What does the Unlock Image Recognition course cover?
Unlock Image Recognition is covered here in 7 modules: Introduction to Image Recognition and CNNs: History of image recognition, Fundamentals of CNNs: Fully connected layers: softmax, sigmoid, Image Preprocessing and Data Augmentation: Importance of data augmentation and 4 more. The outline lists 23 specific topics, opening with what is image recognition? and closing with peer feedback and review.
How do you approach Unlock Image Recognition step by step?
The work is sequenced in 7 stages. It starts with Introduction to Image Recognition and CNNs: History of image recognition, moves through fundamentals of CNNs: Fully connected layers: softmax, sigmoid and Image Preprocessing and Data Augmentation: Importance of data augmentation, and ends at Case Studies and Projects: Peer feedback and review.
What is in Module 1 of the Unlock Image Recognition course?
Module 1 is Introduction to Image Recognition and CNNs: History of image recognition. It works through what is image recognition?, history of image recognition, Introduction to CNNs and 1 more. It sets the vocabulary the remaining 6 modules build on.
How is the Unlock Image Recognition course delivered?
The Unlock Image Recognition course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the Unlock Image Recognition course cost?
The Unlock Image Recognition course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: Convolutional Neural Networks in OKAPI Methodology, Convolutional Neural Networks in Machine Learning Trap, Neural Networks Toolkit, Neural Networks in OKAPI Methodology.
More answers: what you get with every course, refund policy, all help answers.
Unlock Image Recognition: Convolutional Neural Networks Explained | Beginner's Machine Learning Guide
Course Overview
Welcome to our comprehensive course on image recognition and convolutional neural networks (CNNs) designed specifically for beginners in machine learning. In this interactive and engaging course, you'll gain hands-on experience with real-world applications and develop a deep understanding of CNNs and image recognition techniques.
Course Highlights
- Interactive and Engaging: Learn through hands-on projects, quizzes, and gamification.
- Comprehensive Curriculum: Covering the fundamentals of CNNs and image recognition.
- Personalized Learning: Get tailored feedback and guidance from expert instructors.
- Up-to-date Content: Stay current with the latest advancements in CNNs and image recognition.
- Practical Applications: Apply your knowledge to real-world projects and case studies.
- High-quality Content: Learn from expert instructors with years of experience in machine learning.
- Certification: Receive a certificate upon completion, showcasing your expertise.
- Flexible Learning: Access course materials anytime, anywhere, on any device.
- User-friendly Platform: Navigate our intuitive platform with ease.
- Mobile-accessible: Learn on-the-go with our mobile-friendly platform.
- Community-driven: Join a community of like-minded learners and experts.
- Actionable Insights: Gain practical knowledge that can be applied to real-world problems.
- Hands-on Projects: Work on projects that challenge you and help you grow.
- Bite-sized Lessons: Learn in manageable chunks, at your own pace.
- Lifetime Access: Enjoy continued access to course materials, even after completion.
- Gamification: Engage with our interactive platform and track your progress.
- Progress Tracking: Monitor your progress and stay motivated.
Course Curriculum
Module 1. Introduction to Image Recognition and CNNs: History of image recognition
- What is image recognition?
- History of image recognition
- Introduction to CNNs
- Key concepts: convolutional layers, pooling layers, fully connected layers
Module 2. Fundamentals of CNNs: Fully connected layers: softmax, sigmoid
- Convolutional layers: filters, stride, padding
- Pooling layers: max pooling, average pooling
- Fully connected layers: softmax, sigmoid
- Activation functions: ReLU, Sigmoid, Tanh
Module 3. Image Preprocessing and Data Augmentation: Importance of data augmentation
- Image preprocessing techniques: resizing, normalization
- Data augmentation techniques: rotation, flipping, cropping
- Importance of data augmentation
Module 4. Building and Training a CNN Model: Hyperparameter tuning: learning rate, regularization
- Building a CNN model: architecture, layers, activation functions
- Training a CNN model: loss functions, optimizers, batch size
- Hyperparameter tuning: learning rate, regularization
Module 5. Advanced CNN Techniques: Fine-tuning: adjusting pre-trained models
- Transfer learning: using pre-trained models
- Fine-tuning: adjusting pre-trained models
- Attention mechanisms: focus on important regions
Module 6. Real-world Applications of Image Recognition: Object detection: YOLO, SSD, Faster R-CNN
- Image classification: objects, scenes, actions
- Object detection: YOLO, SSD, Faster R-CNN
- Image segmentation: pixel-wise classification
Module 7. Case Studies and Projects: Peer feedback and review
- Real-world case studies: image recognition applications
- Hands-on projects: build and train your own CNN models
- Peer feedback and review