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
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Equips learners for roles such as Machine Learning Engineer, Deep Learning Specialist, and Computer Vision Engineer.
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Applicable in industries like healthcare, automotive, robotics, and e-commerce.
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Enhances employability by teaching practical skills in building and deploying CNNs using TensorFlow.
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Supports career advancement in AI and machine learning domains.
Explore More Learning Paths
Expand your expertise in deep learning and neural networks with these curated courses designed to strengthen your understanding of CNNs, TensorFlow, and modern AI frameworks.
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Introduction to Deep Learning: Neural Networks with Keras Course – Learn the fundamentals of deep learning and neural networks using Keras to build and train models efficiently.
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Introduction to Neural Networks and PyTorch Course – Gain hands-on experience building neural networks using PyTorch and understand key concepts in AI model development.
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Last verified: March 12, 2026
