– A slide from one of the first lectures – These are a few comments about my experience of taking the Deep Learning specialization produced by deeplearning.ai and delivered on the Coursera platform. But never it was so clear and structured presented like by Andrew Ng. This is my note for the 3rd course of TensorFlow in Practice Specialization given by deeplearning.ai and taught by Laurence Moroney on Coursera. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. Above all, I cannot regret spending my time in doing this specialization on Coursera. For example, if there’s a problem in variance, you could try get more data, add regularization or try a completely different approach (e.g. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. The knowledge and skills covered in this course. The Deep Learning Specialization is the group of courses by Andrew Ng and his staff over at deeplearning.ai, which is a comprehensive course that starts at the extreme basics of Neural Networks (a part of Machine Learning) and ends up teaching you concepts applicable in various cutting-edge fields of AI. Optional: Take the DeepLearning.AI TensorFlow Developer Professional Certificate. Design and Creativity; Digital Media and Video Games Where he essentially starts with the basics of neural networks from scratch in numpy, and moves to more advanced topics. But doing the course work gets you started in a structured manner — which is worth a lot, especially in a field with so much buzz around it. And finally, a very instructive one is the last programming assignment. Started a new career after completing this specialization. Apart of their instructive character, it’s mostly enjoyable to work on them, too. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. You’ll learn about Logistic Regression, cost functions, activations and how (sochastic- & mini-batch-) gradient descent works. When I’ve heard about the deeplearning.ai specialization for the first time, I got really excited. On a professional level, when you are rather new to the topic, you can learn a lot of doing the deeplearning.ai specialization. As I was not very interested in computer vision, at least before taking this course, my expectation on its content wasn’t that high. When I felt a bit better, I took the decision to finally enroll in the first course. Furthermore a positive, rather unexpected sideeffect happened during the beginning. As a sidenote, the first lectures quickly proved the assumption wrong, that the math is probably too advanced for me. Also the concept of data augmentation is addressed, at least on the methodological level. Thereby you get a curated reading list from the lectures of the MOOC, which I’ve found quite useful. I personally found the videos, respectively the assignment, about the YOLO algorithm fascinating. The assignments in this course are a bit dry, I guess because of the content they have to deal with. Cost: $59 per month after a 7-day free trial, financial aid available through application. With a superficial knowledge on how to do matrix algebra, taking derivatives to calculate gradients and a basic understanding on linear regression and the gradient-descent algorithm, you’re good to go — Andrew will teach you the rest. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. So it became a DeepFake by accident. So I had to print out the assignments, solved it on a piece of paper and typed-in the missing code later, before submitting it to the grader. You’ve to build a LSTM, which learns musical patterns in a corpus of Jazz music. My subjective review of this course; Summary: This course is the first course in TensorFlow in Practice Specialization offered by deeplearning.ai. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models.. In the context of YOLO, and especially its successors, it is quite clear that speed of prediction is also an important metric to consider. The most useful insight of this course was for me to use random values for hyperparameter tuning instead of a more structured approach. Nonetheless, I’m quite aware that this is definitely not enough to pursue a further career in AI. In this course you learn good practices in developing DL models. The methodological base of the technology, which is not in scope of the book, is well addressed in the course lectures. If you want to break into AI, this Specialization will help you do so. That might be because of the complexity of concepts like backpropation through time, word embeddings or beam search. - Process text, represent sentences as vectors, and train a model to create original poetry! Perhaps you are only interested in a specific field of DL, than there are also probably more suitable courses for you. What you learn on this topic in the third course of deeplearning.ai, might be too superficial and it lacks the practical implementation. Deep Learning is one of the most highly sought after skills in tech. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. To begin, you can enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. What I’ve found very useful to deepen the understanding is to complement the course work with the book “Deep Learning with Python” by François Chollet. Some videos are also dedicated to Residual Network (ResNet) and Inception architecture. Coursera Specialization is a series of courses that help you master a skill. Once I felt a bit like Frankenstein for a moment, because my model learned from its source image the eye area of a person and applied it to the face of the person on the input photo. Nonetheless, it turns out, that this became the most valuable course for me. Go to course 1 - Intro to TensorFlow for AI, ML, DL. Udacity, Fast.ai, and Coursera / Deeplearning.ai are releasing new courses today aimed at training people how to use TensorFlow 2.0 and TensorFlow Lite. TensorFlow in Practice Specialization on Coursera Time: 3 weeks (advanced user) to 3 months (beginner). “Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning” is the first course of “TensorFlow in Practice” specialization from deeplearning.ai in Coursera. By the end of this program, you will be ready to: - Build and train neural networks using TensorFlow, - Improve your network’s performance using convolutions as you train it to identify real-world images, - Teach machines to understand, analyze, and respond to human speech with natural language processing systems. Bihog Learn. And I think also, the amount of these non-trivial topics would be better split up in four, instead of the actual three weeks. You learn how to develop RNN that learn from sequences of characters to come up with new, similar content. I’ve found the review on the first three courses by Arvind N very useful in taking the decision to enroll in the first course, so I hope, maybe this can also be useful for someone else. If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. Currently doing the deeplearning.ai specialization on coursera with Andrew ng. minimize the loss. Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications. With the assignments, you start off with a single perceptron for binary classification, graduate to a multi-layer perceptron for the same task and end up in coding a deep NN with numpy. TensorFlow in Practice Specialization. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device. It is an introduction to TensorFlow as the course name implies it. DeepLearning.AI TensorFlow Developer Professional Certificate, 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. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow … I completed and was certified in the five courses of the specialization during late 2018 and early 2019. Art and Design. Check the codes on my Github. You build a Trigger Word Detector like the one you find in Amazon Echo or Google Home devices to wake them up. And on the other hand, the practical aspects of DL projects, which are somehow addressed in the course, but not extensivly practised in the assignments, are well covered in the book. Especially the data preprocessing part is definitely missing in the programming assignments of the courses. If you want to have more informations on the deeplearning.ai specialization and hear another (but rather similar) point of view on it: I can recommend to watch Christoph Bonitz’s talk about his experience in taking this series of MOOCs, he gave at Vienna Deep Learning Meetup. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Although it was for me the ultimate goal in taking this specialization to understand and use these kinds of models, I’ve found the content hard to follow. Yes, if you paid a one-time $49 payment for one or more of the courses, you can still subscribe to the Specialization for $49/month. If you’re a software developer who wants to get into building deep learning models or you’ve got a little programming experience and want to do the same, this course is for you. 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. DeepLearning.AI TensorFlow Developer Professional Certificate ... TensorFlow in Practice Specialization (Coursera) This certification is vital to developers who want to become proficient with the tools needed to build scalable AI-powered algorithms in TensorFlow. Build natural language processing systems using TensorFlow. Apprenez Tensorflow en ligne avec des cours tels que DeepLearning.AI TensorFlow Developer and TensorFlow: Advanced Techniques. Before starting a project, decide thoroughly what metrices you want to optimize on. The deeplearning.ai specialization is easily one of the best courses I've ever taken. Skip to content. You learn how to find the right weight initialization, use dropouts, regularization and normalization. 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. The most instructive assignment over all five courses became one, where you implement a CNN architecture on a low-level of abstraction. The deeplearning.ai specialization is dedicated to teaching you state of the art techniques and how to build them yourself. Mine sounds like this — nothing to come up with in Montreux, but at least, it sounds like Jazz indeed. It’s fantastic that you learn in the second week not only about Word Embeddings, but about its problem with social biases contained in the embeddings also. So I experienced this set of courses as a very time-effective way to learn the basics and worth more than all the tutorials, blog posts and talks, which I went through beforehand. On the other hand, be aware of which learning type you are. These videos were not only informative, but also very motivational, at least for me— especially the one with Ian Goodfellow. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. But going further, you have to practice a lot and eventually it might be useful also to read more about the methodological background of DL variants (e.g. The Machine Learning course and Deep Learning Specialization … Some experience in writing Python code is a requirement. The last one, I think is the hardest. Visit your learner dashboard to track your progress. Do I need to attend any classes in person? It was also enlightening that it’s sometimes not enough to build an outstanding, but complex model. Nontheless, every now and then I heard about DL from people I’m taking seriously. After that, I’ll conclude with some final thoughts. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. I strongly suggest the TensorFlow: Advanced Techniques Specialization course by deeplearning.ai hosted on Coursera, which will give you a foundational understanding on Tensorflow. Looking to customize and build powerful real-world models for complex scenarios? Go to course 2 - CNN in TensorFlow. © 2021 Coursera Inc. All rights reserved. So, I want to thank Andrew Ng, the whole deeplearning.ai team and Coursera for providing such a valuable content on DL. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. HLE) and training error, of course. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. In this fourth course, you will learn how to build time series models in TensorFlow. And doing the programming assignments have been a welcome opportunity to get back into coding and regular working on a computer again. If you’re a software developer who wants to get into building deep learning models or you’ve got a little programming experience and want to do the same, this course is for you. Normally, I enroll only in a specific course on a topic I wanna learn, binge watch the content and complete the assignments as fast as possible. LSTMs pop-up in various assignments. Andrew Ng is a great lecturer and even persons with a less stronger background in mathematics should be able to follow the content well. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. I solemnly pledge, my model understands me better than the Google Assistant — and it even has a more pleasant wake up word ;). And finally, my key take-away from this spezialization: Now I’m absolutely convinced of the DL approach and its power. Learn how to go live with your models with the TensorFlow: Data and Deployment Specialization. Its major strength is in the scalability with lots of data and the ability of a model to generalize to similar tasks, which you probably won’t get from tradtional ML models. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. I would say, each course is a single step in the right direction, so you end up with five steps in total. Courses. Basically, you have to implement the architecture of the Gatys et al., 2015 paper in tensorflow. In fact, with most of the concepts I’m familiar since school or my studies — and I don’t have a master in Tech, so don’t let you scare off from some fancy looking greek letters in formulas. This course is completely online, so there’s no need to show up to a classroom in person. But I can definitely recommend to enroll and form your own opinion about this specialization. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. Official notebooks on Github. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! DeepLearning.AI offers classes online only. 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