Convolutional Neural Networks with TensorFlow Online Course

Convolutional Neural Networks with TensorFlow Online Course

TensorFlow, Google’s open-source deep learning library, is one of the most widely used tools in artificial intelligence and machine learning today. Mastering it is essential for anyone pursuing deep learning. In this course, you will learn to use TensorFlow 2 to build and train convolutional neural networks (CNNs). You’ll begin with a detailed exploration of convolution—what it is, why it matters, and how to integrate it into neural networks. From there, you’ll apply CNNs to a range of image recognition datasets, progressing from simple to complex challenges. You will also learn how to perform text preprocessing and classification with CNNs. Finally, the course covers advanced techniques such as batch normalization, data augmentation, and transfer learning to boost performance in computer vision tasks.

By the end, you will have the skills to confidently build and optimize CNNs with TensorFlow for real-world deep learning applications.

Who should take this Course?

The Convolutional Neural Networks with TensorFlow Online Course is ideal for data scientists, AI/ML enthusiasts, and software developers who want to specialize in deep learning for image recognition and computer vision. It is also well-suited for students, researchers, and professionals in fields such as robotics, healthcare, and autonomous systems who seek practical skills in building, training, and deploying CNN models using TensorFlow.

What you will learn

• Understand the concept of convolution
• Integrate convolution into neural networks
• Apply CNNs to several image recognition datasets, both small and large
• Learn best practices for designing CNN architectures
• Learn about batch normalization and data augmentation
• Learn how to preform text preprocessing

Course Outline 

Welcome

• Introduction
• Outline

Convolutional Neural Networks (CNNs)

• What Is Convolution? (Part 1)
• What Is Convolution? (Part 2)
• What Is Convolution? (Part 3)
• Convolution on Color Images
• CNN Architecture
• CNN Code Preparation
• CNN for Fashion MNIST
• CNN for CIFAR-10
• Data Augmentation
• Batch Normalization
• Improving CIFAR-10 Results
• Suggestion Box

Natural Language Processing (NLP)

• Embeddings
• Code Preparation (NLP)
• Text Preprocessing
• CNNs for Text
• Text Classification with CNNs

Transfer Learning for Computer Vision

• Transfer Learning Theory
• Some Pre-Trained Models (VGG, ResNet, Inception, MobileNet)
• Large Datasets and Data Generators
• 2 Approaches to Transfer Learning
• Transfer Learning Code (Part 1)
• Transfer Learning Code (Part 2)