| Summary: | Learn how to perform data science and Computer Vision with TensorFlow 2. About This Video: 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. In Detail: 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 GitHub.
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