Low-Code and Deep Learning Applications

Artificial Intelligence engineering aims to provide a framework of tools to proactively design AI systems to function in environments characterized by computational complexity and ambiguity and uses restricted Boltzmann machine in DL network. The novelty of this book chapter is in providing a comprehensive knowledge in tool sets for deep learning applications, specifically building the virtual environment for various MI platforms. Networks include NN, CNN, DNN, GAN, VAE, LSTM, etc. The book introduces the role of tool sets in AI applications and also discusses custom framework for real-time application deployment. More importantly, sample AI application deployment includes quick-look IBM WATSON, IBM Watson service, and monitor tomato farm and real-time audit of IP networks. Agriworks work flow complexity for diagnosis is used using a smartphone application with a few clicks.