End to End Deep Learning for Computer Vision - Portland

As applied engineering just knows building models are not sufficient for production grade software, these roles focus on core principles, best practices, design patterns, and expertise with a framework and toolset, such as deploy models, and scale for your fast growing applications/services.
This two day immersive instructor-led training will teach everything you need to know to become a software engineer in computer vision and deep learning! You will learn:
  • Recognize problems can be solved with deep learning and Select right technique for problems
  • Select the right technique for the problems.
  • Master deep learning algorithms, models and computer vision tech
  • Master the most popular tools like numpy, Keras, Tensorflow, and openCV
  • Master google cloud machine learning pipelines
  • This training is packed with practical exercises and code labs. not only will you learn theory, but also get hands-on practice building your own models, tuning models, and serving models

    • Aug 6th Tue 9am-4:00pm PT
    • Aug 7th Wed 9am-4:00pm PT

    • Practical walkthroughs that present solutions to actual, real-world image classification problems and challenges
    • Hands-on tutorials (with lots of code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well
    • A no-nonsense teaching style that is guaranteed to cut through all the cruft and help you master deep learning for image understanding and visual recognition
    • End to end machine learning pipeline from building models to deploy and serve models
    • light breakfast, lunch, coffee breaks, and happy hour

    Check the content tab for full course outlines.

    Developers interested in Computer Vision and Deep Learning


  • Seattle: 6/15-16, 2019
  • Overall score: 4.6 / 5
  • Score: 5 (59%); 4 (41%)
  • "Personally I really loved the pace and the fact that you explained everything intuitively. In addition, it was apparent that students really loved it. " - UCLA Professor/CS 168
  • "good balance on tech concepts and hands-on code labs"
  • Pictures of Previous Courses

    Day 1 (9am -4:00pm)
    Module 1: Computer Vision Models
    • Neural Networks
      • Activation, loss function
      • classifier
      • Flattening, overfitting, dropout
      • code lab with Keras
    • Convolutional Neural Networks
      • resize, feature detection
      • filters, strides, pooling
      • VGG, ResNet
      • Batch normalization
      • code lab with Keras
    • Wide Convolutional Neural Networks
      • inception, ResNeXt
      • code lab
    • Advanced CNNs
      • pre-stems, DenseNet, MobileNet
      • code lab
    Module 2: Computer Vision Data Engineering
    • Data Collection & Assembly
      • best practice
      • unbalanced data
      • insufficient variance
      • dataset layout
    • Data Engineering
      • PIL
      • Normalization and Standardization
      • label encoding
      • data splitting
      • openCV
    • Data Augmentation
      • under-fitting
      • perspective
      • flipping
      • rotation
      • code lab with Keras
    • Data Curation
      • population distribution
      • sampling distribution
      • code lab with Keras
    Day 2 (9am -4:00pm)
    Module 3: Training Models
    • Training Preparation
      • splitting
      • shuffling
      • stratification
      • code lab
    • Hyperparameter Tuning
      • Epochs and Steps
      • Batch size
      • learning rate
      • optimizer
      • feeding
      • code lab
    • Training
      • pre-training
      • weight initialization
      • Grid Search
      • Gradient Descent
    • Pre-Built Models & Transfer Learning
    Module 4: Deployment and Production
    • Intro to TensorFlow 2.0, tf.Keras, tools (Colab, TensorBoard)
    • Deploy models with TensorFlow serving
    • Model training and deployment in the browser with TensorFlow JS
    • On-device ML: train a model from scratch, convert to TFLite and deploy to mobile and IoT
    • Demo of TFLite models on microcontroller and Coral Edge TPU
    12:00-1:00pm: Lunch break
    Andrew Ferlitsch

    Andrew is a machine learning expert at Google. he educates software engineers in machine learning and artificial intelligence. He is the creator of and oversees the development of the open source project Gap, which is a ML data engineering framework for computer vision. Andrew was formerly a principal research scientist at Sharp Corporation, working on imaging, energy, solar, teleconferencing, digital signage, and autonomous vehicles.
    • Start Date: ended
    • Venue: Portland, OR
    • Fee:
      $399 $199 USD
    • Students enrolled:13
    • Status: course ended
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