14 hours (usually 2 days including breaks)
Audience
OpenCV is a library of programming functions for deciphering images with computer algorithms. OpenCV 4 is the latest OpenCV release and it provides optimized modularity, updated algorithms, and more. With OpenCV 4 and Python, users will be able to view, load, and classify images and videos for advanced image recognition.
This instructor-led, live training (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
Format of the Course
Course Customization Options
Introduction
What is AI
Deep Learning
Preparing the Development Environment
OpenCV 4 Quickstart
Deep Learning Computer Vision
Neural Networks
Convolutional Neural Networks
Classifiers
Summary and Conclusion
7 hours (usually 1 day including breaks)
Audience
OpenAI Gym is an open-source interface used to create, develop, and compare reinforcement learning (RL) tasks and algorithms. Gym provides a wide set of environment libraries to run reinforcement learning tasks with ease.
This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to install, configure, customize, and implement OpenAI Gym to quickly develop reinforcement learning algorithms.
By the end of this training, participants will be able to build, develop, execute, and test reinforcement learning algorithms to optimize tasks and achieve maximum results.
Format of the Course
Course Customization Options
Introduction
Overview of OpenAI Gym Features and Architecture
Learning About OpenAI Gym Basics
Exploring the OpenAI Gym Library
Installing OpenAI Gym and Dependencies
Building Gym Directly from Source
Working with OpenAI Gym Environments
Creating Your Own Environments
Executing Environment Functions and Tasks
Working with Agents, Actions, Observations, and Rewards
Understanding Space Types in Environments
Using OpenAI Gym’s Registry Function
Testing the Algorithms and Environments
Troubleshooting
Summary and Conclusion
7 hours (usually 1 day including breaks)
Audience
Horovod is an open source software framework, designed for processing fast and efficient distributed deep learning models using TensorFlow, Keras, PyTorch, and Apache MXNet. It can scale up a single-GPU training script to run on multiple GPUs or hosts with minimal code changes.
This instructor-led, live training (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.
By the end of this training, participants will be able to:
Format of the Course
Course Customization Options
Introduction
Installing and Configuring Horovod
Running Distributed Training
Optimizing Distributed Training Processes
Troubleshooting
Summary and Conclusion
35 hours (usually 5 days including breaks)
Audience
An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.
By the end of this training, participants will be able to:
Format of the Course
Course Customization Options
Introduction
Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application
Setting up OpenVINO
Overview of OpenVINO Toolkit and its Components
Understanding Deep Learning Acceleration GPU and FPGA
Writing Software That Targets FPGA
Converting a Model Format for an Inference Engine
Mapping Network Topologies onto FPGA Architecture
Using an Acceleration Stack to Enable an FPGA Cluster
Setting up an Application to Discover an FPGA Accelerator
Deploying the Application for Real World Image Recognition
Troubleshooting
Summary and Conclusion
21 hours (usually 3 days including breaks)
Audience
MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.
By the end of this training, participants will be able to:
Format of the Course
Course Customization Options
Introduction
Deep Learning Principles and the Deep Learning Ecosystem
Overview of Apache MXNet Features and Architecture
Setup
Working with Data
Developing a Deep Learning Model
Deploying the Model
MXNet Security Best Practices
Troubleshooting
Summary and Conclusion
21 hours (usually 3 days including breaks)
Audience
Deep learning is a subfield of machine learning. It uses methods based on learning data representations and structures such as neural networks.
Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.
This instructor-led, live training (online or onsite) is aimed at developers who wish to build a self-driving car (autonomous vehicle) using deep learning techniques.
By the end of this training, participants will be able to:
Format of the Course
Course Customization Options
Introduction
Setting up the Development Environment
Creating a Project
Configuring the Simulator
Preparing the Data Sets
Overview of Python Deep Learning Libraries
Applying Computer Vision Techniques to Track Lanes
Training Perceptron-Based Neural Networks to Detect Other Vehicles
Implementing Convolutional Neural Networks to Predict Steering Angle and Speed
Training a Deep Learning Model to Classify Traffic Signs
Using Polynomial Regression to Improve Predictive Accuracy
Testing the Self Driving Car
Troubleshooting
Summary and Conclusion
21 hours (usually 3 days including breaks)
Audience
Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.
This instructor-led, live training (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.
By the end of this training, participants will be able to:
Format of the Course
Course Customization Options
Introduction
Overview of Neural Networks
Understanding Convolutional Networks
Setting up Keras
Overview of Keras Features and Architecture
Overview of Keras Syntax
Understanding How a Keras Model Organize Layers
Configuring the Keras Backend (TensorFlow or Theano)
Implementing an Unsupervised Learning Model
Analyzing Images with a Convolutional Neural Network (CNN)
Preprocessing Data
Training the Model
Training on CPU vs GPU vs TPU
Evaluating the Model
Using a Pre-trained Deep Learning Model
Setting up a Recurrent Neural Network (RNN)
Debugging the Model
Saving the Model
Deploying the Model
Monitoring a Keras Model with TensorBoard
Troubleshooting
Summary and Conclusion