Deep Learning for Developers (Batch #6)

In this course you will learn the fundamentals of Deep Learning primarily through a series of hands on exercises guided by the instructor. Students will learn about the foundational underpinnings of machine learning and deep learning as well as how to put that knowledge to the test with practical exercises.
The course takes unique project focused approach to teach you deep learning by building deep learning models. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code, and building neural networks.
The course will balance learning theory and working on projects
Students who take this course will be able to:
  • Identify and frame problems that can be solved by deep learning
  • Choose the right techniques to the problems
  • Understand key deep learning concepts and how deep learning models work
  • Identify and fix problems with messy datasets
  • Build deep neural nets for classification and regression using the Keras framework
  • Build convolutional neural networks for image classification, object localization and segmentation using the Keras
  • Discuss the parts and processes involved in building large scale deep learning applications
    • Session 1: August 24, 5pm-7pm PST (US Pacific GMT-7)
    • Session 2: August 26, 5pm-7pm PST
    • Session 3: August 31, 5pm-7pm PST
    • Session 4: September 2, 5pm-7pm PST
    • Skip Sep 7th due to US holiday
    • Session 5: September 9, 5pm-7pm PST
    • Session 6: September 14, 5pm-7pm PST
    • Session 7: September 16, 5pm-7pm PST
    • Session 8: September 21, 5pm-7pm PST

    • 16 hours/ 8 sessions
    • 10 lectures / 16 hands-on code labs
    • Live Sessions, Real time interaction
    • Slack support in and after class
    • Capstone project, Github portfolio
    • Life time access to course materials

    Check the content tab for full course outlines.

    Developers, data scientists, students who want to get started on building deep learning projects or applications.

  • Familiarity with Python, or willingness to learn it quickly
  • Basic familiarity with statistics and probability theory is a plus, but not required
  • Comfort with the command line a plus
  • Beginner~Intermediate
  • Est. time spend per week: 4 hours live class (required) + 4 hours homework (required) + 4 hours projects (bonus, optional).
    Full refund upon request before the first session ends (August 24th, 2020 7:00pm PT). 5% transaction fee is not refundable.

    If miss the live sessions, you can watch recorded sessions any time, along with interactive learning tools, slides, course notes

  • Certificate of completing course
  • Scholarship is available, contact us for applications
  • Batch #5: Apr 28, 2019 ~ May 21, 2020
  • Batch #4: Jan 7, 2019 ~ Jan 30, 2020
  • Batch #3: Oct 15, 2019 ~ Nov 7, 2019
  • Batch #2: Aug 20, 2019 ~ Aug 21, 2019
  • Batch #1: May 14, 2019 ~ Jun 6, 2019
  • Module 1: Machine Learning & Neural Network Fundamentals
    • Deep learning applications, key concepts and terminology
    • Neural network architecture and theory
    • Code lab 1: Building and training a neural network.
    • Code lab 2: Exploring different network architectures

    Module 2: Deep Neural Networks in Detail
    • Identify tasks that are well and ill suited to deep learning
    • Underfitting and overfitting, Regularization tactics
    • Feature engineering, Optimizers and training parameters
    • Code Lab 3: Exploring common activation functions
    • Code lab 4: Exploring common loss functions
    • Code lab 5: Exploring common optimizers
    • Code lab 6: Underfitting, overfitting, and regularization tactics

    Module 3: Data Cleaning and Preprocessing
    • Define and identify common problems with training data
    • Describe and apply tactics for improving data quality and improve network performance
    • Evaluate the appropriateness of applying said tactics.
    • Code Lab 7: Clean up a messy dataset
    • Code Lab 8: Improve a dataset with synthetic data and augmentation

    Module 4: Convolutional neural networks for classification and segmentation
    • Identify tasks for which CNNs are well suited
    • Compare and contrast classification with segmentation
    • Build CNNs from scratch using Keras and evaluate their performance
    • Code lab 9: Build a CNN from scratch.
    • Code lab 10: Import and use well known pre-trained CNN architectures.

    Module 5: Convolutional Neural Networks and Transfer Learning
    • Transfer learning and identify examples of situations where it might help
    • Import well known CNN architectures and leverage transfer learning using Keras
    • Code Lab 11: Transfer learning with the Cifar100 dataset
    • Code Lab 12: A complete CNN pipeline with transfer learning

    Module 6: Object Localization and Image Segmentation
    • Define object localization and image segmentation and what they’re used for
    • Code lab 13: implement object localization
    • Code lab 14: implement image segmentation

    Module 7: Generative Adversarial Networks (GAN)
    • Define GANs and what they’re used for
    • Code lab 15: implement a GAN in Keras

    Module 8: Deploying Models On a Web Server
    • Code Lab 16: Deploy a CNN model with a Python a server
    • Build a simple web server in Python
    • Allow users to get classifications from the model via the webserver
    Tyler Bettilyon

    Tyler is an educator, technologist, programmer, and all around curious human. He holds a bachelor’s degree in computer science and completed his MBA by counter example in San Francisco’s startup scene. Looking out from inside the Bay Area Bubble he realized that the world is not prepared for the future that technology is bringing. He is now focused on technology education, outreach, and policy
    • Start Date: Aug 24, 17:00PST | Mon,Wed
    • Venue: Online
    • Fee:
      $299 $199 USD
    • Max/Avail. Seats 30/21
    • Status: start soon
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