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

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Neural Networks and Computer Vision Foundations

Dieser Kurs ist Teil von Spezialisierung „ Advanced Deep Learning Architectures“

Dozent: Edureka

Bei enthalten

Mehr erfahren

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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

Kompetenzen, die Sie erwerben

Kategorie: Model Optimization
Kategorie: Convolutional Neural Networks
Kategorie: Machine Learning Methods
Kategorie: Data Science
Kategorie: Data Visualization
Kategorie: Model Evaluation
Kategorie: Transfer Learning
Kategorie: Model Training
Kategorie: Artificial Intelligence
Kategorie: Recurrent Neural Networks (RNNs)
Kategorie: Image Analysis
Kategorie: Artificial Intelligence and Machine Learning (AI/ML)
Kategorie: Deep Learning
Kategorie: Artificial Neural Networks
Kategorie: Matplotlib
Kategorie: Machine Learning
Kategorie: Computer Vision

Werkzeuge, die Sie lernen werden

Kategorie: NumPy
Kategorie: Python Programming
Kategorie: PyTorch (Machine Learning Library)

Wichtige Details

Zertifikat zur Vorlage

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Kürzlich aktualisiert!

März 2026

Bewertungen

13 Aufgaben

Unterrichtet in Englisch

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „ Advanced Deep Learning Architectures“

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Erwerben Sie ein Berufszertifikat zur Vorlage

In diesem Kurs gibt es 4 Module

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.

Das ist alles enthalten

15 Videos6 Lektüren4 Aufgaben

15 VideosInsgesamt 73 Minuten

Specialization Introduction4 Minuten
Course Introduction3 Minuten
Introduction to Deep Learning3 Minuten
How Neural Networks Learn3 Minuten
Perceptrons and Multi Layer Networks4 Minuten
Demonstration: Forward Pass Implementation from Scratch7 Minuten
Demonstration: Loss Computation and Prediction Flow5 Minuten
Backpropagation Intuition and Mathematics4 Minuten
Chain Rule and Gradient Computation4 Minuten
Demonstration: Manual Backpropagation Implementation7 Minuten
Demonstration: Visualizing Gradient Flow7 Minuten
Neural Network Training Pipelines3 Minuten
Demonstration: Training Loop Implementation7 Minuten
Demonstration: Loss Curves and Convergence Analysis : Training Pipeline6 Minuten
Demonstration: Loss Curves and Convergence Analysis : Visualization 5 Minuten

6 LektürenInsgesamt 110 Minuten

Welcome to Neural Network and Vision System Foundations10 Minuten
Neural Network Fundamentals Explained20 Minuten
Backpropagation Step by Step20 Minuten
PyTorch Explained: Libraries, Workflows and Advanced Features30 Minuten
Training Neural Networks Correctly20 Minuten
Module Summary: Neural Network Core Foundations10 Minuten

4 AufgabenInsgesamt 48 Minuten

Knowledge Check : Neural Network Core Foundations30 Minuten
Practice Knowledge Check: Neural Network Fundamentals6 Minuten
Practice Knowledge Check: Backpropagation and Gradient Flow6 Minuten
Practice Knowledge Check: Training Loops and Model Convergence6 Minuten

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.

Das ist alles enthalten

14 Videos4 Lektüren4 Aufgaben

14 VideosInsgesamt 76 Minuten

SGD and Momentum Optimization4 Minuten
Learning Rate Scheduling Strategies5 Minuten
Demonstration: SGD vs. Momentum Comparison7 Minuten
Demonstration: Learning Rate Sensitivity Analysis7 Minuten
RMSProp and Adam Optimizers4 Minuten
Demonstration: Optimizer Performance Comparison7 Minuten
Demonstration: Convergence Speed Analysis: Data Preparation5 Minuten
Demonstration: Convergence Speed Analysis: Model Training7 Minuten
Demonstration: Convergence Speed Analysis: Visualization3 Minuten
Dropout and Weight Decay Techniques3 Minuten
BatchNorm and LayerNorm Explained3 Minuten
Demonstration: Regularization Effect Analysis7 Minuten
Demonstration: Normalization Training Stability: Model and Training Setup7 Minuten
Demonstration: Normalization Training Stability: Visualization5 Minuten

4 LektürenInsgesamt 70 Minuten

Gradient Descent Optimization20 Minuten
Adaptive Optimization Algorithms20 Minuten
Regularization and Normalization Methods20 Minuten
Module Summary: Regularization and Normalization Strategies10 Minuten

4 AufgabenInsgesamt 48 Minuten

Knowledge Check: Regularization and Normalization Strategies30 Minuten
Practice Knowledge Check: Gradient Descent Optimization Methods6 Minuten
Practice Knowledge Check: Adaptive Optimizers Explained6 Minuten
Practice Knowledge Check: Regularization and Normalization Strategies6 Minuten

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.

Das ist alles enthalten

12 Videos4 Lektüren4 Aufgaben

12 VideosInsgesamt 59 Minuten

Computer Vision as Multidimensional Learning3 Minuten
Convolutional Neural Networks Architecture4 Minuten
Demonstration: Images as Multidimensional Tensors6 Minuten
Demonstration: Feature Map Visualization7 Minuten
Object Detection and Segmentation Architectures4 Minuten
Demonstration: Bounding Boxes vs Segmentation Masks7 Minuten
Demonstration: Detection and Segmentation Outputs: Model Steup 4 Minuten
Demonstration: Detection and Segmentation Outputs: Output Analysis5 Minuten
Similarity Learning with Visual Embeddings4 Minuten
Demonstration: Distance Metrics Comparison: Embedding Setup6 Minuten
Demonstration: Distance Metrics Comparison: Similarity Ranking4 Minuten
Demonstration: Image Similarity Using Embedding Distance7 Minuten

4 LektürenInsgesamt 65 Minuten

Computer Vision Fundamentals20 Minuten
Object Detection and Segmentation20 Minuten
Similarity Learning for Images15 Minuten
Module Summary: Foundations of Computer Vision and CNNs10 Minuten

4 AufgabenInsgesamt 48 Minuten

Knowledge Check: Foundations of Computer Vision and CNNs30 Minuten
Practice Knowledge Check: Computer Vision and CNN Fundamentals6 Minuten
Practice Knowledge Check: Object Detection and Image Segmentation6 Minuten
Practice Knowledge Check: Similarity Learning for Vision6 Minuten

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.

Das ist alles enthalten

1 Video1 Lektüre1 Aufgabe

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Dozent

Edureka

185 Kurse172.403 Lernende

von

Edureka

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Häufig gestellte Fragen

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.