What will you learn in this Convolutional Neural Networks in TensorFlow Course
- Build convolutional neural networks (CNNs) using TensorFlow and Keras.
- Handle real-world image data and perform image classification.
- Implement strategies to prevent overfitting, including data augmentation and dropout.
- Apply transfer learning to leverage pre-trained models for new tasks.
- Visualize the journey of an image through convolutions to understand how a computer “sees” information.
Program Overview
1. Exploring a Larger Dataset
⏳ 2 hours
Work with the Cats vs. Dogs dataset, a real-world dataset with images of varying sizes and aspect ratios, to build a CNN that can classify images.
2. Augmentation
⏳ 4 hours
Learn how to implement data augmentation techniques to improve model generalization and prevent overfitting.
3. Dropout
⏳ 4 hours
Understand and apply dropout regularization to reduce overfitting in neural networks.
4. Transfer Learning
⏳ 6 hours
Explore transfer learning by leveraging pre-trained models to improve performance on new tasks with limited data.
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Job Outlook
Equips learners for roles such as Machine Learning Engineer, Deep Learning Specialist, and Computer Vision Engineer.
Applicable in industries like healthcare, automotive, robotics, and e-commerce.
Enhances employability by teaching practical skills in building and deploying CNNs using TensorFlow.
Supports career advancement in AI and machine learning domains.
Specification: Convolutional Neural Networks in TensorFlow
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