Getting Started with Google Colab Using PyTorch (Manning Publications)

Getting Started with Google Colab Using PyTorch (Manning Publications)
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intermediate Python • basics of Jupyter Notebook • basics of Google Colab • basics of PyTorch, scikit-learn, Git and GitHub • basics of Naive Bayes, neural networks, classification, and evaluation
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Getting Started with Google Colab Using PyTorch (Manning Publications)
In this liveProject, you’ll get hands-on experience using the powerful Google Colab tool for machine learning and deep learning. Colab notebooks let you execute your data science code in Google’s cloud, getting all the benefits of Google’s incredible hardware. You’ll see how Colab works for yourself by running through simple machine learning tasks such as data preprocessing, making use of Colab’s free GPU and TPU hardware acceleration capabilities, and combining Colab with scikit-learn and PyTorch to train a classifier.

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This liveProject is for intermediate Python programmers who know the basics of data science and machine learning. To complete the second milestone of this liveProject, you will work with PyTorch. To begin this liveProject you will need to be familiar with the following:

TOOLS

- Intermediate Python

- Basics of Jupyter Notebook

- Basics of Google Colab

- Basics of PyTorch

- Basics of scikit-learn

- Basics of Git and GitHub

- Basics of Google Drive

TECHNIQUES

- Naive Bayes

- Neural Networks

- Classification

- Evaluation

you will learn

In this liveProject, you’ll learn how to effectively utilize Google Colab in a data science project. Mastery of Colab opens up free resources that you can use to operate processor-taxing data science that is often impossible on personal hardware.

- Using Colab as a Jupyter Notebook

- Reading input from your Google Drive

- Utilizing Colab hardware acceleration capabilities

- Combining Colab with PyTorch

- Getting the most out of Colab’s free resources



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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Free Course
intermediate Python • basics of Jupyter Notebook • basics of Google Colab • basics of PyTorch, scikit-learn, Git and GitHub • basics of Naive Bayes, neural networks, classification, and evaluation

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.