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221017 ||| eng |
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|a 9781804617878
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| 050 |
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4 |
|a Q325.73
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| 245 |
0 |
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|a Python for deep learning
|b build neural networks in Python
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| 250 |
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|a [First edition]
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| 260 |
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|a [Place of publication not identified]
|b Packt Publishing
|c 2022
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| 300 |
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|a 1 video file (2 hr., 9 min.)
|b sound, color
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| 653 |
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|a Apprentissage profond
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Deep learning (Machine learning) / fast
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Deep learning (Machine learning) / http://id.loc.gov/authorities/subjects/sh2021006947
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| 653 |
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|a Python (Langage de programmation)
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| 710 |
2 |
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|a Meta Brains (Firm)
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| 710 |
2 |
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|a Packt Publishing
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 856 |
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|u https://learning.oreilly.com/videos/~/9781804617878/?ar
|x Verlag
|3 Volltext
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|a 006.3/1
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| 520 |
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|a Master Data Science, TensorFlow, Artificial Intelligence, and Neural Networks with this comprehensive deep learning course for absolute beginners About This Video Fundamentals course designed for both beginners and experts alike Use different frameworks in Python to solve real-world problems using deep learning and AI Make predictions using linear regression, polynomial regression, and multivariate regression In Detail Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python's best application is in deep learning and artificial intelligence tasks. We will start with an introduction to deep learning where we will focus on the fundamentals of the deep learning theory and learn how to use deep learning in Python. Followed by this we will move on to Artificial Neural Networks (ANN). You will learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence. Next, we will make predictions using linear regression, polynomial regression, and multivariate regression, and build artificial neural networks with TensorFlow and Keras. We will also cover Convolutional Neural Networks (CNN) at length and go through the different components such as convolution layer, pooling layer, and fully connected layer. Finally, we will wrap up the implementation of CNN in Python. By the end of this course, you will be able to use the concepts of deep learning to build neural networks in python like a professional. Audience This course is intended for both beginners and professionals in programming who want to expand their knowledge of deep learning or professional mathematicians who want to learn how to analyze data programmatically. Basic mathematical skills and Python coding experience are prerequisites
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