Deep Learning involves techniques that can’t be understood without an effective teacher.
I’ve taken many data science and Machine Learning related courses and audited portions of many more.
I know the options out there, and what skills are needed for learners preparing for a Data Scientist, Machine Learning Engineer or Deep Learning Scientist role.
Few weeks ago, I started creating a review-driven guide that recommends the best courses for each subject within Deep Learning and for this guide, I put a tremendous amount of efforts trying to identify every best deep learning course.
Deep learning offers enormous potential for creative applications and in this guide, for best Deep Learning Courses we interrogate what's possible.
Below, I’ve curated a selection of the best available courses in Deep Learning for beginners and experts who aspire to expand their minds.
1. An Introduction to Practical Deep Learning - Intel AI Academy
An Introduction to Practical Deep Learning is taught by AI Principal Engineers at Intel.
This course is very dense and informative that aims to help learners to grasp the basics of Deep Learning.
This course is primarily aimed at learner with some background in programming and understanding of basic calculus, but are new to the field deep learning.
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2. Introduction to Deep Learning - National Research University
Introduction to Deep Learning is an advanced 6-week course created by the National Research University Higher School of Economics.
This course teaches learners the basic understanding of modern neural networks and their applications in computer vision and natural language understanding.
This course is suitable for Developers, analysts, and researchers with the basic knowledge of Python, linear algebra and probability.
The topics covered in this course will help learners who are faced with tasks involving complex structure understanding such as image, sound and text analysis.
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3. Deep Learning Specialization - Andrew NG
Deep Learning CourseThis is one of the best and highly recommended Deep Learning Specialization, comprised of five courses taught be the AI Pioneer - Andrew Ng, Co-Founder of Coursera, DeepLearning AI and Adjunct Professor at Stanford University.
This specialization will help to learn the foundations of Deep Learning, understand techniques to build effective neural networks, and learn how to manage successful machine learning projects.
This specialization assumes that a learner has intermediate skills in Python and basic knowledge of statistics to understand and work on case studies from healthcare, autonomous driving, music generation, sign language reading, and natural language processing.
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4. Deep Learning with Keras - PluralSight
This course on Deep Learning with Keras is Created by Jerry Kurata, Technology Expert and best selling author of Machine Learning and Deep Learning Courses on Pluralsight and Coursera.
This course will get you up to speed with both the theory and practice of using Keras to create powerful deep neural networks.
And equip you with the various methods of using Keras for interconnecting various layers of neurons quickly and easily to form the structure of your deep neural networks.
This course is suitable for learners with a good knowledge of Python to work with Keras and will help gain the skills required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.
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These courses will prepare you for the Deep Learning role and help you learn more about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language, and human motion, and more.
If you know of any courses that I may have missed, please let me know in the comments below!
You may also be interested in starting with one of the Best Courses in Machine Learning to use as a springboard for a rewarding and lucrative career in the field of Machine Learning.
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Originally published at sinxloud.com - Nov 20, 2018