Convolutional Neural Networks with TensorFlow Online Course
Convolutional Neural Networks with TensorFlow Online Course
This course teaches TensorFlow 2 for building convolutional neural networks (CNNs). You'll explore the basics of convolution, its role in neural networks, and apply CNNs to image recognition datasets of varying complexity. The course also covers text preprocessing and text classification using CNNs. Lastly, you'll learn performance-enhancing techniques like batch normalization, data augmentation, and transfer learning for Computer Vision. By the end, you'll be equipped to build and optimize CNNs in deep learning with TensorFlow.
Who is this Course for?
This course is ideal for those interested in deep learning and machine learning, or anyone looking to implement convolutional neural networks (CNNs) using TensorFlow 2. A good understanding of Python programming is required, along with experience in building feedforward artificial neural networks (ANNs) in TensorFlow 2 and familiarity with data science libraries like NumPy and Matplotlib.
What you will learn
- Concept of convolution
- Integrate convolution in neural networks
- Apply CNNs to image datasets
- Learn best practices for CNN architecture
- Explore batch normalization & data augmentation
- Perform text preprocessing
Course Table of Contents
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)
