Deep Learning for Computer Vision (Batch #4)

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COURSE OBJECTIVES:
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 3 weeks immersive instructor-led course 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
  • 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 deep learning pipelines
  • This course 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

  • 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 deep learning pipeline from building models to deploy and serve models
  • COURSE SCHEDULE:
    • Session 1: August 4, 5:30pm-7:30pm PST (US Pacific Time)
    • Session 2: August 6, 5:30pm-7:30pm PST
    • Session 3: August 11, 5:30pm-7:30pm PST
    • Session 4: August 13, 5:30pm-7:30pm PST
    • Session 5: August 18, 5:30pm-7:30pm PST
    • Session 6: August 20, 5:30pm-7:30pm PST

    COURSE INCLUDE:
    • 3 weeks / 6 sessions / 12 hours
    • 6 lectures / 8 code labs
    • Capstone project, Github portfolio
    • Live Sessions, Real time interaction
    • Slack support after class and homeworks
    • Life time access to course materials

    COURSE CONTENT:
    Check the content tab for full course outlines.

    WHO SHOULD LEARN:
    Developers interested in Computer Vision and Deep Learning

    PREREQUISITE:
  • Basic familiarity with python
  • DIFFICULTY LEVEL
    Beginner~Intermediate
    Est. time spend per week: 4 hours live class (required) + 4 hours homework (required) + 4 hours projects (bonus, optional).

    FREE TRIAL
    Full refund upon request before the first session ends (Aug 4th, 2020 8:00pm PT). 5% transaction fee is not refundable.

    SESSION REPLAY
    If missed live sessions, you can watch recordings any time, along with interactive learning tools, slides, course notes
    Students have life time access to course materials

    BENEFITS
  • Earn Certificate of Completion
  • Scholarship is available, contact us for details
  • COURSE HISTORY (PREVIOUS BATCHES)
  • Batch #3: Feb 15~16, 2020
  • Batch #2: Aug 22~23, 2019
  • Batch #1: Jun 15~16, 2019
  • Module 1: Computer Vision Models
    • Deep Neural Networks
      • Input Layer
      • Deep Neural Networks (DNN)
      • Feed Forward
      • DNN Binary Classifier
      • DNN Multi-Class Classifier
      • DNN Multi-Label Multi-Class Classifier
      • Code Lab
    • Convolutional and ResNet Neural Networks
      • Convolutional Neural Networks
      • CNN Classifier
      • Basic CNN
      • VGG/li>
      • Residual Networks (ResNet)
      • ResNet50/li>
      • Code lab
    Module 2: Computer Vision Data Engineering
    • Training Foundation
      • Feed Forward and Backward Propagation
      • Dataset Splitting
      • Normalization
      • Validation & Overfitting
      • Convergence
      • Checkpointing & Earlystopping
      • Hyperparameters
      • Invariance
      • Raw (Disk) Datasets
      • Model Save/Restore
      • Code lab
    • Procedural Design Pattern
      • Procedural Design Pattern
      • Stem Component
      • Learner Component
      • Task Component
      • Code lab
    Module 3: Training Models
    • Wide Convolutional Neural Networks
      • Inception
      • ResNeXt
      • Wide Residual Network (WRN)
      • code lab
    • Alternative Connectivity Patterns
      • Densely Connected CNN (DenseNet)
      • SE-Net
      • Xception
      • code lab
    • Mobile Convolutional Neural Networks
      • MobileNet
      • SqueezeNet
      • ShuffleNet
      • Quantization
      • code lab
    Module 4: AutoEncoders and Optimization
    • AutoEncoders
      • Deep Neural Network AutoEncoder
      • Convolutional AutoEncoder
      • Sparse AutoEncoder
      • Denoising AutoEncoder
      • Super Resolution
      • Pre-text Tasks
      • code lab
    • Hyperparameter Tuning
      • Weight Initialization
      • Warmup (Numerical Stability)
      • Hyperparameter Search
      • Learning Rate Scheduler
      • Regularization
      • Code lab
    Module 5: Transfer Learning
    • Transfer Learning
      • Tf.Keras Pre-Built Models
      • TF.Hub Pre-Built Models
      • Transfer Learning
      • code lab
    • Data Distributions
    • Dataset Curation
    • Data Pipeline
      • Data Formats & Storage
      • Data Feeding
      • Data Preprocessing
      • Data Augmentation
      • Code lab
    Module 6: Deployment and Production
    • Model Feeding
    • Training Schedulers
    • Model Evaluations
    • Serving
    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: Online (zoom)
    • Fee:
      $249 $249 USD
    • Students enrolled:36
    • Status: course ended
    • Course Preview:
    • Any questions? Contact Us