Deep Learning with PyTorch

Take our Deep Learning with PyTorch course! Learn how to use your knowledge of Python to create neural networks with Facebook’s PyTorch library.

 

Student rating

5.00 Out Of 45 Students

What You’ll Learn

  • Learn how to use NumPy to format data into arrays
  • Use pandas for data manipulation and cleaning
  • Learn classic machine learning theory principals
  • Use PyTorch Deep Learning Library for image classification
  • Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data
  • Create state of the art Deep Learning models to work with tabular data

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Course Content

This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! PyTorch’s deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.

 

  • NumPy
  • Pandas
  • Machine Learning Theory
  • Test/Train/Validation Data Splits
  • Model Evaluation – Regression and Classification Tasks
  • Unsupervised Learning Tasks
  • Tensors with PyTorch
  • Neural Network Theory
    • Perceptrons
    • Networks
    • Activation Functions
    • Cost/Loss Functions
    • Backpropagation
  • Gradients
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks

 

Intermediate to experienced Python developers who want to learn how to use Facebook’s PyTorch library for creating deep neural networks.

 

Course Description

This PyTorch training course is designed for students familiar with the basics of data science and machine learning who want to learn how to use Facebook’s PyTorch library for deep learning and neural network based applications. PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.

 

What Students Are Saying

Jose is the man. He is extremely helpful on the road to developing essential Data Science skills.

James Hizon

Absolute masterclass! Examples given here are well explained and can be applied to real ML problems. In my opinion, so far best PyTorch tutorial online available. Keep going Jose!

Piotr Bełda

Excellent way of presenting concept followed up with exercises. Thanks

Pravin Kumar

Extremely good. This is the best tutorial on PyTorch that i have seen, so simple and so rightly paced for a beginner... You have a Talent of Teaching, please keep it up..

Abhijeet Konduskar

Good course to teach PyTorch. Not much on deep learning and doesn't go as deep into some things as I may like. But I think this is all okay. I don't expect (or want) deep learning theory when learning a library. Certainly worth diving deeper into some of the PyTorch library though. Overall fantastic, and would recommend.

Anonymized User

This instructor really does an excellent job explaining the material clearly with great examples.

John Kopsky

I didn't expect to find so clear explanations. The instructor is really good.

Arturo Padilla

Tired of instructors who throw around fancy words without explaining what they mean and why? I've watched a ton of videos on machine learning and no one explained it better than Jose. He doesn't miss anything that could potentially leave you frustrated if left unexplained. All the steps are well thought through and make sense.

Ildar Masagutov

I am a big fan of all Jose's coding courses. Very informative, very knowledgeable, and very easy to understand. Keep the courses coming!

Eric Esajian

I got confident to create my own model usig PyTorch

Jenisha T
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Course type
  • In-House
  • Self-Paced
Student rating

5.00

Course length

97 Lectures

22 Hours

On-Demand training
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