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This essay delves into the principles, applications, and societal implications of deep learning, a subset of machine learning within ai. How deep learning algorithms, inspired by human brain neural networks, have transformed various domains, from image and speech recognition to natural language processing and autonomous systems. It also discusses the ethical and societal concerns associated with this groundbreaking technology.
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Gunadarma University Deep Learning: Revolutionizing Technology and Society Deep learning, a subset of machine learning within the broader field of artificial intelligence (AI), has transformed the landscape of technology and society. By emulating the human brain's neural networks, deep learning algorithms have achieved remarkable success in diverse domains, from image and speech recognition to natural language processing and autonomous systems. This essay explores the principles of deep learning, its applications, and the societal implications of this groundbreaking technology. Principles of Deep Learning At its core, deep learning relies on artificial neural networks (ANNs), which are computational models inspired by the biological neural networks in the human brain. These networks consist of layers of interconnected nodes or "neurons," each performing simple calculations. The power of deep learning lies in its ability to automatically discover intricate patterns in data through multiple layers of abstraction, which is why these networks are often referred to as deep neural networks (DNNs). The primary components of a deep neural network include the input layer, multiple hidden layers, and the output layer. The input layer receives raw data, which is then processed through the hidden layers. Each hidden layer consists of neurons that apply a transformation to the input data, typically involving a weighted sum of the inputs followed by a non-linear activation function. The final output layer produces the network's prediction or classification. During training, the network
adjusts the weights of connections between neurons using a process called backpropagation, which minimizes the error between the predicted and actual outputs. Applications of Deep Learning Deep learning has catalyzed advancements across various fields, offering solutions to problems once considered intractable. Some prominent applications include:
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