This school offers training in 3 qualifications, with the most reviewed qualifications being Deep Learning Specialization, convolutional neural networks with tensorflow and deeplearning.ai on Coursera. In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. TensorFlow in Practice Specialization. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. In this fourth course, you will learn how to build time series models in TensorFlow. You build one that writes a poem in the (learned) style of Shakespeare, given a Sequence to start with. In the DeepLearning.AI TensorFlow Developer Professional Certificate program, you'll get hands-on experience through 16 Python programming assignments. In previous courses I experienced Coursera as a platform that fits my way of learning very well. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. 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. And finally, a very instructive one is the last programming assignment. If you pay for one course, you will have access to it for 180 days, or until you complete the course. You build a Trigger Word Detector like the one you find in Amazon Echo or Google Home devices to wake them up. As you can see on the picture, it determines if a cat is on the image or not — purr ;). 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. You learn how to find the right weight initialization, use dropouts, regularization and normalization. This online Specialization is taught by three instructors. What’s very useful for newbies is to learn about different approaches for DL projects. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. DLI collaborated with Deeplearning.ai on the “sequence models” portion of term 5 of the Deep Learning Specialization. Also, this story doesn’t have the claim to be an universal source of contents of the courses (as they might chance over time). But I’ve never done the assignments in that course, because of Octave. Currently doing the deeplearning.ai specialization on coursera with Andrew ng. Natural Language Processing in TensorFlow | DeepLearning.ai A thorough review of this course, including all points it covered and some free materials provided by Laurence Moroney Pytrick L. Coming from traditional Machine Learning (ML), I couldn’t think that a black-box approach like switching together some functions (neurons), which I’m not able to train and evaluate on separately, may outperform a fine-tuned, well-evaluated model. Learn how to go live with your models with the TensorFlow: Data and Deployment Specialization. On the other hand, quizzes and programming assignments of this course appeard to be straight forward. Art and Design. 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. As its title suggests, in this course you learn how to fine-tune your deep NN. As you go through the intermediate logged results, you can see how your model learns and applies the style to the input picture over the epochs. Also, I thought that I’m pretty used to, how to structure ML projects. 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’ve to build a LSTM, which learns musical patterns in a corpus of Jazz music. I think it builds a fundamental understanding of the field. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. deeplearning.ai on Coursera. And from videos of his first Massive Open Online Course (MOOC), I knew that Andrew Ng is a great lecturer in the field of ML. 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. 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. When I felt a bit better, I took the decision to finally enroll in the first course. The most useful insight of this course was for me to use random values for hyperparameter tuning instead of a more structured approach. TensorFlow in Practice Specialization on Coursera Time: 3 weeks (advanced user) to 3 months (beginner). As a reward, you’ll get at the end of the course a tutorial about how to use tensorflow, which is quite useful for upcoming assignments in the following courses. Visit your learner dashboard to track your progress. This is strongly … 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. In this course you learn good practices in developing DL models. But this time, I decided to do it thoroughly and step-by-step, repectively course-by-course. I personally found the videos, respectively the assignment, about the YOLO algorithm fascinating. 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. I read and heard about this basic building blocks of NN once in a while before. I completed and was certified in the five courses of the specialization during late 2018 and early 2019. It probably will not make you a specialist in DL, but you’ll get a sense in which part of the field you can specialize further. I solemnly pledge, my model understands me better than the Google Assistant — and it even has a more pleasant wake up word ;). And of course, how different variants of optimization algorithms work and which one is the right to choose for your problem. 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