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Learn PyTorch Hands-on with 7 Applied Projects

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7 Guided Projects

About this

Explore the dynamic world of machine learning and deep learning with our comprehensive package of seven highly-rated hands-on PyTorch projects. Designed for both beginners and intermediate learners, this collection covers a diverse range of applications, from stock price prediction using LSTM networks to deploying computer vision apps in serverless environments. Delve into object detection with Faster R-CNN, tackle medical image segmentation with U-Net, and enhance image classification skills using Vision Transformers. For those with a creative flair, generate unique anime characters using DCGANs. Each project provides step-by-step guidance, empowering you to build, train, and optimize deep learning models in real-world scenarios, and equipping you with the skills to advance in the field of artificial intelligence.
Average Course Rating

4.6 out of 5

Effort

6 Hours

Average Difficulty Level

Intermediate

Skills You Will Learn

Machine Learning, Open Source AI, Python, PyTorch, Deep Learning, Lstm

Language

English

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List of Projects
  • Getting Started with Machine Learning with PyTorch
    Beginner Guided Project Artificial Intelligence

    Getting Started with Machine Learning with PyTorch

    PyTorch is a leading open source framework for AI research and commercial production in machine learning. It is used to build, train, and optimize deep learning neural networks for applications such as image recognition, natural language processing, and speech recognition.

    4.5
    (325 Reviews)
    1.8k+ Enrolled
    1 Hour
  • Predict stock prices with LSTM in PyTorch
    Beginner Guided Project Machine Learning

    Predict stock prices with LSTM in PyTorch

    Learn to predict time series data with Long Short-Term Memory (LSTM) in PyTorch. Create a deep learning model that can predict a stock's value using daily Open, High, Low, and Close values and practice visualizing results and evaluating your model. Build foundational skills in machine learning while exploring the LSTM architecture. Develop practical knowledge with this beginner-friendly tutorial and apply it to real-world datasets using PyTorch.

    4.6
    (27 Reviews)
    213 Enrolled
    30 Minutes
  • Deploy a Computer Vision App in a Serverless Environment
    Beginner Guided Project Containers

    Deploy a Computer Vision App in a Serverless Environment

    Learn how to make your object detection application available to the world by deploying to a serverless environment. Focus on building your app instead of buying, installing or configuring servers.

    4.4
    (119 Reviews)
    688 Enrolled
  • Object detection with Faster R-CNN  and PyTorch
    Intermediate Guided Project Deep Learning

    Object detection with Faster R-CNN and PyTorch

    Faster R-CNN is a method for object detection that uses region proposal. In this project, you will use Faster R-CNN pre-trained on the COCO dataset. You will learn how to detect several objects by name and to use the likelihood of the object prediction being correct.

    4.6
    (166 Reviews)
    957 Enrolled
    1 Hour
  • Medical image segmentation with PyTorch and U-Net
    Intermediate Guided Project Deep Learning

    Medical image segmentation with PyTorch and U-Net

    Explore biomedical image segmentation in computer vision with PyTorch and U-Net, an architecture also used in Stable Diffusion. Learn to construct a U-Net model using PyTorch's convolutional, max-pooling, and upsampling layers. Through hands-on steps, train your model to automate image segmentation, showcasing the power of deep learning in medical imaging. Perfect for biomedical engineering or data science professionals aiming to enhance diagnostic accuracy.

    4.7
    (78 Reviews)
    447 Enrolled
    30 Minutes
  • Vision Transformers for Image Classification Hands-on
    Intermediate Guided Project Computer Vision

    Vision Transformers for Image Classification Hands-on

    Up your game in Image classification by using Vision Transformers to achieve remarkable performance, surpassing CNN-based methods, and delivering state-of-the-art results on large image datasets.

    4.5
    (54 Reviews)
    367 Enrolled
    1 Hour
  • Creating anime characters using DCGANs and  PyTorch
    Intermediate Guided Project Artificial Intelligence

    Creating anime characters using DCGANs and PyTorch

    Mass production of millions of unique anime characters is nearly impossible for even the most skilled painter, but it becomes feasible with the use of machine learning methods. In this guided project, you will have the opportunity to build machine learning models and generate anime characters on your own. Furthermore, you will explore the Deep Convolutional Generative Adversarial Networks (DCGANs) method, which is specifically designed for large-scale anime production.

    4.6
    (85 Reviews)
    584 Enrolled
    2 Hours

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