Tag: Classification

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From the Publisher

Chris Kuo

Chris Kuo

Chris Kuo has been a quantitative professional for more than 20 years. During that time, he contributed various data science solutions to industrial operations including customer analytics, risk segmentation, insurance, underwriting, claims, workers’ compensation, fraud detection, and litigation. He is the inventor of a U.S. patent. He has worked at the Hartford Insurance Group (HIG), the American International Group (AIG), Liberty Mutual Insurance, the BJ’s Wholesale Club, and Brookstone Inc.

Chris Kuo is a passionate educator. He has been an adjunct professor at Columbia University, Boston University, University of New Hampshire, and Liberty University since 2001. He published articles in economic and management journals and was a journal reviewer for related journals. He is the author of The eXplainable A.I., Modern Time Series Anomaly Detection: With Python & R Code Examples, and Transfer Learning for Image Classification: With Python Examples. He is known as Dr. Dataman on Medium.com.

He received his undergraduate degree in Nuclear Engineering from TsingHua National University in Taiwan, and his Ph.D. in Economics from the State University of New York at Stony Brook. He lives in New York City with his wife France.

Books by Chris Kuo

Modern Time Series Anomaly Detection

This book is for data science professionals who want to get hands-on practice with cutting-edge techniques in time series modeling, forecasting, and anomaly detection. This book is certainly suitable for students who want to advance in time series modeling and fraud detection.

Transfer Learning for Image Classification

Transfer learning techniques have gained popularity in recent years. It builds image classification models effectively for any specialized use cases. This subject is not necessarily easy to navigate because it utilizes much knowledge developed in the past two decades. This book explains transfer learning, its relationships to other fields in Computer Vision (CV), the development of pre-trained models, and the application of transfer learning to pre-trained models. This book guides you to build your own transfer learning models. You will apply the transfer learning techniques to your image classification model successfully.

The eXplainable A.I.

How AI systems make decisions is not known to most people. Many of the algorithms, though achieving a high level of precision, are not easily understandable for how a recommendation is made. This is especially the case in a deep learning model. As humans, we must be able to fully understand how decisions are made so we can trust the decisions of AI systems. We need ML models to function as expected, to produce transparent explanations, and to be visible in how they work. Explainable AI (XAI) is important research and has been guiding the development of AI. It enables humans to understand the models so as to manage effectively the benefits that AI systems provide, while maintaining a high level of prediction accuracy. Explainable AI answers the following questions to build the trusts of users for the AI systems:

● Why does the model predict that result?

● What are the reasons for a prediction?

● What is the prediction interval?

● How does the model work?

ASIN ‏ : ‎ B0BDGF3FV1
Publication date ‏ : ‎ September 6, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 18618 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 127 pages

Transfer Learning for Image Classification: With Python Examples

In the field of deep learning, transfer learning has emerged as a powerful technique for leveraging pre-trained models to solve new tasks with minimal data and computational resources. In this post, we will explore how transfer learning can be applied to image classification tasks using Python and popular deep learning frameworks like TensorFlow and Keras.

Transfer learning works by taking a pre-trained model that has been trained on a large dataset, such as ImageNet, and reusing its learned features to solve a new classification task. By fine-tuning the pre-trained model on a smaller dataset specific to the new task, we can achieve impressive results with less data and training time.

To demonstrate transfer learning in action, we will use a pre-trained ResNet50 model available in Keras and fine-tune it on the CIFAR-10 dataset, which consists of 60,000 32×32 color images in 10 classes. We will first load the pre-trained ResNet50 model and remove the top classification layer, replacing it with a new fully connected layer with 10 output units corresponding to the 10 classes in CIFAR-10.

Next, we will freeze the weights of the pre-trained layers and only train the weights of the new fully connected layer on the CIFAR-10 dataset. This allows us to quickly adapt the pre-trained model to the new task without forgetting the valuable features it has learned from the original dataset.

Finally, we will evaluate the fine-tuned model on the test set of CIFAR-10 and compare its performance with a randomly initialized model trained from scratch. By the end of this post, you will have a solid understanding of how transfer learning can be used for image classification tasks and how to implement it using Python and deep learning frameworks.

Stay tuned for the upcoming code examples and step-by-step instructions on how to implement transfer learning for image classification with Python.
#Transfer #Learning #Image #Classification #Python #Examples