Microsoft DirectX Training Course.


DirectX12 

DirectX 12 is an expert API which builds on knowing the ins & outs of DirectX 11. DirectX 12 is an extremely low-level API designed for graphic experts who have a solid understanding of the architecture of modern GPU hardware, and can essentially write the DirectX 11 Runtime from scratch. Both DirectX 11 and DirectX 12 provide access to the same hardware features on Windows 10, but drive the hardware in different ways which can allow a well-optimized DirectX 12 engine to achieve much lower CPU overhead than in DirectX 11.

 

 

Course Outline

1. DIRECT 3D FOUNDATIONS

· Direct3D 12 Overview

· Textures Formats

· Depth Buffering

· Resources and Descriptors

· Multisampling in Direct3D

 

2. DirectX Graphics Infrastructure

· Checking Feature Support

· CPU/GPU Interaction

· The Command Queue and Command Lists

· CPU/GPU Synchronization

· Resource Transitions

· Multithreading with Commands

 

3. Initializing Direct3D

· Create the Device

· Create the Fence and Descriptor Sizes

· Create Command Queue and Command List

· Describe and Create the Swap Chain

· Create the Render Target View

· Create the Depth/Stencil Buffer and View

· Set the Viewport

 

4. Timing and Animation

· The Performance Timer

· Game Timer Class

· Time Elapsed Between Frames

· Total Time

· The Demo Application Framework

· D3DApp

 

5. The Rendering Pipeline

· The 3D Illusion

· Model Representation

· Basic Computer Color

· Color Operations

· Overview of the Rendering Pipeline

· The Input Assembler Stage

· Primitives with Adjacency

· Control Point Patch List

 

6. Drawing in Direct3D

· Vertices and Input Layouts

· Vertex Buffers

· Indices and Index Buffers

· Example Vertex Shader

· Input Layout Description and Input Signature Linking

· Example Pixel Shader

· Constant Buffers

7. Drawing in Direct 3D Part II

· Frame Resources

· Render Items

· Pass Constants

· Shape Geometry

· Generating a Cylinder Mesh

· Cylinder Side Geometry

· Cap Geometry

· Generating a Sphere Mesh

 

8. Lighting

· Light and Material Interaction

· Normal Vectors

· Computing Normal Vectors

· Transforming Normal Vectors

· Important Vectors in Lighting

· Lambert’s Cosine Law

· Diffuse Lighting

· Ambient Lighting

· Specular Lighting

 

9. Texturing

· Texture and Resource Recap

· Texture Coordinates

· Texture Data Sources

· DDS Overview

· Creating DDS Files

· Creating and Enabling a Texture

· Loading DDS Files

 

10. The Geometry Shader

· Programming Geometry Shaders

· Tree Billboards Demo

· Vertex Structure

· The HLSL File

· Alpha-to-Coverage

 

11. Normal Mapping

· Normal Maps

· Texture/Tangent Space

· Vertex Tangent Space

· Normal Mapping Shader Code

 

12. Windows Programming

· Events, the Message Queue, Messages, and the Message Loop

· Basic Windows Application

· Explaining the Basic Windows Application

· Includes, Global Variables, and Prototypes

· WinMain

· WNDCLASS and Registration

· Creating and Displaying the Window

· The Message Loop

· The Window Procedure

 

For Training Requirement Contact-

udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Mobile +94 0716092918

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Machine Learning and AI Workshop at Singapore.


Machine Learning

Recently I had conducted Machine Learning and AI workshop at New Horizons Singapore. Around 10 attended the workshop. Employees from various IT companies and organizations attended the event.

Machine Learning  

ML Workshop

Topics covered at the workshop-

https://uditha.wordpress.com/2017/11/15/big-data-and-machine-learning-workshop-sri-lanka/

 

Android Mobile Application Development Training at Celiveo Singapore.


Recently I did Android Mobile Application Development Training at Celiveo Singapore.

I covered Android Studio, web, json, maps, notifications and app publishing during the training.

Android Mobile Application Development Topics-

https://uditha.wordpress.com/2016/08/16/android-application-development-course-sri-lanka/

Software developers from Celivo attended 3 day training. It was held at Celiveo ,Asia-Pacific ,Jalan Bukit Merah ,Singapore.

https://www.celiveo.com/

 

For Training Requirement Contact-  udithamail@yahoo.com

udithait@gmail.com

training@bluechiptraining.biz

Mobile  +94 0716092918

MCSA: Machine Learning Certifications Sri Lanka.


Machine Learning  exam

Earning an MCSA: Machine Learning demonstrates knowledge relevant to Machine Learning, Data Scientists and Data Analysts positions, particularly those who process and analyze large data sets using R and use Azure cloud services to build and deploy intelligent solutions. It is the first step on your path to becoming a Data Management and Analytics Microsoft Certified Solutions Expert (MCSE).

Course 20774A:
Perform Cloud Data Science with Azure Machine Learning

Course Outline

Module 1: Introduction to Machine Learning

This module introduces machine learning and discussed how algorithms and languages are used.Lessons

  • What is machine learning?
  • Introduction to machine learning algorithms
  • Introduction to machine learning languages

Lab : Introduction to machine Learning

  • Sign up for Azure machine learning studio account
  • View a simple experiment from gallery
  • Evaluate an experiment

After completing this module, students will be able to:

  • Describe machine learning

  • Describe machine learning algorithms

  • Describe machine learning languages

Module 2: Introduction to Azure Machine Learning

Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.Lessons

  • Azure machine learning overview
  • Introduction to Azure machine learning studio
  • Developing and hosting Azure machine learning applications

Lab : Introduction to Azure machine learning

  • Explore the Azure machine learning studio workspace
  • Clone and run a simple experiment
  • Clone an experiment, make some simple changes, and run the experiment

After completing this module, students will be able to:

  • Describe Azure machine learning.

  • Use the Azure machine learning studio.

  • Describe the Azure machine learning platforms and environments.

Module 3: Managing Datasets

At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.Lessons

  • Categorizing your data
  • Importing data to Azure machine learning
  • Exploring and transforming data in Azure machine learning

Lab : Managing Datasets

  • Prepare Azure SQL database
  • Import data
  • Visualize data
  • Summarize data

After completing this module, students will be able to:

  • Understand the types of data they have.

  • Upload data from a number of different sources.

  • Explore the data that has been uploaded.

Module 4: Preparing Data for use with Azure Machine Learning

This module provides techniques to prepare datasets for use with Azure machine learning.Lessons

  • Data pre-processing
  • Handling incomplete datasets

Lab : Preparing data for use with Azure machine learning

  • Explore some data using Power BI
  • Clean the data

After completing this module, students will be able to:

  • Pre-process data to clean and normalize it.

  • Handle incomplete datasets.

Module 5: Using Feature Engineering and Selection

This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.Lessons

  • Using feature engineering
  • Using feature selection

Lab : Using feature engineering and selection

  • Prepare datasets
  • Use Join to Merge data

After completing this module, students will be able to:

  • Use feature engineering to manipulate data.

  • Use feature selection.

Module 6: Building Azure Machine Learning Models

This module describes how to use regression algorithms and neural networks with Azure machine learning.Lessons

  • Azure machine learning workflows
  • Scoring and evaluating models
  • Using regression algorithms
  • Using neural networks

Lab : Building Azure machine learning models

  • Using Azure machine learning studio modules for regression
  • Create and run a neural-network based application

After completing this module, students will be able to:

  • Describe machine learning workflows.

  • Explain scoring and evaluating models.

  • Describe regression algorithms.

  • Use a neural-network.

Module 7: Using Classification and Clustering with Azure machine learning models

This module describes how to use classification and clustering algorithms with Azure machine learning.Lessons

  • Using classification algorithms
  • Clustering techniques
  • Selecting algorithms

Lab : Using classification and clustering with Azure machine learning models

  • Using Azure machine learning studio modules for classification.
  • Add k-means section to an experiment
  • Add PCA for anomaly detection.
  • Evaluate the models

After completing this module, students will be able to:

  • Use classification algorithms.

  • Describe clustering techniques.

  • Select appropriate algorithms.

Module 8: Using R and Python with Azure Machine Learning

This module describes how to use R and Python with azure machine learning and choose when to use a particular language.Lessons

  • Using R
  • Using Python
  • Incorporating R and Python into Machine Learning experiments

Lab : Using R and Python with Azure machine learning

  • Exploring data using R
  • Analyzing data using Python

After completing this module, students will be able to:

  • Explain the key features and benefits of R.

  • Explain the key features and benefits of Python.

  • Use Jupyter notebooks.

  • Support R and Python.

Module 9: Initializing and Optimizing Machine Learning Models

This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.Lessons

  • Using hyper-parameters
  • Using multiple algorithms and models
  • Scoring and evaluating Models

Lab : Initializing and optimizing machine learning models

  • Using hyper-parameters

After completing this module, students will be able to:

  • Use hyper-parameters.

  • Use multiple algorithms and models to create ensembles.

  • Score and evaluate ensembles.

Module 10: Using Azure Machine Learning Models

This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.Lessons

  • Deploying and publishing models
  • Consuming Experiments

Lab : Using Azure machine learning models

  • Deploy machine learning models
  • Consume a published model

After completing this module, students will be able to:

  • Deploy and publish models.

  • Export data to a variety of targets.

Module 11: Using Cognitive Services

This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.Lessons

  • Cognitive services overview
  • Processing language
  • Processing images and video
  • Recommending products

Lab : Using Cognitive Services

  • Build a language application
  • Build a face detection application
  • Build a recommendation application

After completing this module, students will be able to:

  • Describe cognitive services.

  • Process text through an application.

  • Process images through an application.

  • Create a recommendation application.

Module 12: Using Machine Learning with HDInsight

This module describes how use HDInsight with Azure machine learning.Lessons

  • Introduction to HDInsight
  • HDInsight cluster types
  • HDInsight and machine learning models

Lab : Machine Learning with HDInsight

  • Provision an HDInsight cluster
  • Use the HDInsight cluster with MapReduce and Spark

After completing this module, students will be able to:

  • Describe the features and benefits of HDInsight.

  • Describe the different HDInsight cluster types.

  • Use HDInsight with machine learning models.

Module 13: Using R Services with Machine Learning

This module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.Lessons

  • R and R server overview
  • Using R server with machine learning
  • Using R with SQL Server

Lab : Using R services with machine learning

  • Deploy DSVM
  • Prepare a sample SQL Server database and configure SQL Server and R
  • Use a remote R session
  • Execute R scripts inside T-SQL statements

Microsoft EXAM – 70-774

Perform Cloud Data Science with Azure Machine Learning

https://www.microsoft.com/en-us/learning/exam-70-774.aspx

 

Second Course 20773A:
Analyzing Big Data with Microsoft R

https://www.microsoft.com/en-us/learning/course.aspx?cid=20773

Microsoft EXAM – 70-773

Analyzing Big Data with Microsoft R

https://www.microsoft.com/en-us/learning/exam-70-773.aspx

 

 

By-
Uditha Bandara is specializes in Microsoft AI Development technologies.  He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).  He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India. He has published several books,articles, tutorials, and demos on his Blog – https://uditha.wordpress.com

· Contact    +94 071-6092918

· udithamail@yahoo.com

· udithait@gmail.com

Blue Chip Training and Consulting Corporate Video.


 

Version 1.0 of the Blue Chip Training and Consulting Corporate Video

Big Data and Machine Learning Workshop at MAS Holdings Sri Lanka.


Recently I had conducted Big Data and Machine Learning workshop at MAS Bodyline Sri Lanka.  Around 30 attended the workshop. Employees from IT , Engineering and Production departments came to the workshop.

Topics covered at the workshop-

https://uditha.wordpress.com/2017/11/15/big-data-and-machine-learning-workshop-sri-lanka/

Xamarin Mobile Application Development Workshop at Singapore.


Recently I did Xamarin Mobile Application Development workshop at Singapore.

I covered following topics at the training.

  • Xamarin for Mobile Development
  • Architecting Solutions for Cross-Platform Development
  • Building Windows Universal and Phone Applications
  • Android Development with Xamarin
  • iOS Development with Xamarin
  • Cross-Platform Development with Xamarin.Forms
  • Navigation
  • Data-Binding
  • Using Web Services
  • Deployment

Software developers from following companies attended the training.