Computer Vision with TensorFlow 2.4

• Up your skills in Data Science, Machine Learning and Artificial Intelligence in days, not months!

• Master the rapidly evolving technologies in Transfer Learning, Image Classification, and Model Deployment!

Deploy and share your models between mobile phones with our unique, no-code tool PalletML (Free, 90-day Pro-Plan with our mini-course)

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A Hands-On Mini-Course

Computer Vision is being used in health, agriculture, transportation, the retail industries, and many others. Quickly learn the skills you need to enable technologies that are transformative in our rapidly changing world of data analytics. 

Quick TensorFlow's video lessons, practical projects, Colab notebooks and supplementary materials are designed to get you started building and deploying machine learning models in the real world as quickly as possible.

  • 18+
    On-Demand Video Lessons
  • 20+
    Downloadable Resources
  • Lifetime Access to Course Materials
  • $0
    PalletML Pro: deploy unlimited models free for 3 months

    Using modern machine learning platforms and frameworks

      Master the Machine Learning Workflow

      We'll guide you through an end-to-end ML workflow:

      1. Download and prepare training data from TensorFlow Datasets, or use your own custom images

      2. Build and train a powerful machine learning model for image classification

      3. Optimize your models for mobile devices with TensorFlow Lite

      4. Deploy your models to Android in minutes using PalletML, a no-code machine learning platform
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      Import training data from TensorFlow Datasets

      TensorFlow Datasets is a collection of datasets ready to use with the TensorFlow machine learning framework.

      We'll teach you best-practices for building performant data pipelines for your machine learning models. 

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      Build powerful models using Transfer Learning

      With Transfer Learning, you can use the "knowledge" from existing pre-trained models to empower your own custom models. 

      This course includes an in-depth discussion of various CNN architectures that you can use as a "base" for your models, including: MobileNet, EfficientNet, ResNet, and Inception

      We then demonstrate how you can acess these models through both the Keras API and TensorFlow Hub.

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      Apply techniques to optimize model performance

      Discover essential techniques that ensure your classification models perform well in production, starting with the best way to train-test split datasets and create validation subsets to avoid overfitting.

      Learn how to effectively apply optimization strategies like hyper-parameter tuning and fine-tuning to improve training and validation accuracy, while comparing various optimization algorithms including SGD and Adam.

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      Deploy your models to mobile, No code necessary

      When you create an image classification model, the first thing you want to do is try it in the real world! But integrating machine learning models into usable applications can be challenging.

      This course gives you early access to a new tool called PalletML , that lets you to deploy your image classification model to an Android app without any extra code.

      With Pallet you can test your model in the real world using pictures taken right from your phone's camera, and easily share your model with friends and colleages by just sending them a link.

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        Quickly deploy image classification models

        Just upload your TensorFlow Lite model + labels, and Pallet takes care of hosting and serving your model through a mobile app. If you host your models on Google Drive, you can even deploy them right from your phone.

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        Tune inference settings to support various classifiers

        Configure real-time preprocessing options for images before model inference for improved accuracy: resizing, inversion, and grayscaling operations are available out-of-the-box.

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        Check your model's performance

        Save predictions for later review, and filter through them using a custom interface designed for evaluating classification results.

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        Share your model with friends & colleagues

        Easily send your model to any number of friends and colleagues from within the Pallet app. A unique link is generated for each model that you deploy and opens directly to your model when activated.

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        Direct access to community models

        The ML community has deployed numerous classifiers - from flower recognizers to clothing detectors - all direcly accessible in the Pallet app.

      Ready to become a machine learning practitioner?

      Join our growing community today 🤝
      We're adding new material to the course all the time!
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      $ 19.99

      $ per user

      FOR EARLY ADOPTERS
      • ✓
        18+ on-demand video lessons
      • ✓
        20+ Colab notebooks
      • ✓
        10+ supplementary tutorials
      • ✓
        PalletML Pro
        3 free months to deploy unlimited models
      • ✓
        Lifetime access to the course

      Meet Your Instructor

      Nice to meet you and hope you enjoy the course!

      • Qtf instructor
        Dr. Anthony Teate
        Instructor

        Dr. Teate is a tenured Professor at James Madison University , and Director of the Data Science and Applied Machine Learning Laboratory.

        His current areas of research include development and applications of machine learning models for the design of nanoscale semiconducting materials; small scale autonomous vehicles; image recognition; edge computing; and deep learning.

      Runs on Unicorn Platform