Deep Learning Specialization on Coursera: Is it for you?

Over the past 18 months, like most of the tech-savvy world, I’ve embarked on a journey with AI, particularly since the emergence of ChatGPT. The capabilities of this AI were nothing short of impressive, yet my engineering background urged me to peel back the layers and understand the mechanics behind such groundbreaking technology.

In a post over a decade ago, I encouraged my readers to try Coursera, and mentioned in particular the Machine Learning class led by Stanford professor Andrew Ng. This course laid the foundation for my understanding of learning algorithms, introducing neural networks through the task of recognizing handwritten numbers. The sophistication of these early models was intriguing and served as a precursor to my current exploration of AI.

My goal was to dive deeper into AI and Machine Learning, focusing specifically on neural networks. I knew jumping straight into understanding something as complex as ChatGPT wasn’t realistic. I needed a solid grounding in neural networks and deep learning first.

I also wanted a course that was hands-on and technical. It was important for me not just to get the concepts but to see the math that powers these models and to get my hands dirty by coding some examples myself.

So, I headed back to Coursera, looked at the different options, and decided to go for Andrew Ng’s “Deep Learning Specialization“.

Coursera: an update

Since my last post about Coursera, the platform has significantly evolved. The option to “audit” courses for free remains, allowing learners to access video content but now comes without the perks of grading, certification, or hands-on programming assignments. For those seeking a more immersive experience, Coursera now offers paid subscriptions granting access to graded assignments and certificates. These subscriptions are done either for a specific course, or under the Coursera Premium Program which gives access to plethora of courses, akin to a membership model like PlayStation Plus.

Coursera has also introduced “Specializations”, a curated series of courses around a specific theme. Completing a Specialization yields a comprehensive certificate, signifying mastery over the subject area. The Specialization I discuss below cost me around $50 per month. For specializations, subscription needs to continue until the entire series is completed. I am not entirely sure of the implication of cancelling the subscription before completing it entirely.

Why the Deep Learning Specialization?

The specialization, offered under the banner, includes five pivotal courses:

  1. Neural Networks and Deep Learning
  2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
  3. Structuring Machine Learning Projects
  4. Convolutional Neural Networks
  5. Sequence Models

This post isn’t meant to delve into the depths of these topics but rather to shed light on the quality of the courses and assist in determining if this specialization aligns with your learning goals.

Firstly, a solid mathematical foundation, particularly in linear algebra, is essential for these courses. They are designed for engineers or individuals comfortable with matrix multiplications and vector manipulations. The programming assignments in the initial three courses are manageable, but the complexity significantly ramps up in the latter two, especially for those not well-versed in Python and vector manipulations.

For those lacking these skills, I understand alternatives are available on Coursera or, catering to beginners or those seeking less technical content.

Every course is broken down into weekly modules. Andrew Ng presents the content in a series of videos, using downloadable slides annotated with his notes. To finish a module, you need to:

  • Pass a Theoretical Quiz: This usually means answering about 10 multiple-choice questions correctly, hitting at least an 80% score.
  • Complete Programming Assignments: These are done in an online Jupyter notebook that guides you through coding examples related to that week’s topic. The beauty of these notebooks is that they’re online – no need to install anything, a huge plus compared to courses that require you to set up your environment.

The specialization quite neatly divides into two segments. The first three courses serve as an introduction to neural networks and deep learning, discussing practical aspects like training and testing models and setting realistic objectives. Theoretical quizzes are relatively easy, and the programming assignments almost tell you exactly what to do step by step.

The final two courses are particularly engaging, introducing complex network architectures and providing a blend of theoretical knowledge and practical coding exercises. While the quizzes remain relatively straightforward, the programming tasks demand a deeper understanding of specific Machine Learning libraries like TensorFlow.

Professor Andrew Ng‘s teaching style is a true highlight of the course. He excels at demystifying complex topics, starting with giving students an intuitive grasp of the concepts before diving into the technical details. This approach is especially beneficial for abstract topics that might otherwise seem daunting. What’s more, he consistently links theory to real-world applications, clarifying which concepts are crucial and which are more supplementary. His emphasis on the practical application of technology is insightful, like his discussions on the importance of avoiding biases, such as gender bias, in model creation. This level of teaching is nothing short of top-notch.

It’s essential to know that this specialization doesn’t center around Generative AI, which is quite the buzzword these days. It only starts to scratch the surface towards the final class’s end. But for me, this wasn’t a setback. I was keen on grasping the core concepts and the various advancements leading up to Gen-AI. I was truly content with what the specialization offered, and if you’re on the same page with what you want to learn, I’d definitely recommend taking up this specialization.

To wrap up, it’s vital to set realistic expectations about what comes after finishing these courses. They lay down a strong theoretical base, which is fantastic. But, it’s pretty clear that putting this theory into practice, creating your own AI models or applications, requires a lot of hands-on work. As Professor Ng points out repeatedly, developing these applications is a process of trial, error, and heavy-duty computing. So, while the courses build a solid foundation, be prepared for the rigorous journey of turning theory into practice.

What About the Original Machine Learning Class?

The original Machine Learning class by Stanford appears to be phased out, replaced by the “Machine Learning Specialization“, consisting of three comprehensive courses:

  1. Supervised Machine Learning: Regression and Classification
  2. Advanced Learning Algorithms
  3. Unsupervised Learning, Recommenders, Reinforcement Learning

Although I haven’t personally taken these courses, they promise an updated curriculum delving deeper into machine learning concepts and enhanced programming assignments. For beginners in the field of machine learning, this specialization seems like the ideal starting point.

Embarking on this journey through Coursera’s Deep Learning Specialization has been both enlightening and challenging. The blend of theoretical knowledge and practical application in these courses provides a solid foundation for understanding complex AI models like ChatGPT. As the field of AI continues to evolve, the importance of continuous learning and staying abreast of the latest advancements cannot be overstated. Whether you’re a seasoned engineer or a curious enthusiast, the world of AI and Machine Learning offers a perpetual learning curve, inviting each of us to dive deeper and explore its limitless potential.