Neural Networks and Deep Learning
DeepLearning.AI and Stanford University via Coursera
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Overview
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
Syllabus
- Introduction to Deep Learning
- Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
- Neural Networks Basics
- Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
- Shallow Neural Networks
- Build a neural network with one hidden layer, using forward propagation and backpropagation.
- Deep Neural Networks
- Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.
Taught by
Andrew Ng
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Reviews
4.8 rating, based on 16 Class Central reviews
4.9 rating at Coursera based on 121522 ratings
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Neural Networks and Deep Learning is the first course in a new deep learning specialization offered by Coursera taught by Coursera founder Andrew Ng. The 4-week course covers the basics of neural networks and how to implement them in code using Pyth…
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The first excellent course of Andrew Ng's specialization on deep learning. WOW, this guy, the godfather of machine learning education (and co-founder of Coursera), knows how to educate the masses on one of the hottest technology topics in recent yea…
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Having completed his classic Machine Learning course a few months earlier, I had all the concepts and intuitions still fresh in my mind, so I could go quickly through the lectures, quizzes and assignments. I really enjoyed it and highly recommend it for anyone interested on ML, Deep Learning and AI! I'm doing the entire specialization and couldn't be more satisfied!
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First and foremost, the course content was comprehensive and well-structured. It covered fundamental concepts such as neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced topics like generative advers…
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I particularly enjoyed Andrew Ng's first course of the Deep Learning specialization because of its interactivity. Like any other programming course should be, we had to complete programming assignments as Jupyter Notebooks in the browser. We did not…
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It is designed for beginners in deep learning who have a background in basic python and linear algebra. Well planned, lets you develop intuitions about neural networks , also has optional video series on heroes of deep learning which is quite cool. People with some knowledge on NN might find it slow, but a good refresher. The Deep Learning specialization, which it is part of, is quite comprehensive too!
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It's a great course to understand basics of deep learning with a detailed walkthrough of gradient descent algorithm, forward propagation, backward propagation, cost and activation functions with logistic regression as a starting point.
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I took this course with some prior background in the subject, including Python deep learning libraries. This is rather math (calculus) heavy course, but in order to understand the basic concepts and logic, and complete the course, one does not need to fully understand how formulas work - just what is the objective of using this step or that step in the algorithm. Overall, one of the best introductory materials on deep learning. For those still struggling I recommend to star with Khan's Academy intro lectures on deep learning.
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Its great ! It teaches you to build a simple neural network from scratch, the assigbments were very illustrative and it was a very good thing that the assignments are solved on the cloud using jupyter
Notebooks without the need to download the data -
The Best Course on the internet to study about Artificial Neural Networks,you just need to know basic high school calculus and linear algebra to finish this course.Well structured and the programming assignments are so helpful!
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Great course. Builds the concepts step by step.
End of the course have a good comfort of forward prop , back prop and build a shallow neural network and deep neural network using python numpy. Highly recommend for anyone pursuing deep learning -
The course was good, Andrew NG is undoubtedly a great mind with too much knowledge, however, all the lessons were written on a white board. Is not that bad, but is not the easiest way to learn nevertheless.
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It is good course even though there is redundancy in it. I guess Andrew Ng repeats the concepts he finds important so students can understand. But that much repetition kills the flow, in my opinion.
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Great course! I really liked how systematically Andrew Ng goes through this broad field. You probably won't learn everything there is in the real of Deep Learning just by taking this course (I think that would be impossible) but you will get a rock solid (trusted) foundation for the important parts to expand that knowledge and keep up with latest progress on your own afterwards.
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Great introduction to the nuts and bolts of neural networks. Not math intensive but enough to give you more than an intuition of what’s happening under the hood. Notebooks with boilerplate code allow for targeted and efficient learning.
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Totally enjoyed this class! I consider Andrew Ng one of the best instructors in this field. The class will actually have you write code weekly and your own deep neural network. Highly recommend it.