Unable to find what you're searching for?
We're here to help you find itGetting Started with Deep Learning (NVIDIA) is an 8-hour course designed for those with a basic understanding of Python and familiarity with Pandas datastructures. You will explore deep learning through hands-on exercises in computer vision and natural language processing. The course covers fundamental techniques to train deep learning models, enhancing datasets through data augmentation, and leveraging transfer learning for efficiency. By the end of the course, you’ll be confident enough to tackle your own projects using modern frameworks like TensorFlow 2 with Keras.
Learning Objectives:
- Train deep learning models from scratch.
- Utilize common data types and model architectures.
- Apply data augmentation for better accuracy.
- Implement transfer learning for efficient results.
Practical applications include real-world projects in sectors like healthcare, retail, and automotive.
Purchase This Course
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
To ensure you can successfully undertake the "Getting Started with Deep Learning (NVIDIA)" course, we recommend the following minimum knowledge and skills:
These prerequisites will help you maximize the learning experience and better understand the hands-on exercises and tools used throughout the course. If you possess these foundational skills, you're well-prepared to dive into the world of deep learning with confidence.
Introduction: Getting Started with Deep Learning (NVIDIA) is ideal for professionals with basic Python knowledge seeking hands-on experience in deep learning techniques and applications using modern frameworks.
Target Audience and Job Roles:
This course provides a hands-on introduction to deep learning, covering fundamental techniques and tools, data augmentation, transfer learning, and applications in computer vision and natural language processing.
