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Building End-to-End Vision Applications

This course provides an introduction to machine learning for computer vision with a focus on practical applications relevant to industry teams. In this course, we will “reverse-engineer” a number of applications, such as traffic flow analysis, digital medicine, optical character recognition, and video analytics. We will discuss the fundamental machine learning principles required to build these applications, focusing on practical tools instead of algorithmic details. You will build these applications from scratch, using open-source tools that cover the full stack of modern machine learning, from datasets to deployment. By the end of the course, you will have built a portfolio of computer vision applications that you can reference or share with your team and colleagues.

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Abubakar Abid
Machine Learning Team Lead at Hugging Face
Real-world projects that teach you industry skills.
Learn alongside a small group of your professional peers
Part-time program with 2 live events per week:
Lecture
Monday @ 5:00 PM UTC
Project Session
Wednesday @ 5:00 PM UTC
Next Cohort
March 20, 2023
Duration
4 weeks
Price
US$ 400
or included with membership

Course taught by expert instructors

Instructor Photo
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Abubakar Abid

Machine Learning Team Lead at Hugging Face

Abubakar has been building machine learning models for over a decade. He did his PhD at Stanford in deep learning applied to medical images and videos. During his PhD, he developed Gradio (www.gradio.dev), an open-source Python library for creating GUIs for machine learning models. Since Gradio’s acquisition by Hugging Face, Abubakar continues to lead the Gradio team and also teaches machine learning at Hugging Face and beyond!

The course

Learn and apply skills with real-world projects.

Project
You will build a machine learning model to classify plants, and deploy it as an application using the concepts we have studied our first week. You will then be able to take your phone and test the web application.
    Learn
    • Steps to do machine learning: from building datasets to deploying applications
    • Overview of algorithms for image classification, including an overview of recent progress in deep deep learning in the last decade (from AlexNet to Transformers)
    • Training vs. fine-tuning machine learning models
    • How to download models from the Hugging Face Hub using the transformers library
    • How to finetune a model for image classification
    Project
    You will build a machine learning model to segment images for self-driving cars (e.g. into pedestrians, roads, etc.), and deploy it as an application using the concepts we have studied our second week. You will then be able to take your application and test it with real images of the road.
      Learn
      • The machine learning system that powers a self-driving car
      • Different kinds of image segmentation (semantic segmentation, object detection)
      • Overview of algorithms for image segmentation
      • How to download datasets from the Hugging Face Hub using the datasets library
      • How to train an image segmentation model from scratch
      Project
      You will build a machine learning model that can recognize if a photo is of an authorized person. You will be able to deploy it as an application using the concepts we have studied our third week. You will be able to test it with pictures from a webcam / phone camera.
        Learn
        • Machine learning system that powers the FaceID authentication system for Apple iPhones
        • Different ways images can be converted into embeddings
        • Different uses of embeddings
        • How to download datasets from the Hugging Face Hub using the datasets library
        • How to train an image segmentation model from scratch
        Project
        You will build a machine learning model that can generate new images of people that do not exist. You will then deploy it on a web application and use it to generate new images.
          Learn
          • Machine learning models for generating images including GANs and diffusion models
          • Different uses of image generation
          • Ethical risks and biases that are part of such applications
          • How to train an image generation model from scratch
          • How to add class conditioning so that you can generate specific kinds of images

          Real-world projects

          Work on projects that bring your learning to life.
          Made to be directly applicable in your work.

          Live access to experts

          Sessions and Q&As with our expert instructors, along with real-world projects.

          Network & community

          Core reviews a study groups. Share experiences and learn alongside a global network of professionals.

          Support & accountability

          We have a system in place to make sure you complete the course, and to help nudge you along the way.

          Course success stories

          Learn together and share experiences with other industry professionals

          It has been my pleasure to be Abubakar Abid's student as he has taught visual algorithms. Abubakar is an excellent educator with a great ability to explain complex concepts in a simple and intuitive manner, something that has made learning enjoyable for me. Not only is he very highly capable from the technical aspects, but he is also an outstanding communicator

          Tarek NaousGraduate Student at American University of Beirut, Lebanon

          Abubakar is amongst the best teachers I’ve ever had. I was entirely new to machine learning yet he was able to distill complicated concepts clearly and effectively. I felt everywhere else was teaching me just the surface, but Abubakar was able to tie the theory, practice and intuition together.

          Ali AbdallaEngineer at Hugging Face

          This course is for...

          Software engineers who want to build vision applications for prototyping or deployment without worrying too much about the underlying algorithmic details.

          Machine learning engineers who may already know the algorithms but are interested in building practical computer vision applications using the best open-source tools.

          Prerequisites

          Ability to write Python proficiently and work with documented libraries

          Experience using Jupyter notebooks or Google Colab notebooks recommended

          Basic understanding of machine learning (no experience in computer vision is required)

          Frequently Asked Questions

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