Docsity
Docsity

Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity


Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan


Guidelines and tips
Guidelines and tips

Deep Learning: Revolutionizing Technology and Society, Essays (high school) of Machine Learning

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.

Typology: Essays (high school)

2023/2024

Available from 06/02/2024

ricoputrabuana
ricoputrabuana 🇮🇩

28 documents

1 / 4

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
Deep Learning Essay
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
pf3
pf4

Partial preview of the text

Download Deep Learning: Revolutionizing Technology and Society and more Essays (high school) Machine Learning in PDF only on Docsity!

Deep Learning Essay

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:

  1. Computer Vision : Deep learning models, such as convolutional neural networks (CNNs), have revolutionized image and video analysis. Applications range from facial recognition systems used in security and social media, to medical imaging techniques that assist in diagnosing diseases like cancer with high accuracy.
  2. Natural Language Processing (NLP) : Recurrent neural networks (RNNs) and transformers have significantly improved the ability of machines to understand and generate human language. These advancements power applications like machine translation, sentiment analysis, chatbots, and virtual assistants such as Apple's Siri and Amazon's Alexa.
  3. Speech Recognition : Deep learning has enhanced the performance of speech-to-text systems, enabling more accurate and efficient transcription services. This technology is fundamental to voice-activated assistants and real-time translation services.
  4. Autonomous Vehicles : Self-driving cars rely on deep learning algorithms to process vast amounts of sensor data, including images from cameras, lidar, and radar, to navigate safely and efficiently. Companies like Tesla, Waymo, and Uber are at the forefront of this technology.
  5. Healthcare : In addition to medical imaging, deep learning is used for predictive analytics, drug discovery, and personalized medicine. For instance, deep learning models can predict patient outcomes and identify potential treatment plans based on historical health data.

References Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature , 521(7553), 436-444. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems , 25, 1097 - 1105. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810..