Tag: TensorFlow

Course content

• Practical Reinforcement Learning – Agents and Environments
• Advanced Practical Reinforcement Learning
• Hands-On Deep Q-Learning
• Reinforcement Learning with TensorFlow & TRFL

Course content

• Introduction
• Why learn TenserFlow
• Setting up the TensorFlow Environment
• A1 and Machine Learning Concepts
• Applying the Machine Learning Workflow with TensorFlow
• Understanding Neural Networks
• Building and Training Your First Neural Network
• Monitoring and Improving Neural Network Performance
• Deploying Your Neural Network
• Assignment
• Conclusion and Final Words

Course content

• Introduction
• Basic python + Pandas + Plotting
• Machine Learning: Numpy + Scikit Learn
• Machine Learning: Classification + Time Series + Model Diagnostics
• Unsupervised Learning
• Natural Language Processing + Regularization
• Deep Learning
• Deep Learning (TensorFIow) – Convolutional Neural Nets
• Deep Learning: Recurrent Neural Nets
• Deep Learning: PyTorch Introduction
• Deep Learning: Transfer Learning with PyTorch Lightning
• Pixel Level Segmentation (Semantic Segmentation) with PyTorch
• Deep Learning: Transformers and BERT
• Bayesian Learning and probabilistic programming
• Model Deployment
• AWS Sagemaker (for Model Deployment)
• Final Thoughts

Course content

• Introduction
• Fundamentals of Reinforcement Learning
• Deep Learning Crash Course
• Human Level Control Through Deep Reinforcement Learning: From Paper to Code
• Deep Reinforcement Learning with Double Q Learning
• Dueling Network Architectures for Deep Reinforcement Learning
• Improving On Our Solutions
• Conclusion
• Bonus Lecture
• Tensorflow 2 Implementations
• Appendix

Course content

• Introduction and Outline
• Generative Modeling Review
• Variational Autoencoders
• Generative Adversarial Networks (GANs)
• Theano and Tensorflow Basics Review
• Setting Up Your Environment (FAQ by Student Request)
• Extra Help With Python Coding for Beginners (FAQ by Student Request)
• Effective Learning Strategies for Machine Learning (FAQ by Student Request)
• Appendix / FAQ Finale

Course content

• Introduction
• Deep Learning and TensorFlow Fundamentals
• Neural network regression with TensorFlow
• Neural network classification in TensorFlow
• Computer Vision and Convolutional Neural Networks in TensorFlow
• Transfer Learning in TensorFlow Part 1: Feature extraction
• Transfer Learning in TensorFlow Part 2: Fine tuning
• Transfer Learning with TensorFlow Part 3: Scaling Up
• Milestone Project 1: Food Vision Big
• NLP Fundamentals in TensorFlow
• Milestone Project 2: SkimLit
• Time Series fundamentals in TensorFIow + Milestone Project 3: BitPredict
• Passing the TensorFlow Developer Certificate Exam
• Where To Go From Here?
• Appendix: Machine Learning Primer
• Appendix: Machine Learning and Data Science Framework
• Appendix: Pandas for Data Analysis
• Appendix: NumPy
• BONUS SECTION

Duration

21 hours (usually 3 days including breaks)

Requirements

• Experience with Swift programming
• Experience with mobile application development
• An iOS device running v12 or higher

Audience

• Developers
• Data scientists who wish to develop AI-enabled mobile applications on iOS

Overview

TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.

By the end of this training, participants will be able to:

• Install and configure TensorFlow Lite.
• Understand the principles behind TensorFlow and machine learning on mobile devices.
• Load TensorFlow Models onto an iOS device.
• Run an iOS application capable of detecting and classifying an object captured through the device’s camera.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

• Tensorflow vs Tensorflow Lite

Overview of TensorFlow Lite Features and Workflow

• Recap of machine learning and deep learning concepts
• How on-device low-latency inference is achieved
• End-to-end model building and deployment

Preparing the Development Environment

• Starting a Swift project
• Adding TensorFlow to the project

Capturing an Image with a Device Camera

• How camera input is captured
• Overview of classes and methods
• Running inference on a frame (performing image classification)

Creating an App for Object Detection

• Selecting a TensorFlow Model
• Converting the TensorFlow Model
• Loading the TensorFlow Model onto a Mobile Device
• Loading a Pre-trained TensorFlow Model

Creating an App for Image Classification

• Selecting a TensorFlow Model
• Converting the TensorFlow Model
• Loading the TensorFlow Model onto a Mobile Device
• Loading a Pre-trained TensorFlow Model

Customizing the Model and Data

• Pre-processing a dataset
• Setting the hyperparameters

Optimizing the TensorFlow Model

• Measuring performance against a benchmark
• Measuring accuracy
• Retraining a TensorFlow model

Exploring Alternative Models

• Choosing a different model
• Training a model to recognize new classes (transfer learning)
• Obtaining training images for new labels

Deploying the AI Enabled iOS App

• Performing image classification in the field

Troubleshooting

Summary and Conclusion

Duration

21 hours (usually 3 days including breaks)

Requirements

• C or C++ programming experience
• A basic understanding of Python
• A general understanding of embedded systems

Audience

• Developers
• Programmers
• Data scientists with an interest in embedded systems development

Overview

TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

• Microcontroller vs Microprocessor
• Microcontrollers designed for machine learning tasks

Overview of TensorFlow Lite Features

• On-device machine learning inference
• Solving network latency
• Solving power constraints
• Preserving privacy

Constraints of a Microcontroller

• Energy consumption and size
• Processing power, memory, and storage
• Limited operations

Getting Started

• Preparing the development environment
• Running a simple Hello World on the Microcontroller

Creating an Audio Detection System

• Obtaining a TensorFlow Model
• Converting the Model to a TensorFlow Lite FlatBuffer

Serializing the Code

• Converting the FlatBuffer to a C byte array

Working with Microcontroller’ss C++ Libraries

• Coding the microcontroller
• Collecting data
• Running inference on the controller

Verifying the Results

• Running a unit test to see the end-to-end workflow

Creating an Image Detection System

• Classifying physical objects from image data
• Creating TensorFlow model from scratch

Deploying an AI-enabled Device

• Running inference on a microcontroller in the field

Troubleshooting

Summary and Conclusion

Duration

21 hours (usually 3 days including breaks)

Requirements

• An understanding of deep learning concepts
• Python programming experience
• A device running embedded Linux (Raspberry Pi, Coral device, etc.)

Audience

• Developers
• Data scientists with an interest in embedded systems

Overview

TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.

By the end of this training, participants will be able to:

• Install and configure Tensorflow Lite on an embedded device.
• Understand the concepts and components underlying TensorFlow Lite.
• Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
• Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
• Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

• TensforFlow Lite’s game changing role in embedded systems and IoT

Overview of TensorFlow Lite Features and Operations

• Addressing limited device resources
• Default and expanded operations

Setting up TensorFlow Lite

• Installing the TensorFlow Lite interpreter
• Installing other TensorFlow packages
• Working from the command line vs Python API

Choosing a Model to Run on a Device

• Overview of pre-trained models: image classification, object detection, smart reply, pose estimation, segmentation
• Choosing a model from TensorFlow Hub or other source

Customizing a Pre-trained Model

• How transfer learning works
• Retraining an image classification model

Converting a Model

• Understanding the TensorFlow Lite format (size, speed, optimizations, etc.)
• Converting a model to the TensorFlow Lite format

Running a Prediction Model

• Understanding how the model, interpreter, input data work together
• Calling the interpreter from a device
• Running data through the model to obtain predictions

Accelerating Model Operations

• Understanding on-board acceleration, GPUs, etc.
• Configuring Delegates to accelerate operations

Adding Model Operations

• Using TensorFlow Select to add operations to a model.
• Building a custom version of the interpreter
• Using Custom operators to write or port new operations

Optimizing the Model

• Understanding the balance of performance, model size, and accuracy
• Using the Model Optimization Toolkit to optimize the size and performance of a model
• Post-training quantization

Troubleshooting

Summary and Conclusion

Duration

21 hours (usually 3 days including breaks)

Requirements

• An understanding of DevOps concepts
• Machine learning development experience
• Python programming experience

Audience

• Data scientists
• ML engineers
• Operation engineers

Overview

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.

By the end of this training, participants will be able to:

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

Setting up TensorFlow Extended (TFX)

Overview of TFX Features and Architecture

Understanding Pipelines and Components

Working with TFX Components

Ingesting Data

Validating Data

Tranforming a Data Set

Analyzing a Model

Feature Engineering

Training a Model

Orchestrating a TFX Pipeline

Managing Meta Data for ML Pipelines

Model Versioning with TensorFlow Serving

Deploying a Model to Production

Troubleshooting

Summary and Conclusion