If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.
In this course, you will learn the foundations of deep learning. 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 surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.
This is the first course of the Deep Learning Specialization.
Introduction to deep learning Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
Neural Networks Basics Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
Shallow neural networks Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
Deep Neural Networks Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
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 Python and numpy. The course page states that it only requires basic Python
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 Python and numpy. The course page states that it only requires basic Python programming knowledge, although any experience you have with machine learning, linear algebra and calculus will be helpful with gaining a deeper understanding of the material. You can access the quizzes and programming assignments without paying for the full course, but if you want to submit them for grading and get credit as having completed the course, you have to pay for the certificate.
Neural Networks and Deep Learning starts with a short introduction to deep learning in week 1, followed by 3 full weeks that build your understanding of neural networks by starting with logistic regression implemented with the same structure as a neural net in week 2, shallow nets in week 3 and deep nets in week 4. Key topics include computational graphs and derivatives on graphs, gradient descent, vectorizing code, neural network representations, activation functions, backpropagation and deep nets. The course touches on high level concepts and considerations to frame learning, but the majority of the content focuses on the low-level nuts and bolts of neural network structure and how to translate it into code.
Each week after the first has roughly 1-2 hours of lecture split up into 5 to 15 minute video segments. In each segment, Andrew Ng appears on screen and gives a brief overview of what the the video is going to cover and then he discusses the topic with voice-overs while writing on white slides, followed by a brief outro where he reappears and summarizes key takeaways. There is a lot of handwritten information and notation in the lectures, which means some students may find certain lectures difficult (or boring) to follow, but he explains things very well and the notation is there to help you gain a concrete understanding of the structure of neural nets and prepare you for working with them in the programming assignments. The production value of the videos is fairly low as the intros and outros seem to be recorded with a non wide screen SD camera and the vast majority of content is simply Ng writing on mostly blank slides. The production style is reminiscent of his original machine learning MOOC which was released back in 2012. Still, the logical organization of the content combined with Ng's masterful knowledge and lucid explanations means the relatively rudimentary production doesn't detract from the course's value. Weeks 1-3 also include an optional guest lectures with different "heroes of deep learning."
The programming assignments in Neural Networks and Deep Learning are very well done, providing great instructions, explanations and examples. You can access all of the assignments as a freeware student, so even though the course won't be listed as completed when you finish, you can still work through them and learn all the same things as paying students. The assignments are heavily structured, giving students complete code skeletons of all required functions and only requiring students to implement specific key lines of code which are described in detail. In other words, most of the difficulty in implementing neural nets--such as the logic and structure of the code and aligning matrix dimensions--is taken care of for you so you don't need to be a strong programmer to complete the assignments. This keeps the assignments moving along at a nice pace and should help keep students from getting stuck for too long and while you may struggle to implement neural nets from scratch yourself after completing this course, it shows you the tools you would need to do it. And perhaps more importantly, it gives you insight into how neural nets are working under the hood, which is good to know even if you end up using a package to build them.
Neural Networks and Deep Learning is the best introductory course on neural networks on any of the main MOOC platforms that is accessible to about as broad a group of students as possible given the nature of the material. The course isn't perfect: notation-heavy videos can get tedious and it sometimes eschews mathematical details. It also makes a few questionable decisions such as putting a 40 minute interview of Geoffrey Hinton at the end of the first week, most of which you will not understand unless you've seen neural networks before and have familiarity with his work. That said, if you want to learn about neural networks and how to make them in code, this is the right place to start.
I give Neural Networks and Deep Learning 5 stars out of 5: Excellent.
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 have to install anything on the computer so there were virtually no ha
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 have to install anything on the computer so there were virtually no hardware or software requirements.
The assignments blend well with the lectures and there is a lot of code that's already included so you would have to work your way out with the rest.
There were some issues with grading the assignments, but after a few submissions, the grader graded them correctly.
This course is complex, as it requires some solid knowledge of linear algebra, calculus, and Python programming. So, I would say it's not for beginners.
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 refresh
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!
Adail Muniz Retamalcompleted this course, spending 4 hours a week on it and found the course difficulty to be medium.
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!
Vijayabhaskarcompleted this course, spending 4 hours a week on it and found the course difficulty to be medium.
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!
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.