Traffic Sign Classification Using Deep Learning in Python&Keras
This course is perfect for anyone interested in learning how to use Deep Learning to classify traffic signs. With the help of Python and Keras, you will be able to build a Convolutional Neural Network (CNN) and use it to classify traffic signs with high accuracy. You will also learn how to improve the performance of your model using regularization techniques. So, join this course and start your journey to becoming a Deep Learning expert! ▼
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Course Feature
Cost:
Paid
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
31st Jul, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [August 31st, 2023]
Skills and Knowledge:
- Understand the fundamentals of deep learning and neural networks.
- Learn how to use Python and Keras to build a Convolutional Neural Network (CNN).
- Understand the theory and intuition behind CNNs and how they are used for image classification.
- Learn how to pre-process images for CNNs.
- Learn how to compile and fit a CNN model to training data.
- Learn how to assess the performance of a trained CNN and ensure its generalization.
- Learn how to improve network performance using regularization techniques such as dropout.
Professional Growth:
This course contributes to professional growth by providing the following benefits:
1. Understanding of Convolutional Neural Networks (CNNs): CNNs are widely used in image recognition and classification tasks. By understanding the theory and intuition behind CNNs, professionals can apply this knowledge to various computer vision projects and tasks.
2. Familiarity with key libraries and dataset: The course teaches how to import key libraries and datasets, which are essential skills for working with deep learning frameworks. This knowledge can be applied to other projects and datasets, enabling professionals to work efficiently and effectively.
3. Image preprocessing and manipulation: The course covers image normalization and conversion from color-scaled to gray-scaled images. These techniques are fundamental in image processing and can be applied to a wide range of computer vision tasks.
4. Building a CNN using Keras with Tensorflow 2.0: Keras is a popular deep learning framework, and Tensorflow is one of the most widely used backends. By learning how to build a CNN using Keras and Tensorflow 2.0, professionals can leverage these tools to develop and deploy deep learning models in their own projects.
5. Model evaluation and performance assessment: The course teaches how to assess the performance of a trained CNN using various key performance indicators (KPIs). This skill is crucial for evaluating the effectiveness of deep learning models and making informed decisions about model improvements.
6. Regularization techniques: The course covers regularization techniques such as dropout, which can improve the performance and generalization of deep learning models. Professionals can apply these techniques to their own models to enhance their performance and reduce overfitting.
Overall, this course equips professionals with essential skills and knowledge in deep learning, image processing, and model evaluation. These skills can contribute to their professional growth by enabling them to work on computer vision projects, develop robust deep learning models, and make informed decisions about model performance and improvements.
Further Education:
This course is suitable for preparing for further education. It covers the theory and intuition behind Convolutional Neural Networks (CNNs), which is a fundamental concept in deep learning. It also teaches you how to import libraries, datasets, and visualize images, which are important skills for working with deep learning models. Additionally, the course covers topics such as image normalization, converting images to grayscale, building a CNN using Keras with Tensorflow 2.0, and assessing the performance of the trained model. These skills and knowledge will be valuable for further education in the field of deep learning and computer vision.
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