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Artificial Intelligence, Machine Learning, and Deep Learning have become popular buzzwords of late.
PyTorch is a skill that any deep learning engineer should have as part of their resume.
What is PyTorch?
PyTorch is a popular machine learning library used with the Python programming language.
PyTorch makes it easy for developers to build and train machine learning models quickly and easily.
It was developed by Meta based on the original Torch library made for the Lua programming language.
PyTorch is free to use and open-source.
As a result, it is a popular choice among developers to build deep learning applications.
Why use PyTorch?
This course is probably the most comprehensive you will find on PyTorch that will make you job ready.
To make the most out of this course, you must already knowPythonbefore commencing.
In addition, Machine Learning knowledge is recommended though not required.
A Deep Understanding of Deep Learning
This Deep Learning course by Udemy is highly-rated and enrolled.
This is in part because it has fewer prerequisites.
Instead, you will be taught everything that you should probably understand PyTorch and deep learning from scratch.
To prepare you further, will learn Python and Numpy.
This course also covers the different types of networks you will work with.
These include Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks, and Feed-Forward Networks.
You will learn how to implement these in PyTorch.
As mentioned before, this course requires no prior knowledge before beginning.
A lot of what you will learn will be in the context of PyTorch.
In addition, you will learn how to build Natural Language Processing and Recommender systems.
You will also learn Computer Vision, Generative Adversarial networks, and Reinforcement learning.
Before starting this course, you should already know Python and Numpy.
It is also recommended that you know derivatives and probability.
It includes code examples that you might edit and run to confirm you understand what each line does.
You will learn to implement amachine-learning modeland train it.
Knowledge of Python, Jupyter notebooks, and basic machine learning are the only requirements of this course.
The course begins by introducing you to PyTorch by comparing and contrasting it to Numpy.
As a result, familiarity with Numpy helps, and knowledge of Python is required.
The course then teaches you how to use PyTorch to build Artificial Neural Networks.
You will also learn how to build and use Convolutional Neural Networks.
In addition to knowing Python, you should also know Supervised learning with Python.
It demonstrates that machine learning can be accessible and understandable to anyone with Python knowledge and little math.
The book does not cover Python, so you will need to know it beforehand.
PyTorch Pocket Reference
This book serves as a concise reference for learning PyTorch.
It covers everything from the basic PyTorch syntax to optimizing your training.
You will build Natural Language classifiers based on Wikipedia and use torch audio to classify audio data.
You will also learn how to deploy models to production using Docker.
PyTorch is an incredibly useful library to know as a machine learning engineer.
We hope this article has helped introduce you to PyTorch.
