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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Ce cours n'est pas disponible en Français (France)

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Neural Networks and Computer Vision Foundations

Ce cours fait partie de Spécialisation " Advanced Deep Learning Architectures"

Instructeur : Edureka

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

1 semaine à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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

Compétences que vous acquerrez

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

Outils que vous découvrirez

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

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

mars 2026

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Ce cours fait partie de la Spécialisation " Advanced Deep Learning Architectures"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

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Acquérez une compréhension de base d'un sujet ou d'un outil
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Il y a 4 modules dans ce cours

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.

Inclus

15 vidéos6 lectures4 devoirs

15 vidéosTotal 73 minutes

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

6 lecturesTotal 110 minutes

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

4 devoirsTotal 48 minutes

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

Inclus

14 vidéos4 lectures4 devoirs

14 vidéosTotal 76 minutes

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

4 lecturesTotal 70 minutes

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

4 devoirsTotal 48 minutes

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

Inclus

12 vidéos4 lectures4 devoirs

12 vidéosTotal 59 minutes

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

4 lecturesTotal 65 minutes

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

4 devoirsTotal 48 minutes

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

Inclus

1 vidéo1 lecture1 devoir

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Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Edureka

178 Cours162 805 apprenants

Offert par

Edureka

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Foire Aux 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.