Deep Learning with Python 在线电子书 图书标签: 深度学习 Python 机器学习 人工智能 AI
发表于2024-12-24
Deep Learning with Python 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024
https://github.com/greatabel/MachineLearning/tree/master/05Deep%20Learning%20with%20Python-%20A%20Hands-on%20Introduction
评分利用openCV+Keras破解验证码,不仅仅是图片识别,更是深度学习和计算机视觉技术的良好开端。简单来说,用WordPress获取验证码后,将验证码图像分割成独立字符,调用神经网络对独立的字符块进行预测,将多个预测结果排列以作为该 验证码的返回结果。
评分利用openCV+Keras破解验证码,不仅仅是图片识别,更是深度学习和计算机视觉技术的良好开端。简单来说,用WordPress获取验证码后,将验证码图像分割成独立字符,调用神经网络对独立的字符块进行预测,将多个预测结果排列以作为该 验证码的返回结果。
评分https://github.com/greatabel/MachineLearning/tree/master/05Deep%20Learning%20with%20Python-%20A%20Hands-on%20Introduction
评分利用openCV+Keras破解验证码,不仅仅是图片识别,更是深度学习和计算机视觉技术的良好开端。简单来说,用WordPress获取验证码后,将验证码图像分割成独立字符,调用神经网络对独立的字符块进行预测,将多个预测结果排列以作为该 验证码的返回结果。
From the Back Cover
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Pythonallows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.Deep Learning with Pythonalso introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.You will:Leverage deep learning frameworks in Python namely, Keras, Theano, and CaffeGain the fundamentals of deep learning with mathematical prerequisitesDiscover the practical considerations of large scale experimentsTake deep learning models to production
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About the Author
Nikhil S. Ketkar currently leads the Machine Learning Platform team at Flipkart, India’s largest e-commerce company. He received his Ph.D. from Washington State University. Following that he conducted postdoctoral research at University of North Carolina at Charlotte, which was followed by a brief stint in high frequency trading at Transmaket in Chicago. More recently he led the data mining team in Guavus, a startup doing big data analytics in the telecom domain and Indix, a startup doing data science in the e-commerce domain. His research interests include machine learning and graph theory.
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Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.
This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.
Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.
What You Will Learn
Leverage deep learning frameworks in Python namely, Keras, Theano, and CaffeGain the fundamentals of deep learning with mathematical prerequisitesDiscover the practical considerations of large scale experimentsTake deep learning models to production
Who This Book Is ForSoftware developers who want to try out deep learning as a practical solution to a particular problem.Software developers in a data science team who want to take deep learning models developed by data scientists to production.
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Deep Learning with Python 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024