What Is Deep Learning? What Can Deep Learning Do That Tradit ✓ Solved

What Is Deep Learning What Can Deep Learning Do That Traditional M

1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot? 2. List and briefly explain different learning paradigms/ methods in AI.

3. What is representation learning, and how does it relate to machine learning and deep learning? 4. List and briefly describe the most commonly used ANN activation functions. 5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN. 6. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/ computers to show “intelligent” behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many real-world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.

Sample Paper For Above instruction

Deep learning, a subset of machine learning within artificial intelligence (AI), has revolutionized numerous fields by enabling computers to learn hierarchical representations of data. Unlike traditional machine learning methods that often rely on handcrafted features and shallow models, deep learning employs multi-layered neural networks capable of automatically discovering intricate data patterns. This fundamental difference allows deep learning to excel in complex tasks such as image and speech recognition, natural language processing, and more, surpassing the capabilities of traditional algorithms.

Traditional machine learning encompasses various learning paradigms, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning (Samuel, 1959). Supervised learning trains models on labeled datasets to predict outcomes, as seen in spam detection or medical diagnosis. Unsupervised learning, on the other hand, finds hidden patterns or groupings within unlabeled data, exemplified by clustering algorithms like K-means. Reinforcement learning involves learning optimal actions through trial-and-error interactions with an environment, as demonstrated by game-playing AI systems such as AlphaGo. Semi-supervised learning combines both labeled and unlabeled data to improve model performance where labeled data is scarce.

Representation learning is a core concept in machine learning and deep learning, referring to the automatic discovery of features or representations from raw data that allow models to perform tasks more effectively (Bengio et al., 2013). In deep learning, multiple hidden layers enable the learning of increasingly abstract and high-level representations, facilitating better generalization and robustness. This contrasts with traditional approaches that depend heavily on manual feature engineering, making deep learning especially powerful for complex perceptual tasks like image classification or speech recognition.

The most commonly used Artificial Neural Network (ANN) activation functions include sigmoid, hyperbolic tangent (tanh), ReLU (Rectified Linear Unit), leaky ReLU, and softmax (Nair & Hinton, 2010). The sigmoid function maps inputs to an output between 0 and 1, useful for probability estimation but prone to vanishing gradients. Tanh outputs values between -1 and 1, centering data and aiding convergence. ReLU, defined as the positive part of its input, has become predominant due to its simplicity and efficiency, especially in deep networks. Softmax converts logits into probability distributions across multiple classes, mainly used in the output layer for classification tasks.

The Multilayer Perceptron (MLP) is a fundamental type of feedforward neural network consisting of an input layer, one or more hidden layers, and an output layer. MLPs operate by performing weighted sums of input signals, followed by passing these sums through activation functions (Rumelhart et al., 1986). The summation function computes a linear combination of inputs and associated weights, where each weight reflects the importance of its corresponding input. Activation weights serve to adjust the influence of each connection, enabling the network to model complex, non-linear relationships. During training, these weights are iteratively optimized via algorithms like backpropagation, motivating the network to improve performance on the desired task.

Cognitive computing aims to emulate human reasoning and decision-making processes, enhancing machines' ability to understand, interpret, and respond to complex information. Notable applications include diagnosing diseases using AI-powered medical systems like IBM Watson for Oncology, where cognitive analytics assist physicians in treatment planning by analyzing vast amounts of clinical data (Ferrucci et al., 2010). In financial services, cognitive computing facilitates fraud detection by analyzing transaction patterns and detecting anomalies in real-time. Another application is in customer service, where chatbots powered by cognitive systems provide personalized, context-aware support, reducing response times and improving customer satisfaction (Shadi et al., 2019). These cases demonstrate how cognitive computing addresses complex, data-intensive problems across diverse industries, contributing significantly to automation and augmented decision-making.

References

• Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
• Ferrucci, D., Chopra, S., Curwen, M., et al. (2010). Building IBM Watson: An overview of the DeepQA project. AI Magazine, 31(3), 59-79.
• Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807-814.
• Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210-229.
• Shadi, A., Khamis, M. A., & Suganthi, R. (2019). Cognitive chatbot for customer support: A review. Journal of Ambient Intelligence and Humanized Computing, 10(8), 3085-3100.