Into the Unknown
Deep Learning SpecializationDeepLearning.AI Mission complete! This is probably the most popular specialization on Coursera. Cutting-edge topics, high quality programming assignments鈥攂oth newbies and veterans can benefit. I found the knowledge learned can be used directly in daily work and also let me get prepared for the future. Thank you Andrew Ng, much obliged! My Specialization Certificate…
Sequence ModelsDeepLearning.AI When I finished this course, I picked up my phone and asked “Hey Siri, what is sequence model”? Guess what? She understand and she showed me web page of this course exactly. Pretty cool! This course reveals what is under the hood: recurrent neural network, word embedding, beam search, attention model, etc. I…
Convolutional Neural NetworksDeepLearning.AI This great course helps you grasp fundamentals of CNN and get familiar with the most recent hot applications (self-driving, face recognition, neural style transfer) in just a few weeks. Programming assignments are well-prepared, easy to complete, and more about how to leverage existing frameworks / algorithms / models (e.g. YOLO). You will have…
Probabilistic Graphical Models SpecializationStanford University A souvenir from Coursera for those days alone and nights sleepless, dedicated to Probabilistic Graphical Models Specialization. In the past half year I learned a lot, thank you Prof. Daphne Koller and Coursera! My Specialization Certificate Courses I am Kesler Zhu, thank you for visiting. Checkout all of my course…
Probabilistic Graphical Models 3: Learning (with Honors)Stanford University This course “PGM Learning” almost killed me – I restudied the 2nd course “PGM Inference” and Bayesian methods before even making progress. The last programming assignment (build a HMM + CLG model for Kinect body poses and actions) is the toughest one in entire specialization, but invaluable…
Bayesian Methods for Machine Learning (with Honors)Higher School of Economics This is an excellent course from Russia and almost only for those who love mathematics and enjoy the procedure of derivation. Topics range from basic Expectation-Maximization to advanced such as Variational Autoencoder, and Bayesian Optimization. Programming assignments cover a few powerful libraries – PyMC, GPy,…
Neural Networks for Machine LearningUniversity of Toronto Taught by ‘godfather’ Geoffrey Hinton, this 16-week course is definitely a masterpiece, which covers almost entire modern AI history ever since 1960 – tons of theories, equations, derivations, examples, and anecdotes. The topics like Boltzmann machine, belief nets, auto-encoder are rather tough, but they help me ‘re-build’ my…
Applied Machine Learning in PythonUniversity of Michigan This is a nice course about scikit-learn – a stable, powerful and still indispensable library nowadays. The course covers many classic models, and how to use them efficiently. Various evaluation curves and model selection are what I like most. The comparison between scikit-learn with other new libraries (such…
Structuring Machine Learning ProjectsDeepLearning.AI This is a short-but-effective course about strategy. The topics are all critical for helping team analyze errors, select direction, make progress and archive goals effectively. The methods demonstrated when doing analysis are very impressive. I am afraid these knowledge will never be available in any academic textbook, you can only learn…
Improving Deep Neural NetworksDeepLearning.AI This course guides you through the “mist” of optimizing deep learning neural networks, very clear and joyful. RMSprop / Adam algorithms, Batch Norm, everything is elaborated carefully. Final programming assignment is to use TensorFlow to create a neural network. Fantastic course! I am very grateful. My Certificate Related Specialization I am…
Probabilistic Graphical Models 2: Inference (with Honors)Stanford University A very challenging course! The programming assignments (clique tree, sampling methods) almost killed me, but gladly I made it. Happy New Year 2018, more challenges coming soon. My Certificate Related Specialization I am Kesler Zhu, thank you for visiting. Checkout all of my course reviews at http://KZHU.ai
Probabilistic Graphical Models 1: Representation (with Honors)Stanford University HAHA, certificate with Honors! PGM, it is said this is one of the hard courses in Stanford, but totally worth it! All Programming Assignments are challenging (Bayesian net, Markov net/CRF, and decision making), but very essential to help understand how PGM works. I definitely will enroll the…
Neural Networks and Deep LearningDeepLearning.AI GREAT course! Andrew elaborated the neural network for Deep Learning in an extremely clear and well-organized way! 鉂わ笍 Follow his guidance and instructions, not only I comprehended the intricacies of deep learning, but also a full-fledged neural network can be implemented from scratch by myself in Python! My Certificate Related…
Machine LearningStanford University The course is taught by Andrew Ng. Probably this is the most popular and famous MOOC course on the Internet. It is also the primary reason why I signed up and started my journey on Coursera. I spent almost 3 months to finish this course, I still remember how thrilled I was…