Neural Networks and Deep Learning

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 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.

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 adversarial networks (GANs) and natural language processing (NLP). Each module was presented clearly, with detailed explanations and practical examples that helped solidify my understanding of complex algorithms.Moreover, the course platform itself was user-friendly and conducive to learning. The video lectures were high-quality, and supplementary materials such as readings and additional resources were readily accessible. The discussion forums provided a supportive community where students could exchange ideas, seek help, and collaborate on projects.