Foundations of PyTorch faq

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This course provides an introduction to the fundamentals of PyTorch, including neurons and neural networks, differential calculus, and dynamic computation graphs. Participants will gain an understanding of how to develop deep learning models in PyTorch.

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Course Overview

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Updated in [March 06th, 2023]

1. You can learn the inner workings of neurons and neural networks, as well as how activation functions, affine transformations, and layers interact within a deep learning model.


2. You can understand how a model like this is trained, or how the best values of model parameters are estimated. You can also learn how gradient descent optimization is used to optimise this process.


3. You can gain knowledge of the various types of differentiation that could be used in this process, as well as how PyTorch implements reverse-mode auto-differentiation using Autograd.


4. You can learn how to create dynamic computation graphs in PyTorch.


5. You can gain a comprehensive understanding of the foundations of PyTorch.

[Applications]
The application of the Foundations of PyTorch course can be seen in the development of deep learning models. After completing the course, students should be able to understand the inner workings of neurons and neural networks, as well as how activation functions, affine transformations, and layers interact within a deep learning model. They should also be able to understand how a model is trained, and how gradient descent optimization is used to optimize the process. Additionally, students should be able to understand the various types of differentiation that could be used in this process, as well as how PyTorch implements reverse-mode auto-differentiation using Autograd. Finally, students should be able to create dynamic computation graphs in PyTorch.

[Career Paths]
1. Machine Learning Engineer: Machine learning engineers are responsible for developing and deploying machine learning models. They use PyTorch to build, train, and deploy models for various applications. They also need to be familiar with the latest trends in deep learning and be able to apply them to their projects.

2. Data Scientist: Data scientists use PyTorch to analyze large datasets and build predictive models. They need to be familiar with the latest trends in deep learning and be able to apply them to their projects. They also need to be able to interpret the results of their models and make decisions based on them.

3. Artificial Intelligence Engineer: Artificial intelligence engineers use PyTorch to develop and deploy AI-based applications. They need to be familiar with the latest trends in deep learning and be able to apply them to their projects. They also need to be able to interpret the results of their models and make decisions based on them.

4. Research Scientist: Research scientists use PyTorch to develop and deploy AI-based applications. They need to be familiar with the latest trends in deep learning and be able to apply them to their projects. They also need to be able to interpret the results of their models and make decisions based on them. They also need to be able to conduct research and develop new algorithms and techniques for AI-based applications.

The development of AI and deep learning is rapidly growing, and the demand for professionals with expertise in these areas is increasing. As such, these four career paths are expected to continue to grow in the coming years.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, operating systems, and computer architecture. It also covers topics such as artificial intelligence, machine learning, and deep learning. With the increasing demand for data science and AI, this degree path is becoming increasingly popular.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of AI systems and their applications. It covers topics such as natural language processing, computer vision, robotics, and machine learning. It also provides an in-depth understanding of the underlying principles of AI and its applications.

3. Master of Science in Data Science: This degree path focuses on the development of data-driven solutions. It covers topics such as data mining, machine learning, and data visualization. It also provides an in-depth understanding of the underlying principles of data science and its applications.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and their applications. It covers topics such as supervised and unsupervised learning, deep learning, and reinforcement learning. It also provides an in-depth understanding of the underlying principles of machine learning and its applications. With the increasing demand for AI and machine learning, this degree path is becoming increasingly popular.

Course Syllabus

Representation Learning Using Neural Networks

Neuron as a Mathematical Function

Activation Functions

Introducing PyTorch

TensorFlow and PyTorch

Demo: PyTorch Install and Setup

Course Provider

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AZ Class hope that this paid Pluralsight course can help your Pytorch skills no matter in career or in further education. Even if you are only slightly interested, you can take Foundations of PyTorch course with confidence!

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Quiz

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1. What is Autograd in PyTorch?

2. What is the purpose of gradient descent optimization?

3. What is the main purpose of PyTorch?

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