Stanford Professional Certificate In Artificial Intelligence – Between a full-time job and a kid at home, I spend my free time learning ideas about cognitive science and AI. Every now and then a great paper/video/course comes out and you are instantly hooked.
Of course, as soon as the course was posted on Coursera, I signed up and spent the last 4 nights watching the lectures, working on tests, and scheduling assignments.
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DL professionals and ML engineers often spend most days at an abstract level of Keras or TensorFlow. But it’s good to take a break every now and then to learn the learning algorithms and do the backpropagation by hand. It’s fun and incredibly useful!
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Venture is a bottom-up approach to neural network learning: powerful non-linear learning algorithms, at a beginner to intermediate level.
In classic NG style, the course is delivered through carefully chosen curriculum, well-timed videos and precisely positioned information. Andrew picks up where his classic ML course left off and introduces the idea of neural networks using a single neuron (logistic regression) and slowly adding complexity: more neurons and layers. At the end of 4 weeks (run 1), a student receives all the fundamental ideas needed to build a dense neural network, such as cost/loss functions, learning iteratively using gradient descent and vectorized parallel Python (nupy) implementations .
Andrew patiently explains the necessary math and programming concepts in a carefully planned order and well-regulated pace suitable for students who may be rusty on math/coding.
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Lectures are delivered with presentation slides on which Andrew writes using digital pens. It seemed like an effective way to get the listener to focus. I felt comfortable watching videos at 1.25 or 1.5x speed.
Quizzes are placed at the end of each lesson section and are in the format of multiple choice questions. If you watch the videos once, you should be able to quickly answer all the questions on the quiz. You can try tests multiple times and the system is designed to keep your score high.
Tasks have a nice guided sequential structure and you don’t need to write more than 2-3 lines of code in each section. If you understand concepts like vectorization intuitively, you can complete most programming sections with just 1 line of code.
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After coding the assignment, it takes 1 click to submit your code to the automated grading system that returns your score within minutes. Some tasks have time restrictions, for example three attempts in 8 hours, etc.
Jupyter notebooks are well designed and run smoothly. The instructions are accurate and it looks like a polished product.
Anyone interested in understanding what neural networks are, how they work, how to build them, and the tools available to bring your ideas to life.
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If your math is rusty, don’t worry: Andrew explains all the necessary calculus and provides derivations at every turn so you can focus on building the network and implementing your ideas in code.
If your programming is rusty, this is a good coding assignment to teach you about numpy. But I recommend learning Python first at Codecademy.
Jeremy’s FAST.AI course puts you in the driver’s seat right from the start. It teaches you how to move the steering wheel, how to press the brake, accelerator, etc., then gradually explains more details about how the car works: that turning the wheel makes the car turn, that pressing the brake pedal makes you accelerate and pairs etc. Delve into the inner workings of the car and by the end of the course you know how the internal combustion engine works, how the fuel tank is designed, etc.
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The aim of this course is to get you driving. You can choose to stop at any time after you can drive reasonably well; no need to learn how to build/repair the car.
Andrew’s DL course does all of this, but in reverse order. First it teaches you about the internal combustion engine. He continues
If you haven’t done any machine learning before, don’t take this course first. The best starting point is Andrew’s original ML course on Coursera.
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After completing this course, try to complete part 1 of Jeremy Howard’s excellent deep learning course. Jeremy teaches the basic top-down principles of deep learning for absolute beginners.
Once you are comfortable building deep neural networks, it makes sense to take this new deeplearning.ai course specialization that fills any gaps in your understanding of the underlying details and concepts.
2. Andrew emphasizes the engineering aspects of deep learning and gives many practical tips to save time and money: The third course of the DL specialization has been very useful for my role as an architect leading engineering teams.
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– He is brutally honest about the reality of designing and training deep networks. At some point I felt
4. Squashes All Hype Around DL and AI – Andrew makes moderate and careful comments about the proliferation of AI hype in the mainstream media and by the end of the course it’s pretty clear that DL is nothing like the Terminator.
7. Nice, consistent and useful notation. Andrew is striving to establish a new nomenclature for neural networks and I think he may be quite successful in this endeavor.
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8. Teaching style that is unique to Andrew and derived from ML: I can feel the same excitement I felt in 2013 when I took his original ML course.
9. Interviews with deep learning heroes are refreshing – it’s motivating and fun to hear personal stories and anecdotes.
2. Good tools are important and will help speed up the learning process. I bought a digital pen after watching Andrew teach with one. It helped me work more efficiently.
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3. There is a psychological reason why I recommend the Fast.ai course before this one. Once you find your passion, you can learn without inhibitions.
) or the mathematical symbols. If you take a leap of faith and pay attention to the lectures, Andrew shows why symbols and notation are really useful.
Update: Thanks for the very positive response! Many people ask me to explain gradient descent and differential calculus. Disclosure: Class Central is student-supported. When you shop through links on our site, we may earn an affiliate commission.
Stanford Moves To Edx, Stops Offering Free Certificates — Class Central
[Update: January 28, 2020] Today, Stanford removed most of its free certification courses from Lagunita. This is likely a result of the university becoming an institutional member of EDX. Some of the Stanford courses that were offered at Lagunita yesterday have now been transferred to EDX. Unfortunately, at EDX, they do not include a free certificate of completion. You can find more details here.
We recently wrote about CS50, Harvard’s introduction to computer science, which allows students to earn a free certificate. This time, let’s review Stanford’s free certificate offer.
Stanford University offers 69 free online courses through Lagunita, Stanford’s own MOOC platform, which is based on Open EdX. Of the 69 courses, 44 offer a free certificate of completion, including IT, engineering and medical courses.
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The courses are autonomous and include self-rated assessments. In some courses, students can earn a special mention if their final course score is high enough. For example, in the statistical study, 50% give you an ordinary certificate, but 90% give you a “distinction”.
Stanford’s free certificate courses include Algorithms: Design and Analysis, which is often considered one of the best introductions to algorithms and data structures available online, primarily by Class Central founder Dhawal Shah, who recently wrote about it.
This course is also offered as a specialization on Coursera. But there, equivalent certificates cost $ 200. In Lagunita, they are free (although they are not verified by identification). This is what they look like:
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Note that Stanford prefers to call them “statements of achievement” rather than “certificates,” probably to distinguish them from the university’s paid certificates.
Below is a complete list of Stanford’s free certificate courses. For a more complete picture of the university’s online offerings, explore Stanford’s online courses in Class Central. This one was long overdue, but guess what, it’s never too late to #celebrate. After 9 courses, 89 assignments, close to 1000 hours of study, many industry-leading case studies, and life-changing interactions with my professors, course facilitators, and the amazing colleagues at LEAD, I am excited to share that I have finally found the Graduate School of Stanford University. Business Leadership Program.
I am very grateful to my Stanford University community for introducing me to LEAD. The ever-reliable Marine Llikian, who helped me every step of the way on my journey.
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Professor Peter DeMarzo and Professor Sarah Sole, who laid the foundation for the LEAD program and created an opportunity for leaders like me to pursue and gain exposure to the global wealth of knowledge through LEAD.
My LEAD faculty like Prof. Haim Mendelson, Prof. Jeffrey Pfeffer, Prof. Jesper Sorensen, Prof. Stefanos Zenios, Prof. Margaret A. Neale, Prof. Jennifer Acker, and Prof. Naomi Bagdonas. Each of them left a lasting impression on the way I conduct myself every day at work and in my personal life. Thank you professors
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