Last updated 3/2024
Duration: 11h 42m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 6.79 GB
Genre: eLearning | Language: English

Master Computer Vision in PyTorch/Python: Beginner to Pro with Expert Tips on Convolutional Neural Networks (CNNs)

What you'll learn
Master how to Perform Computer Vision Task with Deep Learning
Learn to Work with PyTorch
Convolutional Neural Networks with Torch Library
Build Intuition on Convolution Operation on Images
Learn to Implement LeNet Architecture on CIFAR10 dataset which has 60000 images

Requirements
Basic Machine learning with Python Programming Language

Description
Dive into Computer Vision with PyTorch: Master Deep Learning, CNNs, and GPU Computing for Real-World Applications - 2024 Edition"
Unlock the potential of Deep Learning in Computer Vision, where groundbreaking advancements shape the future of technology. Explore applications ranging from Facebook's image tagging and Google Photo's People Recognition to fraud detection and facial recognition. Delve into the core operations of Deep Learning Computer Vision, including convolution operations on images, as you master the art of extracting valuable information from digital images.
In this comprehensive course, we focus on one of the most widely used Deep Learning frameworks – PyTorch. Recognized as the go-to tool for Deep Learning in both product prototypes and academia, PyTorch stands out for its Pythonic nature, ease of learning, higher developer productivity, dynamic approach for graph computation through AutoGrad, and GPU support for efficient computation.
Why PyTorch?
Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.
Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.
Higher Developer Productivity: PyTorch's design prioritizes developer productivity, promoting efficiency in building and experimenting with models.
Dynamic Approach for Graph Computation - AutoGrad: PyTorch's dynamic computational graph through AutoGrad enables flexible and efficient model development.
GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.
Course Highlights
Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.
Learn GPU programming and explore how to access free GPU resources for efficient learning.
Master the AutoGrad feature of PyTorch, a key aspect for dynamic graph computation.
Implement Deep Learning models using PyTorch, transitioning from theory to practical application.
Explore the basics of Convolutional Neural Networks (CNNs) in PyTorch, a fundamental architecture for computer vision tasks.
Apply CNNs to real-world datasets, developing hands-on experience with practical applications.
Our Approach
We believe that true learning extends beyond theoretical understanding; it involves building confidence through practical application. Throughout the course, we've incorporated assignments at the end of each section, enabling you to measure your progress and reinforce your learning. We aspire to empower you with the skills and confidence needed to navigate the dynamic field of Deep Learning in Computer Vision.
Embark on this journey with Manifold AI Learning, where innovation meets education. We look forward to welcoming you inside the course and witnessing your success. Best of luck!
Manifold AI Learning

Who this course is for
Software Developer
Machine Learning Practitioner
Data Scientist
Anyone interested to learn PyTorch
Anyone interested in Deep learning

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