This hands-on specialization dives in quickly, so you can start training models and gain practical deep learning skills. You don’t need to be an expert programmer or have prior deep learning experience to quickly gain valuable career skills for this rapidly growing area.
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This hands-on specialization dives in quickly, so you can start training models and gain practical deep learning skills. You don’t need to be an expert programmer or have prior deep learning experience to quickly gain valuable career skills for this rapidly growing area.
This hands-on specialization dives in quickly, so you can start training models and gain practical deep learning skills. You don’t need to be an expert programmer or have prior deep learning experience to quickly gain valuable career skills for this rapidly growing area.
Deep learning empowers engineers and scientists to tackle complex problems in computer vision that were previously challenging to solve, such as building autonomous systems like self-driving cars. As companies increasingly adopt computer vision technologies, professionals with deep learning skills are in high demand. Acquiring these skills will give you a competitive advantage in a rapidly changing technological landscape.
By the end of this specialization, you will be able to:
Train image classification and object detection models Train specialized models to detect anomalies Evaluate model performance using more than just prediction accuracy Interpret model behavior by investigating prediction errors Improve model performance by tuning important parameters Use AI-assisted labeling to automatically label thousands of images Generate synthetic images to for training using data-augmentation
For the duration of the specialization, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.
What's inside
Three courses
Introduction to Deep Learning for Computer Vision
Introduction to Deep Learning for Computer Vision
Deep Learning for Object Detection
Deep Learning for Object Detection
Advanced Deep Learning Techniques for Computer Vision
Advanced Deep Learning Techniques for Computer Vision
Learning objectives
- Apply the full deep learning workflow to real-world projects like detecting parking signs
- Retrain common classification and detection models like resnet and yolo
- Train and calibrate specialized models known as anomaly detectors
- Generate synthetic training images and use ai-assisted auto-labeling to save time and money
- Apply the full deep learning workflow to real-world projects like detecting parking signs
- Retrain common classification and detection models like resnet and yolo
- Train and calibrate specialized models known as anomaly detectors
- Generate synthetic training images and use ai-assisted auto-labeling to save time and money
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