Computer Vision and Natural Language Processing

The chapter provides a brief exploration of deep learning applications in computer vision and natural language processing (NLP). It begins with an introduction to deep learning, its key elements, and the significance of neural networks, activation functions, and hyperparameters. The chapter then delves into the realm of computer vision, discussing the evolution and contributions of prominent convolutional neural network (CNN) models like LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet. The practical implementation of CNN using KNIME, a data analytics platform, is also covered. Furthermore, the chapter addresses NLP, discussing the challenges of digitizing natural language and various approaches like one-hot encoding, bag-of-words, neural network language models, and feature extraction techniques like LSTM and Transformer. The chapter concludes by introducing large language models (LLMs) such as ChatGPT, Google’s Bard, and Baidu’s Wenxin Yiyan and their implementation in KNIME through AI assistant and AI extension tools. Practical exercises are provided to reinforce understanding and application of these concepts.