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Deep learning : computer vision for beginners using PyTorch.
Note: The course is primarily focused on teaching PyTorch and deep learning for computer vision, but it also includes a few sections on the fundamentals of Python (Sections 8-12). These optional learning sections are designed for individuals who may be new to Python or who want to refresh their know...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Packt Publishing,
[2023]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 250 | |a [First edition]. | ||
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| 300 | |a 1 online resource (1 video file (7 hr., 18 min.)) : |b sound, color. | ||
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| 520 | |a Note: The course is primarily focused on teaching PyTorch and deep learning for computer vision, but it also includes a few sections on the fundamentals of Python (Sections 8-12). These optional learning sections are designed for individuals who may be new to Python or who want to refresh their knowledge of Python basics. In this course, we will take a step-by-step method by first grasping PyTorch's fundamentals. Then, using a guide to getting free GPU for learning, you will learn how to code in GPU. You will then learn about PyTorch's AutoGrad feature and how to use it. Later, you will learn how to use PyTorch to create deep learning models and understand the fundamentals of convolutional neural networks (CNN). You will also learn how to use CNN with a real-world dataset. Additionally, the course will emphasize the fundamentals and lay the groundwork for an understanding of Python. We will also talk about the three significant Python libraries known as NumPy, Pandas, and Matplotlib. In this part of the course, we will also build a mini project where we will be building a hangman game in Python. By the end of this course, we will be able to perform Computer Vision tasks with deep learning. What You Will Learn Learn how to work with PyTorch Build intuition on convolution operation on images Implement gradient descent using AutoGrad Learn about LeNet architecture Create a mini-Python project game Understand how to use NumPy, Pandas, and Matplotlib libraries Audience Software developers, machine learning practitioners, data scientists, and anybody else interested in understanding PyTorch and deep learning should take this course. While a basic knowledge of Python would be beneficial, it is not a prerequisite as we will be covering the necessary fundamentals during the course. About The Author Manifold AI Learning: Manifold AI Learning℗ is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence. With a curated curriculum and a hands-on guide, you will always be an industry-ready professional. | ||
| 588 | |a Online resource; title from title details screen (O'Reilly, viewed April 11, 2023). | ||
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| 650 | 0 | |a Deep learning (Machine learning) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a PyTorch (Computer program language) | |
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| 650 | 6 | |a Apprentissage automatique. | |
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