Title:Serverless Machine Learning with TensorFlow 2.0
Date:9:00am-4:00pm, Feb. 14th, Friday
Instructor:Amy Unruh, Google
Course Outline: This workshop provides a hands-on introduction to designing and building machine learning models on structured data on Google Cloud Platform. You will learn machine learning (ML) concepts and how to implement them using both BigQuery Machine Learning and TensorFlow/Keras. You will apply the lessons to a large out-of-memory dataset and develop hands-on skills in developing, evaluating, and productionizing ML models.

The course includes 7 modules. Each of module is about 45 minutes and consists of 15 min lecture and 30 min codelab:

  • Module 0: Intro to machine learning and identify problems can be solved by ML
  • Module 1: Intro to BigQuery, TensorFlow and Cloud AI Platform Notebooks
  • Module 2: Use BigQuery ML to build our first ML models for taxifare prediction
  • Module 3: Learn how to read large datasets using TensorFlow.
  • Module 4: Build a DNN model using Keras.
  • Module 5: Improve the basic models through feature engineering
  • Module 6: Carry out equivalent feature engineering in Keras
  • Module 7: Productionize the models

12pm-1pm: lunch and networking break

Who should learn:Developers, Data Scientists who are working on machine learning, deep learning.
Level:Beginner to Intermediate
Prerequsite:The following is prefered but not required.
  • Experience using Python
  • Basic proficiency with a common query language such as SQL
  • A working knowledge of data modeling and extract, transform, load activities
  • Basic familiarity with machine learning and/or statistics
Back to Home
Title:Deep Learning for NLP
Date:9:00am-12:00pm
Instructor:
Outline:
Module 1: Overview of NLP and Deep Learning
  • Vector representations of Words: word2vec
  • Recurrent neural networks
  • LSTM&GRU
  • BERT
Module 2: NLP Pipeline
  • Data preprocessing
  • Text embedding
  • Text Classification
Module 3: Code Labs
  • Text Classification
  • Sentiment analysis
Who should learn:
Difficulity Level:
Prerequsite:
Back to Home
Title:Deep Learning for Computer Vision
Date:9:00am-4:00pm, Feb. 15th~16th (Sat, Sun)
Instructor:Andrew Ferlitsch, Google Cloud AI
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
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 2-day immersive instructor-led training will teach everything you need to know to become a software engineer in deep learning, computer vision! 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
  • 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

    Course Include:
    • 2 days/12 hours instructor-led trainings
    • 10 modules of lectures
    • 8 hands-on code labs
    • Code labs guidance
    • Training materials
    Course Outline:
    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
    Who should learn:Developers, Data Scientists who are working on machine learning, deep learning.
    Level:Beginner to Intermediate
    Prerequsite:
    Back to Home