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Fundamentals of neural networks.
Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recogni...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Packt Publishing,
[2022]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 0 | 0 | |a Fundamentals of neural networks. |
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| 511 | 0 | |a Yiqiao Yin, presenter. | |
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| 520 | |a Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). You will also have code blocks and notebooks to help you understand the topics covered in the course. By the end of this course, you will have a hands-on understanding of Neural Networks in detail. What You Will Learn Learn about linear and logistic regression in ANN Learn about cross-entropy between two probability distributions Understand convolution operation which scans inputs with respect to their dimensions Understand VGG16, a convolutional neural network model Understand why to use recurrent neural network Understand Long short-term memory (LSTM) Audience This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. There is no prior coding or programming experience required. This course assumes you have your own laptop, and the code will be done using Colab. About The Author Yiqiao Yin: Yiqiao Yin was a Ph.D. student in statistics at Columbia University from September of 2020 to December 2021. He has a B.A. in mathematics and an M.S. in finance from the University of Rochester. He also has a wide range of research interests in representation learning: Feature Learning, Deep Learning, Computer Vision, and (NLP). Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street. | ||
| 588 | |a Online resource; title from title details screen (O'Reilly, viewed January 10, 2023). | ||
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| 650 | 0 | |a Deep learning (Machine learning) | |
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