CTO at OpTeamizer Ltd
An NVIDIA Preferred Partner, Deep Learning AI Institute
A lecturer for the hi-tech industry and also in the academy
DLI-certified Instructor of CUDA and Deep Learning courses
NVIDIA DLI University Ambassador
More than 10 years of GPU development experience
Making many years of GPU development experience accessible to you and your team.
To register this course or get more information - contact us via contact form on this website or call us:
| ☎ +972 (54) 746-7477
| ☎ +972 (54) 213-1337
| ☎ +972 (3)-953-3365
About the course
Course Name: NVIDIA Fundamentals of Deep Learning for Multiple Data Types.
Duration: 1 day.
This workshop uses a series of hands-on exercises to teach deep learning techniques for a range of problems involving multiple data types. After a quick introduction to deep learning, you’ll advance to building deep learning applications for image segmentation, sentence generation, and image and video captioning, while learning relevant computer vision, neural network, and natural language processing concepts. At the end of the workshop, you’ll be able to assess a broad spectrum of problems where deep learning can be applied.
At the conclusion of the workshop, you’ll have an understanding of the fundamentals of deep learning for
multiple data types and be able to:
Implement common deep learning workflows such as image segmentation and text generation
Compare and contrast data types, workflows, and frameworks
Combine deep learning-powered computer vision and natural language processing to start solving sophisticated real-world problems that require multiple input data types
Introduction 15 mins
- Meet the instructor.
- Create an account at courses.nvidia.com/join.
Image Segmentation with TensorFlow 120 mins
- Compare image segmentation to other computer vision problems.
- Experiment with TensorFlow tools.
- Implement effective metrics for assessing model performance.
Intermission: Lunch included at Carlton Hotel. [120 mins]
Word Generation with TensorFlow 120 mins
- Learn about natural language processing (NLP) and recurrent neural networks (RNNs).
- Create network inputs from text data.
- Test with new data and iterate to improve performance.
Break [15 mins]
Image and Video Captioning [120 mins]
- Combine computer vision and natural language processing to describe scenes.
- Learn to harness the functionality of convolutional neural networks (CNNs) and RNNs.
Final Review [15 mins]
- Review key learnings and wrap up questions.
- Complete the assessment to earn a certificate.
- Take the workshop survey.
Lunch included at Carlton Hotel.
Enroll to receive price and course information.