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We're here to help you find itThe course begins with an overview of PyTorch, including model classes, datasets, data loaders and the training loop. Next, it covers the role and power of transfer learning, along with how to use it with pretrained models. Practical lab exercises cover multiple topics including: image classification, object detection, sentiment analysis, text classification, and text generation/completion. Learners also will use their data to fine-tune existing models and leverage third-party APIs.
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Free Pre-requisite TrainingJoin a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.
Assessments (Qubits)Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.
Post Training ReportsReceive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.
Class RecordingsGet access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.
Free Lab ExtensionsExtend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.
Free Revision Classes Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
Certainly! Here are the minimum required prerequisites for successfully undertaking the PyTorch Essentials: An Applications-First Approach (LFD273) course:
With these prerequisites, learners will be well-prepared to dive into the practical applications of PyTorch in this course!
PyTorch Essentials: An Applications-First Approach (LFD273) equips learners with practical skills in PyTorch for developing AI applications, targeting individuals eager to enhance their machine learning expertise.
Introduction:
The PyTorch Essentials: An Applications-First Approach (LFD273) course provides a hands-on introduction to PyTorch, focusing on practical applications in deep learning and helping students master essential concepts for developing AI models.
Learning Objectives and Outcomes:
