What technologies and tools are covered?

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

Do I need prior experience in deep learning?

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

Will I build real projects in this course?

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

How does this course help with computer vision?

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

How is model performance evaluated in this course?

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

What roles or career paths does this course prepare me for?

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

What can I do after completing this course?

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.

What will I get if I subscribe to this Specialization?

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.

Is financial aid available?

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.

Neural Networks and Computer Vision Foundations

This course is part of Advanced Deep Learning Architectures Specialization

Instructor: Edureka

Included with

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4 modules

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

4 modules

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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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

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
Knowledge Check : Neural Network Core Foundations 30 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

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
Knowledge Check: Regularization and Normalization Strategies 30 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

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
Knowledge Check: Foundations of Computer Vision and CNNs 30 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

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka

166 Courses 150,377 learners

Offered by

Edureka

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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.

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.