Popular Deep Learning Courses of 2019
With deep learning and AI on the forefront of the latest applications and demands for new business directions, additional education is paramount for current machine learning engineers and data scientists. These courses are famous among peers, and will help you demonstrate tangible proof of your new skills.
Deep Learning is gaining more momentum and notoriety among the data science generation of this decade. A few years ago, it was not as mainstream as Machine Learning techniques, such as Logistic Regression and Random Forest for example.
Nowadays, it is all about Neural Networks, Activation Functions, Multiple Layers, Drop-out, etc. There is good reason for this one, which is simply, Deep Learning has shown to perform better than Machine Learning algorithms at times. The following courses are famous among peers for knowledge on the new wave of Deep Learning and AI.
This is undoubtedly the most famous course in Deep Learning thanks to the team behind it and one especially – Andrew Ng. This course goes in a depth and breadth of different topics of Deep Learning that it is suitable for a person with limited Programming experience can understand. There is an undoubted necessity to have some mathematical/statistical background/understanding to be able to follow the course. They have 3 specialization tracks: Deep Learning, TensorFlow, and AI for Everyone.
Udacity is more of a school to pick up programming for different areas of computer science/software development. The Udacity School of AI caters to potential students with some background in math/stats and as well as more business-focused people in AI and Deep Learning. It offers free and paid courses. It has different pathways to earning their nano degrees such as Machine Learning Engineer, Deep Learning Engineer, AI Specialist, and Quantitative Analyst.
NVIDIA needs no introduction to deep learning enthusiasts. Their GPUs have been configured on millions of PCs, laptops, and cloud computing environments that it is almost becoming a norm to have one of there GPUs. The institute partners with universities to help train the next generation of deep learning and AI experts. For individuals wanting to learn, the institute redirects to NVIDIA for Developers for deep learning training. As the name suggests there may be a need for a more technical computing background for this training.
IBM is a namesake in Artificial Intelligence. This is a 2-4 month online course to be taken at your own pace and gives exposure to a variety of algorithms and technologies. Some of the most attractive parts of this course will be the use of PyTorch and TensorFlow (de facto deep learning libraries today) and learning the concepts of Neural Networks for supervised and unsupervised learning. The course is offered on edX.org.
This course is offered by IBM as a beginner level course on Coursera. People with no background in AI or any technological background can still apply and learn the concepts of AI. This may be a good course to start with before diving into more advanced courses.
Although not AI or Deep Learning specifically, this is a popular machine learning course that teaches the concepts necessary to advance into Deep Learning and AI. It is also online and takes approximately 8 months to complete. It is at the Intermediate level. In this course, students are expected to learn both supervised and unsupervised learning methods.
This is a much shorter course than the rest of the courses stated here. In this course, you will learn the fundamentals of neural networks using Keras 2.0. It is a popular course for beginners in Deep Learning but will require knowledge of Python and Machine Learning to be understood.
This is more of a collection of courses by Brandon Rohrer, Principal Data Scientist at iRobot. His collection of courses is taught by him personally and contains a variety of topics including Deep Learning, Machine Learning, Statistics, etc.
Summary of Popular Deep Learning Courses
These courses can give the student new knowledge of Deep Learning. A hands-on approach is the best way to start implementing the algorithms and getting started on your journey. For employers, in addition to an educational verification, students that show the ability to conduct their own projects and organize them in a way to be shared among their peers are more likely to be able to have the chance to enter industry. While the demand may seem high for these specialists, a business sometimes needs tangible proof of the application of your knowledge and not just the proof of knowledge itself.
Original. Reposted with permission.
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