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March 3, 2025
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Master Deep Learning with PyTorch. Master deep learning using PyTorch with the help of this comprehensive and practical course. Develop, deploy, and innovate with models in Regression, CNNs, GANs, NLP, Recommender Systems, Transformers, and more.
Instructor: Packt - Course Instructors
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(26 reviews)
Recommended experience
Intermediate level
Ideal for Python developers, data enthusiasts, and aspiring data scientists. Gain skills in deep learning with PyTorch. Basic Python skills required.
(26 reviews)
Recommended experience
Intermediate level
Ideal for Python developers, data enthusiasts, and aspiring data scientists. Gain skills in deep learning with PyTorch. Basic Python skills required.
Identify and describe the fundamental concepts of PyTorch, machine learning, and deep learning, including neural networks.
Implement PyTorch models for tasks such as classification and regression, incorporating principles of deep learning.
Evaluate and optimize model performance using metrics and techniques for underfitting, overfitting, and hyperparameter tuning.
Develop advanced AI applications using GANs, CNNs, and RNNs, and deploy models effectively.
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Embark on a transformative learning experience with our PyTorch Ultimate 2024 course. Begin with a solid foundation, understanding the key topics and objectives, and seamlessly transition through machine learning essentials and deep learning principles. From setting up your environment to mastering tensors and neural networks, each section is meticulously crafted to build your expertise. Advanced modules dive into PyTorch modeling, CNNs, RNNs, GANs, and more, ensuring you stay ahead in the rapidly evolving field of AI. With practical coding exercises and real-world applications, this course is your gateway to becoming a PyTorch expert.
Our course is designed for tech professionals, data scientists, and AI enthusiasts eager to deepen their understanding of PyTorch. Prior experience in Python and basic machine learning concepts is recommended. By the end of the course, you'll be equipped with the skills to tackle complex AI projects and leverage PyTorch for innovative solutions.
Applied Learning Project
The included projects offer practical, hands-on experience where learners apply their skills to real-world problems. These projects range from constructing neural networks from scratch to developing complex models for image, audio, and object detection tasks. By engaging with these projects, learners will solve authentic problems, enhancing their ability to deploy robust deep-learning solutions in various practical scenarios.
Set up and configure a PyTorch environment.
Understand fundamental AI and machine learning concepts.
Build, train, and evaluate neural networks from scratch, utilizing various optimization techniques
Apply PyTorch to real-world deep learning tasks.
Build and train neural networks using PyTorch for various tasks.
Implement classification models with multi-class, multi-label datasets, and CNNs for image and audio classification.
Apply object detection techniques using the YOLO algorithm.
Explore neural style transfer, transfer learning, and implement RNNs and LSTM networks.
Create and assess ML models for specific datasets, evaluating performance with proper metrics.
Design autoencoders for dimensionality reduction and build GANs for data simulation, analyzing quality.
Develop Graph Neural Networks for graph data and implement Transformers, including Vision Transformers.
Enhance models with semi-supervised learning using limited data, and deploy them with Flask on Google Cloud.
Packt helps tech professionals put software to work by distilling and sharing the working knowledge of their peers. Packt is an established global technical learning content provider, founded in Birmingham, UK, with over twenty years of experience delivering premium, rich content from groundbreaking authors on a wide range of emerging and popular technologies.
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This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
Financial aid available,