Advancements in Artificial Neural Networks and Tensorflow's Role in Democratizing ML
Wajiha Abdul Shakir · 2024
This paper provides a comprehensive overview of artificial neural networks (ANNs), exploring their theoretical foundations, practical applications, and recent advancements. I delve into the basic constructs of ANNs, discuss vital algorithms, and examine deep neural networks (DNNs) with real-world applications. Highlighting the strengths and weaknesses of various neural network variants, I emphasize the significant impact of Google's TensorFlow in simplifying machine learning processes. Released in 2015, TensorFlow's architecture and programming model has revolutionized the field by enabling scalable, distributed, and efficient machine learning development. Furthermore, I explore the potential future of machine learning, including cutting-edge research in Artificial General Intelligence (AGI) and the pursuit of universal learning algorithms.