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

What is deep learning? What can deep learning do that tr

Discussion Question #1: What is deep learning? What can deep learning do that traditional machine-learning methods cannot?

Discussion Question #2: List and briefly explain different learning paradigms/methods of AI.

Discussion Question #3: What is representation learning, and how does it relate to machine learning and deep learning?

Discussion Question #4: List and briefly describe the most commonly used ANN activation functions.

Discussion Question #5: What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.

Exercise #4: Identify at least three application cases where cognitive computing was used to resolve complex real-world problems. Summarize your findings in a professional organized report.

Paper For Above Instructions

Deep learning, a subset of machine learning and artificial intelligence (AI), has gained immense popularity in recent years due to its ability to analyze vast amounts of data and extract meaningful patterns. Unlike traditional machine learning methods, which often require manual feature extraction, deep learning utilizes neural networks with multiple layers to automatically identify the characteristics of data (LeCun, Bengio & Haffner, 1998). This ability to learn representations of data directly from raw inputs allows deep learning models to excel in tasks such as image recognition, natural language processing, and complex decision-making.

One significant advantage of deep learning over traditional methods is its performance in high-dimensional data contexts. For instance, while classical algorithms might struggle with large datasets involving images or audio, convolutional neural networks (CNNs) can effectively process these types of information by automatically learning spatial hierarchies of features (Krizhevsky, Sutskever & Hinton, 2012). Furthermore, deep learning models can continue improving as they are exposed to more data, allowing them to refine their interpretations and predictions beyond the capacities of conventional learning frameworks.

Regarding learning paradigms, several methodologies define how AI systems learn from data. These include supervised learning, where models are trained on labeled datasets; unsupervised learning, which seeks to find patterns in unlabeled data; semi-supervised learning, a combination of both; and reinforcement learning, where agents learn by receiving feedback from actions taken in an environment (Sutton & Barto, 2018). Each method possesses unique uses and applications, showcasing the diversity within the field of AI.

Representation learning is another critical concept in this domain. It refers to the techniques used to enable a machine learning model to learn features or representations of the input data that facilitate performance on various tasks (Bengio et al., 2013). In deep learning, representation learning often occurs in the layers of the neural networks themselves, allowing these systems to create abstractions of data through inter-layer transformations. This capability leads to improved performance in tasks such as speech recognition and sentiment analysis.

When discussing activation functions in artificial neural networks (ANNs), one frequently encounters several commonly used types. The sigmoid function, for instance, maps input values to a range between 0 and 1, making it suitable for binary classification (Glorot et al., 2011). Another popular choice is the rectified linear unit (ReLU), which introduces non-linearity into the model while being computationally efficient. Variants of ReLU, such as Leaky ReLU and Parametric ReLU, have been developed to address issues like "dying ReLU," where neurons become inactive during training (Xu et al., 2015). Each activation function has its strengths and limitations, affecting the training dynamics and overall performance of neural networks.

The multi-layer perceptron (MLP) is a fundamental type of ANN characterized by layers of interconnected neurons: an input layer, one or more hidden layers, and an output layer. MLPs learn to map inputs to desired outputs through a series of transformations facilitated by weights and activation functions. The summation in MLP refers to the aggregation of inputs and their corresponding weights before being passed to an activation function where non-linearity is introduced (Murphy, 2012). The notion of weights is critical as they determine the strength and direction of influence that an input has on the neuron's output, allowing the model to learn effectively from training data.

In the realm of cognitive computing, we witness practical applications of these complex AI and deep learning techniques. For example, IBM's Watson exemplifies cognitive computing's potential through its success on the quiz show Jeopardy!, where it effectively processed natural language queries and provided accurate responses. Additionally, cognitive computing has been utilized in healthcare; for instance, IBM Watson Health aids in diagnosing diseases and personalizing treatment plans by analyzing patient data and relevant medical literature (Jiang et al., 2017). Another noteworthy application is in financial services, where cognitive systems assist in fraud detection by analyzing transaction patterns and identifying anomalies (Ngai et al., 2011). These examples illustrate the profound impact of cognitive computing technologies in solving real-world problems.

In conclusion, deep learning stands out as a significant advancement in machine learning due to its unique capabilities, particularly in handling complex and high-dimensional datasets. Furthermore, understanding the various learning paradigms, the importance of representation learning, and the role of activation functions deepens our insight into how these systems function. The serviceable applications of cognitive computing further showcase the growing relevance of AI technologies in addressing complex challenges across various domains.

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.
  • Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep Sparse Rectifier Neural Networks. In AISTATS 2011 (Vol. 15, pp. 315–323).
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial Intelligence in Health Care: Anticipating Challenges to Ethics, Privacy, and Bias. SAGE Open Medicine, 5, 2050312117745725.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (pp. 1097–1105).
  • Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
  • Ngai, E. W. T., Hu, Y., Wong, Y. H., Hsu, C. H., & Chan, H. K. (2011). The Application of Data Mining Technologies in Financial Fraud Detection: A Classification Framework. Decision Support Systems, 50(3), 559-569.
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
  • Xu, L., Wang, F., & Gang, H. (2015). Empirical Evaluation of Rectified Activations in Convolutional Network. In Proceedings of the 3rd International Conference on Learning Representations (ICLR).
  • LeCun, Y., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

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