TensorFlow is the world’s most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI (Artificial Intelligence) and machine learning. So, if you want to do deep learning, you must know TensorFlow.
In this course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNN). We will first start by having an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. Then you will learn how to apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. Next, you will learn how to perform text preprocessing and text classification with CNNs.
In the last section, you will learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning for Computer Vision.
By the end of this course, we will have understood how to build convolutional neural networks in deep learning with TensorFlow.
All the notebooks used in the course are available at https://github.com/PacktPublishing/Deep-Learning—Convolutional-Neural-Networks-with-TensorFlow.
This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement convolutional neural networks in TensorFlow 2.
One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.
In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks. The course is well-balanced with theory that explains the CNN concepts and hands-on coding exercises for practical understanding.
Key Features:
- Learn how to use TensorFlow 2 to build Convolutional Neural Networks (CNNs)
- The course covers Natural Language Processing (NLP) and transfer learning for Computer Vision
- Explains how to apply CNNs to NLP
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
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