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⇱ Neural Networks and Computer Vision Foundations | Coursera

Neural Networks and Computer Vision Foundations

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Neural Networks and Computer Vision Foundations

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • How neural networks work, including forward propagation, loss computation, and backpropagation

  • How to train, optimize, and regularize neural networks for stable convergence

  • How convolutional neural networks process images and learn visual features

  • How to build and evaluate end-to-end image classification and vision systems

Details to know

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Recently updated!

March 2026

Assessments

13 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Advanced Deep Learning Architectures Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in 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.

This module introduces neural networks from first principles, explaining how models compute predictions, measure error, and learn through backpropagation. Learners implement forward passes, training loops, and gradient flow to build a strong foundation in how neural networks learn.

What's included

15 videos6 readings4 assignments

15 videos•Total 73 minutes
  • Specialization Introduction•4 minutes
  • Course Introduction•3 minutes
  • Introduction to Deep Learning•3 minutes
  • How Neural Networks Learn•3 minutes
  • Perceptrons and Multi Layer Networks•4 minutes
  • Demonstration: Forward Pass Implementation from Scratch•7 minutes
  • Demonstration: Loss Computation and Prediction Flow•5 minutes
  • Backpropagation Intuition and Mathematics•4 minutes
  • Chain Rule and Gradient Computation•4 minutes
  • Demonstration: Manual Backpropagation Implementation•7 minutes
  • Demonstration: Visualizing Gradient Flow•7 minutes
  • Neural Network Training Pipelines•3 minutes
  • Demonstration: Training Loop Implementation•7 minutes
  • Demonstration: Loss Curves and Convergence Analysis : Training Pipeline•6 minutes
  • Demonstration: Loss Curves and Convergence Analysis : Visualization •5 minutes
6 readings•Total 110 minutes
  • Welcome to Neural Network and Vision System Foundations•10 minutes
  • Neural Network Fundamentals Explained•20 minutes
  • Backpropagation Step by Step•20 minutes
  • PyTorch Explained: Libraries, Workflows and Advanced Features•30 minutes
  • Training Neural Networks Correctly•20 minutes
  • Module Summary: Neural Network Core Foundations•10 minutes
4 assignments•Total 48 minutes
  • Knowledge Check : Neural Network Core Foundations•30 minutes
  • Practice Knowledge Check: Neural Network Fundamentals•6 minutes
  • Practice Knowledge Check: Backpropagation and Gradient Flow•6 minutes
  • Practice Knowledge Check: Training Loops and Model Convergence•6 minutes

This module focuses on training neural networks efficiently and reliably using gradient descent, adaptive optimizers, and learning rate strategies. Learners apply regularization and normalization techniques to stabilize training and improve generalization.

What's included

14 videos4 readings4 assignments

14 videos•Total 76 minutes
  • SGD and Momentum Optimization•4 minutes
  • Learning Rate Scheduling Strategies•5 minutes
  • Demonstration: SGD vs. Momentum Comparison•7 minutes
  • Demonstration: Learning Rate Sensitivity Analysis•7 minutes
  • RMSProp and Adam Optimizers•4 minutes
  • Demonstration: Optimizer Performance Comparison•7 minutes
  • Demonstration: Convergence Speed Analysis: Data Preparation•5 minutes
  • Demonstration: Convergence Speed Analysis: Model Training•7 minutes
  • Demonstration: Convergence Speed Analysis: Visualization•3 minutes
  • Dropout and Weight Decay Techniques•3 minutes
  • BatchNorm and LayerNorm Explained•3 minutes
  • Demonstration: Regularization Effect Analysis•7 minutes
  • Demonstration: Normalization Training Stability: Model and Training Setup•7 minutes
  • Demonstration: Normalization Training Stability: Visualization•5 minutes
4 readings•Total 70 minutes
  • Gradient Descent Optimization•20 minutes
  • Adaptive Optimization Algorithms•20 minutes
  • Regularization and Normalization Methods•20 minutes
  • Module Summary: Regularization and Normalization Strategies•10 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Regularization and Normalization Strategies•30 minutes
  • Practice Knowledge Check: Gradient Descent Optimization Methods•6 minutes
  • Practice Knowledge Check: Adaptive Optimizers Explained•6 minutes
  • Practice Knowledge Check: Regularization and Normalization Strategies•6 minutes

This module applies deep learning fundamentals to visual data, introducing convolutional neural networks and image representation. Learners build systems for classification, detection, segmentation, and similarity learning.

What's included

12 videos4 readings4 assignments

12 videos•Total 59 minutes
  • Computer Vision as Multidimensional Learning•3 minutes
  • Convolutional Neural Networks Architecture•4 minutes
  • Demonstration: Images as Multidimensional Tensors•6 minutes
  • Demonstration: Feature Map Visualization•7 minutes
  • Object Detection and Segmentation Architectures•4 minutes
  • Demonstration: Bounding Boxes vs Segmentation Masks•7 minutes
  • Demonstration: Detection and Segmentation Outputs: Model Steup •4 minutes
  • Demonstration: Detection and Segmentation Outputs: Output Analysis•5 minutes
  • Similarity Learning with Visual Embeddings•4 minutes
  • Demonstration: Distance Metrics Comparison: Embedding Setup•6 minutes
  • Demonstration: Distance Metrics Comparison: Similarity Ranking•4 minutes
  • Demonstration: Image Similarity Using Embedding Distance•7 minutes
4 readings•Total 65 minutes
  • Computer Vision Fundamentals•20 minutes
  • Object Detection and Segmentation•20 minutes
  • Similarity Learning for Images•15 minutes
  • Module Summary: Foundations of Computer Vision and CNNs•10 minutes
4 assignments•Total 48 minutes
  • Knowledge Check: Foundations of Computer Vision and CNNs•30 minutes
  • Practice Knowledge Check: Computer Vision and CNN Fundamentals•6 minutes
  • Practice Knowledge Check: Object Detection and Image Segmentation•6 minutes
  • Practice Knowledge Check: Similarity Learning for Vision•6 minutes

This module consolidates learning through a hands-on vision project and final assessment. Learners demonstrate their ability to design, train, and evaluate complete deep learning systems.

What's included

1 video1 reading1 assignment

1 video•Total 2 minutes
  • Course Summary•2 minutes
1 reading•Total 60 minutes
  • Practice Project: End-to-End Neural Network and Vision System•60 minutes
1 assignment•Total 30 minutes
  • End Knowledge Check: Neural Network and Vision System Foundations•30 minutes

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Instructor

Edureka
203 Courses•185,724 learners

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Frequently asked questions

This course builds a strong foundation in neural networks and computer vision, helping you understand how modern AI systems are designed, trained, and evaluated from scratch.

You will learn how neural networks work, how they are trained using backpropagation, how to optimize models, and how to apply these concepts to computer vision tasks like image classification.

The course combines clear conceptual explanations with hands-on demonstrations and practical exercises, including building neural networks and vision systems end to end.

You will work with Python, PyTorch and supporting libraries for numerical computation and visualization.

No prior deep learning experience is required. A basic understanding of Python and introductory machine learning concepts is sufficient.

Yes. You will complete hands-on demonstrations and a final practice project focused on building a complete image classification system.

The course introduces convolutional neural networks, feature extraction, object detection, segmentation concepts, and similarity learning for vision-based applications.

You will learn to analyze loss curves, convergence behavior, and evaluation metrics to assess and improve model performance.

This course supports roles such as Machine Learning Engineer, AI Engineer, Computer Vision Engineer, and Data Scientist.

After completing this course, you can move on to advanced deep learning, specialized computer vision courses, or begin building real-world vision-based AI systems.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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.

Financial aid available,