Complete The Following Assignments In One MS Word Document

Complete The Following Assignments In One Ms Word Document1 What Is

Complete the following assignments in one MS Word document:

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.

Paper For Above instruction

Deep learning has revolutionized the field of artificial intelligence by enabling machines to learn hierarchical representations from vast amounts of data. Unlike traditional machine learning methods, which often rely on manual feature extraction and simpler algorithms, deep learning employs multilayer neural network architectures that automatically discover intricate patterns within data. This capability allows deep learning systems to perform complex tasks such as image and speech recognition with unprecedented accuracy.

Traditional machine learning methods include algorithms like decision trees, support vector machines, and linear regression. These methods typically require domain expertise for feature engineering and are limited in their ability to model complex, high-dimensional data. Deep learning overcomes these limitations by utilizing deep neural networks composed of multiple hidden layers, which enable the automatic extraction of features directly from raw data, thus reducing the need for manual intervention and enabling the learning of complex representations.

Various learning paradigms exist within artificial intelligence, including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and self-supervised learning. Supervised learning involves training models on labeled datasets to predict outcomes or classify data points. Unsupervised learning, on the other hand, seeks to identify underlying patterns or groupings within unlabeled data, such as clustering and dimensionality reduction. Reinforcement learning involves agents interacting with environments to maximize cumulative rewards, enabling applications in game playing and autonomous control. Self-supervised learning is a recent paradigm where models learn from data's inherent structure without explicit labels, bridging the gap between supervised and unsupervised approaches.

Representation learning refers to methods that automatically discover the representations needed for feature detection from raw data. It is fundamental to deep learning because it allows models to learn hierarchical features at multiple levels of abstraction, which are essential for understanding complex data. In machine learning, representation learning reduces reliance on handcrafted features, making models more adaptable and scalable. Deep learning's layered architecture inherently facilitates representation learning by composing simple building blocks into complex, high-level features that improve model performance across a wide array of tasks.

The most commonly used activation functions in artificial neural networks include sigmoid, tanh, ReLU (Rectified Linear Unit), Leaky ReLU, and softmax. The sigmoid function maps input values into a [0, 1] range, making it suitable for probabilistic interpretations but prone to vanishing gradient problems. Tanh maps inputs into a [-1, 1] range and often performs better than sigmoid in hidden layers. ReLU has become the default activation function due to its simplicity and efficiency, as it introduces non-linearity while mitigating the vanishing gradient problem. Variants like Leaky ReLU address issues with dying neurons. The softmax function converts raw outputs into probability distributions, often used in the output layer for multi-class classification tasks.

A Multilayer Perceptron (MLP) is a feedforward artificial neural network consisting of an input layer, one or more hidden layers, and an output layer. It functions by passing input data through weighted connections, where each neuron computes a weighted sum of its inputs (computing the linear combination). The sum is then transformed by an activation function, introducing non-linearity which allows the network to learn complex functions. The weights, including the activation weights and biases, are adjusted during training via algorithms like backpropagation to optimize the network’s output. The summation function aggregates the input data weighted by respective connection strengths, forming the basis for decision boundaries learned by the network.

Cognitive computing strives to develop systems that simulate human thought processes to solve complex problems. IBM Watson is a prominent example, demonstrating how AI can analyze vast data sets and derive actionable insights. One application is in healthcare, where cognitive systems assist clinicians by diagnosing diseases more accurately through integrating patient data with medical literature. In finance, cognitive analytics help detect financial fraud and assess risks by analyzing transaction data and customer behavior. In customer service, intelligent chatbots leverage cognitive computing to understand and interpret natural language, providing personalized support. These applications exemplify how cognitive computing enhances decision-making processes by mimicking human reasoning and learning capabilities in diverse real-world scenarios.

References

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