Deep Learning & Computer Vision: an Introduction
Ed-Next (Consult +)
Summary
Overview
Deep Learning & Computer Vision: an Introduction is the perfect course for students who want exposure to Machine Learning. This course will cover topics such as: Artificial Neural Networks, how to install Python, and Handwritten Digit Recognition.
Course Information
Requirements:
No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory.
Working knowledge of Python would be helpful if you want to run the source code that is provided.
Highlights:
Design and Implement a simple computer vision use-case: digit recognition
Confidently move on to more complex and comprehensive material on these topics
Grasp the theory underlying deep learning and computer vision
Understand use-cases for computer vision as well as deep learning
Target Audience:
Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
Engineers who want to understand or learn machine learning and apply it to problems they are solving
Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
Tech executives and investors who are interested in big data, machine learning or natural language processing
MBA graduates or business professionals who are looking to move to a heavily quantitative roleDeep Learning & Computer Vision: an Introduction is the perfect course for students who want exposure to Machine Learning. This course will cover topics such as: Artificial Neural Networks, how to install Python, and Handwritten Digit Recognition.
Course Information
Requirements:
No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory.
Working knowledge of Python would be helpful if you want to run the source code that is provided.
Highlights:
Design and Implement a simple computer vision use-case: digit recognition
Confidently move on to more complex and comprehensive material on these topics
Grasp the theory underlying deep learning and computer vision
Understand use-cases for computer vision as well as deep learning
Target Audience:
Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
Engineers who want to understand or learn machine learning and apply it to problems they are solving
Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
Tech executives and investors who are interested in big data, machine learning or natural language processing
MBA graduates or business professionals who are looking to move to a heavily quantitative roleThis course will get you started on two of the hottest topics in Machine Learning. Go beyond the hype, and get started learning how to design and implement a simple computer vision use-case and so much more! With nearly 2 hours of expert-led instruction, by the time you’ve completed this course, you will have a firm grasp on the theory underlying both deep learning and computer vision. Length: 1.5 hrs | Supplemental Material included
Description
Deep Learning & Computer Vision: an Introduction is the perfect course for students who want exposure to Machine Learning. This course will cover topics such as: Artificial Neural Networks, how to install Python, and Handwritten Digit Recognition.
Course Information
Requirements:
No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory.
Working knowledge of Python would be helpful if you want to run the source code that is provided.
Highlights:
Design and Implement a simple computer vision use-case: digit recognition
Confidently move on to more complex and comprehensive material on these topics
Grasp the theory underlying deep learning and computer vision
Understand use-cases for computer vision as well as deep learning
Target Audience:
Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
Engineers who want to understand or learn machine learning and apply it to problems they are solving
Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
Tech executives and investors who are interested in big data, machine learning or natural language processing
MBA graduates or business professionals who are looking to move to a heavily quantitative roleDeep Learning & Computer Vision: an Introduction is the perfect course for students who want exposure to Machine Learning. This course will cover topics such as: Artificial Neural Networks, how to install Python, and Handwritten Digit Recognition.
Course Information
Requirements:
No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory.
Working knowledge of Python would be helpful if you want to run the source code that is provided.
Highlights:
Design and Implement a simple computer vision use-case: digit recognition
Confidently move on to more complex and comprehensive material on these topics
Grasp the theory underlying deep learning and computer vision
Understand use-cases for computer vision as well as deep learning
Target Audience:
Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
Engineers who want to understand or learn machine learning and apply it to problems they are solving
Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
Tech executives and investors who are interested in big data, machine learning or natural language processing
MBA graduates or business professionals who are looking to move to a heavily quantitative roleThis course will get you started on two of the hottest topics in Machine Learning. Go beyond the hype, and get started learning how to design and implement a simple computer vision use-case and so much more! With nearly 2 hours of expert-led instruction, by the time you’ve completed this course, you will have a firm grasp on the theory underlying both deep learning and computer vision. Length: 1.5 hrs | Supplemental Material included
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