Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

4. Reinforcement Learning Reinforcement learning is a subset of machine learning that focuses on how software agents interact with dynamic environments to maximize cumulative rewards. It involves feedback and incentives to guide the agent's behavior. Markov decision processes (MDPs) are commonly used to represent the environment in reinforcement learning. Dynamic programming is employed in many reinforcement learning systems, and these algorithms can be applied when exact models of the environment are impractical. Reinforcement learning finds applications in various fields, including autonomous vehicles and training agents to play games against human opponents. 6. Deep Learning Deep learning 15 1. Virtual Assistants is a subset of machine learning that utilizes neural networks with multiple layers to mimic human brain functions and learn from vast amounts of data. It is a key component of data science and enables quicker and simpler analysis of large datasets. Deep learning is employed in various applications, such as driverless cars recognizing objects and distinguishing between them. The term "deep" refers to the additional layers added to the neural network for learning purposes. The weights in the model are updated through optimization functions during the learning process. Deep learning falls under the broader field of artificial intelligence (AI) and facilitates the development of AI-driven applications. Deep Learning Applications. 2. Chatbots 3. Healthcare 4. Entertainment 5. News Aggregation and Fake News Detection 6. Composing Music 7. Image Coloring 8. Robotics 9. Image Captioning 10. Advertising 11. Self-Driving Cars 12. Natural Language Processing 14 Ganguly Kuntal, “Learning generative adversarial networks: next- generation deep learning simplified”, Packt Publishing, 2017. 15 Josh Patterson, Adam Gibson "Deep Learning: A Practitioner's Approach", O'Reilly Media, 2017. 13. Visual Recognition 14. Fraud Detection 15. Personalization 16. Detecting Developmental Delay in Children 17. Colorization of Black and White images 18. Adding Sounds to Silent Movies 19. Automatic Machine Translation 20. Automatic Handwriting Generation 21. Automatic Game Playing 22. Language Translations 23. Pixel Restoration 24. Demographic and Election Predictions 25. Deep Dreaming Artificial intelligence (AI) enables machines to mimic human activity, while machine learning (ML) incorporates AI to facilitate continuous learning and improvement. Deep learning (DL) is a subset of ML that involves training models using sophisticated algorithms and deep neural networks. Convolutional neural networks (CNNs) are a specific type of deep learning architecture used for tasks like image recognition. DL focuses on transforming and extracting features to establish relationships between stimuli and neural responses in the brain. It addresses the opaqueness or "black box" issue, making it challenging to understand how judgments are reached. DL requires large amounts of data for effective training and often relies on powerful hardware for complex calculations. It excels in tasks such as audio, text, and image classification but may not be suitable for general-purpose algorithms. DL is utilized in various fields including computer vision, speech recognition, natural language processing, and medical image analysis. Artificial neural networks (ANNs) 16 are inspired by biological systems but differ in their static and symbolic nature compared to the dynamic and analog nature of biological brains. Deep learning refers to the usage of multiple layers in neural networks to gradually extract higher-level features from raw data. These deep learning layers can depart significantly from biologically informed models. Convolutional neural networks (CNNs ) 17 16 Umberto Michelucci “Applied Deep Learning. A Case-based Approach to Understanding Deep Neural Networks” Apress, 2018. 17 LiMin Fu, “Neural Networks in Computer Intelligence”, McGraw-Hill edition, 1994. are commonly used in deep learning, especially for image processing tasks. Each layer in deep learning learns to transform the input data into increasingly abstract representations. Deep learning algorithms avoid manual feature engineering by automatically learning concise intermediate representations. They can handle unsupervised learning tasks, which is advantageous due to the prevalence of unlabeled data. However, deep learning techniques have faced challenges in matching the performance of other models in certain domains, such as speech © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 14 ( ) Year 2023 D Journey of Artificial Intelligence Frontier: A Comprehensive Overview 5. Neuromorphic/Physical Neural Networks: An artificial neural network that mimics the function of a neural synapse using an electrically changeable material is known as a physical neural network, also known as a neuromorphic computer. The term " ph ysical" neural network 14 emphasizes using hardware rather than software-based methods to simulate neurons. The phrase refers to other artificial neural networks that simulate neural synapses using a memristor or another material with electrically changeable resistance.