Neural Networks and Computer Vision Foundations
About this course
This course guides you through the foundational principles behind neural networks and computer vision systems, focusing on how forward propagation, backpropagation, optimization, and convolutional architectures enable modern AI applications. Through hands-on demonstrations and practical exercises, you’ll learn to build neural networks from scratch, train them effectively, and apply these models to real-world vision tasks such as image classification, detection, and similarity learning. By the end of this course, you will be able to: - Explain how neural networks learn using forward passes, loss functions, and backpropagation - Implement neural network training pipelines and analyze model convergence - Apply optimization, regularization, and normalization techniques to improve performance - Understand convolutional neural networks and how they extract visual features - Build and evaluate end-to-end image classification and computer vision systems This course is ideal for aspiring AI practitioners, data scientists, software engineers, and ML engineers looking to develop a strong foundation in neural networks and vision-based learning. A working knowledge of Python and basic machine learning concepts is recommended. Join us to build a solid foundation in neural networks and computer vision, the core technologies powering today’s intelligent AI systems.
68/100
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- 32/45
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- 20/35
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- 16/20
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What you'll learn
- Explain forward propagation, loss functions, and backpropagation
- Implement neural network training pipelines and analyze model convergence
- Apply optimization, regularization, and normalization techniques
- Understand the role of convolutional networks in feature extraction
- Build and evaluate end-to-end image classification systems
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