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.
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
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.