot. I’ve been working on Andrew Ng’s machine learning and deep learning specialization over the last 88 days. With it you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. Instructors- Andrew Ng, Kian Katanforoosh, Younes Bensouda. I have decided to pursue higher level courses. We use cookies to collect information about our website and how users interact with it. I’ve been working on Andrew Ng’s machine learning and deep learning specialization over the last 88 days. The idea is that you want the evaluation metric to be computed on examples that you actually care about. He ties the methods together to explain the famous Adam optimization procedure. More about author Andrew Ng: Andrew Ng was born in London in the UK in 1976. Andrew Ng Kurse von führenden Universitäten und führenden Unternehmen in dieser Branche. Coursera. arrow_drop_up. Head to our forums to ask questions, share projects, and connect with the deeplearning.ai community. Before taking this course, I was not aware that a neural network could be implemented without any explicit for loops (except over the layers). Always ensure that the dev and test sets have the same distribution. Ng shows a somewhat obvious technique to dramatically increase the effectiveness of your algorithms performance using error analysis. You will work on case studi… DRAFT Lecture Notes for the course Deep Learning taught by Andrew Ng. Coursera has the most reputable online training in Machine Learning (from Stanford U, by Andrew Ng), a fantastic Deep Learning specialization (from deeplearning.ai, also by Andrew Ng) and now a practically oriented TensorFlow specialization (also from deeplearning.ai). Deep Learning Samy Bengio, Tom Dean and Andrew Ng. Programming assignment: build a simple image recognition classifier with logistics regression. • Deep learning very successful on vision and audio tasks. I have decided to pursue higher level courses. Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks Richard Socher, Christopher Manning and Andrew Ng. If you are working with 10,000,000 training examples, then perhaps 100,000 examples (or 1% of the data) is large enough to guarantee certain confidence bounds on your dev and/or test set. پروفسور Andrew NG یکی از افراد تاثیرگذار در حوزه computer science است. "Artificial intelligence is the new electricity." For example, in the cat recognition Ng determines that blurry images contribute the most to errors. Machine Learning Yearning is also very helpful for data scientists to understand how to set technical directions for a machine learning project. If that isn’t a superpower, I don’t know what is. Click Here to get the notes. That’s all folks — if you’ve made it this far, please comment below and add me on LinkedIn. deeplearning.ai | 325,581 followers on LinkedIn. Quote. در این پست ما دوره یادگیری عمیق Deep Learning Specialization از پروفسور NG را در قالب 5 فایل دانلودی برای شما تهیه کردیم. He is one of the most influential minds in Artificial Intelligence and Deep Learning. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. All information we collect using cookies will be subject to and protected by our Privacy Policy, which you can view here. The Deep Learning Specialization was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. By doing this, I have gained a much deeper understanding of the inner workings of higher level frameworks such as TensorFlow and Keras. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations H Lee, R Grosse, R Ranganath, AY Ng Proceedings of the 26th annual international conference on machine learning … I recently completed all available material (as of October 25, 2017) for Andrew Ng’s new deep learning course on Coursera. The picture he draws gives a systematic approach to addressing these issues. This is the new book by Andrew Ng, still in progress. Part 3 takes you through two case studies. Read writing from Andrew Ng on Medium. I signed up for the 5 course program in September 2017, shortly after the announcement of the new Deep Learning courses on Coursera. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. The basic idea is that a larger size becomes to slow per iteration, while a smaller size allows you to make progress faster but cannot make the same guarantees regarding convergence. Andrew Yan-Tak Ng (Chinese: 吳恩達; born 1976) is a British-born American businessman, computer scientist, investor, and writer.He is focusing on machine learning and AI. This allows the data to speak for itself without the bias displayed by humans in hand engineering steps in the optimization procedure. Ng explains the steps a researcher would take to identify and fix issues related to bias and variance problems. This is the fourth course of the deep learning specialization from the Andrew Ng series. Most machine learning problems leave clues that tell you what’s useful to try, and what’s not useful to try. March 05, 2019. Page 7 Machine Learning Yearning-Draft Andrew Ng Ng founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company's Artificial Intelligence Group into several thousand people. After completing the course you will not become an expert in deep learning. His parents were both from Hong Kong. The specialization only requires basic linear algebra knowledge and basic programming knowledge in Python. The homework assignments provide you with a boilerplate vectorized code design which you could easily transfer to your own application. Andrew Ng announces new Deep Learning specialization on Coursera; DeepMind and Blizzard open StarCraft II as an AI research environment; OpenAI bot beat best Dota 2 players in 1v1 at The International 2017; My Neural Network isn't working! — Andrew Ng, Founder of deeplearning.ai and Coursera Deep Learning Specialization, Course 5 Deep Learning Specialization by Andrew Ng - deeplearning.ai Deep Learning For Coders by Jeremy Howard, Rachel Thomas, Sylvain Gugger - fast.ai Deep Learning Nanodegree Program by Udacity CS224n: Natural Language Processing with Deep Learning by Christopher Manning, Abigail See - Stanford Ng shows that poor initialization of parameters can lead to vanishing or exploding gradients. Learning to read those clues will save you months or years of development time. Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. Take the test to identify your AI skills gap and prepare for AI jobs with Workera, our new credentialing platform. This repo contains all my work for this specialization. Report Message. Machine Learning Yearning, a free book that Dr. Andrew Ng is currently writing, teaches you how to structure machine learning projects. There are currently 3 courses available in the specialization: Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparamater tuning, Regularization and Optimization; Structuring Machine Learning Projects Building your Deep Neural Network: Step by Step. 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. This further strengthened my understanding of the backend processes. Whether you want to build algorithms or build a company, deeplearning.ai’s courses will teach you key concepts and applications of AI. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning Pranav Rajpurkar*, Jeremy Irvin*, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng . His intuition is to look at life from the perspective of a single neuron. For anything deeper, you’ll find the links above a great help. As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people. The idea is that smaller weight matrices produce smaller outputs which centralizes the outputs around the linear section of the tanh function. The idea is that hidden units earlier in the network have a much broader application which is usually not specific to the exact task that you are using the network for. Despite its ease of implementation, SGDs are diffi-cult to tune and parallelize. He also explains the idea of circuit theory which basically says that there exists functions which would require an exponential number of hidden units to fit the data in a shallow network. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. I recently completed all available material (as of October 25, 2017) for Andrew Ng’s new deep learning course on Coursera. Ng does an excellent job at conveying the importance of a vectorized code design in Python. He is one of the most influential minds in Artificial Intelligence and Deep Learning. This sensitivity analysis allows you see how much your efforts are worth on reducing the total error. Deep Learning is a superpower.With it you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself.If that isn’t a superpower, I don’t know what is. By spreading out the weights, it tends to have the effect of shrinking the squared norm of the weights. He also gives an excellent physical explanation of the process with a ball rolling down a hill. - Andrew Ng, Stanford Adjunct Professor Deep Learning is one of the most highly sought after skills in AI. Building your Deep Neural Network: Step by Step. deeplearning.ai | 325,581 followers on LinkedIn. Retrieved from "http://deeplearning.stanford.edu/wiki/index.php/Main_Page" The materials of this notes are provided from the ve-class sequence by Coursera website.

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