Machine Learning

n.zendah — Tue, 21 Dec 2021 07:40:35 +0000

Machine Learning n.zendah Tue, 12/21/2021 - 10:40

Machine Learning For .NET Developers (ML.NET)

Overview of ML.Net

It’s a Free software machine learning library for the C# and F# programming languages.
Allows developers to easily build, train, deploy, and consume custom models in their .NET applications.
Doesn't require prior expertise in developing machine learning models or experience with other programming languages like Python or R.
Provides data loading from files and databases, enables data transformations, and includes many ML algorithms.
With ML.NET, you can train models for a variety of scenarios, like Image classification, Stock prediction, sentiment analysis…. etc.

ML.Net Model Builder

ML.Net has intuitive graphical Visual Studio extension to build, train, and deploy custom machine learning models.

Fig. 1. ML.NET Model Builder Interface

Model Builder uses automated machine learning (AutoML) to explore different machine learning algorithms and settings to help you find the one that best suits your scenario.
Model Builder generates the code to add the model to your .NET application.

NOTE: You don't need machine learning expertise to use Model Builder. All you need is some data, and a problem to solve!

Steps to build a Machine Learning model using ML.NET model Builder:

Select Scenario: Choose the scenario type that, you expect, will solve the problem using your data. Each scenario maps to machine learning task, such as:

Binary or Multi Classification
Regression
Clustering
Forecasting
Recommendation

Environment: Select the training environment that you want to perform training on it.
Locally on your machine, or remotely on the cloud.

Fig. 2. Training Environment.

NOTE: Not all scenarios support remotely training!

Get Data:
Provide the dataset that will be used to train, evaluate, and choose the best model for your scenario.
The supported data formats are: (.csv, .txt, .tsv, .jpg, .png, and SQL database).
Once you provide your dataset, you should determine (label and features) to perform training.

Fig. 3. Preparing Dataset for Training.

Train:
You should specify a time to train (in second).

* Longer training period allows to explore more models.

Exploring multiple models to find you the best performing model.
During training, you are not required to enter any input or tuning.

Fig. 4. Training Setup.

Evaluate:
Measuring the trained model accuracy by predicting the labels for unseen data (testing data) and compare the predicted labels with the original labels.
The dataset will be splitted into (80% training set) and (20% testing set).

Code:
Generates the model file and code that you can add to your .NET application.
Model Builder will create the following projects:

ML.ConsoleApp: Console applications to make predictions from your model.
ML.Model: Model Input, Model Output, Model itself and API.

First ML.NET Project (EXAMPLE):

Let's build a machine learning model with ML.NET using Visual Studio model builder!

Text classification project

To predict if the text is POSITIVE or NEGATIVE sentiment.

Dataset link: Amazon comments labelled dataset

Installation and Configuration:

Install visual studio 2019.
Open visual studio installer.
Choose visual studio community 2019, and then select (modify).
Select (individual components), add (ML.NET Model Builder).
Install (.NET framework).
Now open visual studio, start with (Continue without code).
Go to (tools >> options >> preview features >> enable ML.Net model builder).

Create New Project

Search for a template (Windows Forms App (.NET Framework)).

Select (Next), give a name for your project and then (Create).
Once your project is created:

Right click on project name >> Add >> Machine Learning.

Now, pick a scenario from Model Builder:

Choose (Text Classification).

Select Training Environment:

Train locally on machine.

Click on (Data) for next step.
Select File as the input data source type.

Select the dataset that downloaded from the link, as .txt:

Now, determine the (label and features) of the dataset to perform training.

Label: col1, Features: col0

Click on (Train) for next step.
Now, set the appropriate time for train and start training .

Once the training process is completed, the training results will be shown.

Click on (Evaluate) for next step.
Now, test and try the model (make prediction with new input).

Give the model new input sample. Ex: “It’s a good day”.
Once you click on (Predict), the model prediction result will be shown.
The prediction results will be whether (1 or 0).
- 1: The input you have entered is a POSITIVE sentiment.
- 0: The input you have entered is a NEGATIVE sentiment.

Click on (Code) for next step.
Now, once you click on (Add Projects):

All training and model code and files are added to your .NET project.
Two projects will be added:

ML.ConsoleApp
ML.Model

Working on simple .NET project based on the trained and saved machine learning model.

Firstly, install “ML.NET” package.
- Right click on project name >> Manage NuGet Packages >> Select (Browse) >> Search for (Microsoft.ML) and then install it.

Change the (Platform Target).

Right click on project name >> choose (Properties) >> select (Build) >> Change the platform target into (X64) >> then select (Rebuild) the project.

Copy the following selected files from ML.Model and paste them into your project.

Create the following form.

Change the (namespace) for the following files and name them as the project name.

ModelInput
ModelOutput
ConsumeModel

Change the (Active Solution Platform).

Click on (Build) >> Select (Configuration Manager) >> Change the active solution platform into (X64).

Now, create an event and write a code for (Predict) button.

The code performs the following:

TextBox1 will take the input sample.
- The text you want to predict if it is positive or negative.
The result of prediction will display in TextBox2.
- If the result of prediction is (0):
- TextBox3 will displays (NEGATIVE Comment).
- TextBox4 will displays the score of prediction.

If the result of prediction is (1):
- TextBox3 will displays (POSITIVE Comment).
- TextBox4 will displays the score of prediction.

Once you start running this project, you can make predictions on any “new data sample” using the trained and saved Machine Learning model.
Prediction results examples:

POSITIVE Sentiment:

NEGATIVE Sentiment:

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Introduction to 3D Object Detection Using Deep Learning From Lidar Point Clouds

zrik — Wed, 14 Apr 2021 12:31:17 +0000

Introduction to 3D Object Detection Using Deep Learning From Lidar Point Clouds zrik Wed, 04/14/2021 - 15:31

Introduction

In this blog post, we will briefly review what the LIDAR sensor is, how it works, the data shape and format of LIDAR point clouds, the techniques used in 3d-Object detection that are originally tailored from 2d-object detection networks. While the mostly used dataset is the older KITTI, we will review one of the newer and richer benchmarking dataset for this task (Pandaset).

What Is A Lidar Sensor?

Lidar (light detection and ranging) is a method for measuring distances by sending a laser pulse to a target or a scene and measuring the reflections with a sensor. Lidar sensors are used to create point clouds. A point cloud is a concatenation of all points captured during a single sweep of the LIDAR sensor. It represents a 3D shape or feature. Each point has its own set of X, Y and Z coordinates and in some cases additional attributes like the intensity of the reflection.

How Does It Work?

Active sensors with their own laser illumination source are used in LIDAR. The energy source strikes the surfaces, and sensors detect and measure the reflected energy. Distance to the object is determined by recording the time between transmitted and received (reflected) pulses and by using the speed of light to calculate the distance traveled.

The GIF roughly demonstrates how LiDAR works. Basically, laser beams are shot in all directions by a laser with a specific step or resolution that differs from one LIDAR to another. The laser beams reflect off the objects in their path and the reflected beams are collected by a sensor. 3D maps are created using the information from these sensor

Features Collected

Data consists of rows and columns of pixels with ample "depth" and "intensity" to create 3D landscape models. Each pixel records the time it takes each laser pulse to reach the target and return to the sensor, as well as the depth, position, and reflective intensity of the object that the laser pulse is contacting. When all those data points are collected, they are called a point cloud and they appear as the below image. The yellow boxes represent vehicles bounding boxes.

Applications

LIDAR is used in many applications, mainly in autonomous driving cars to detect objects in order to navigate safely through environments. LIDAR is widely used in video games to reproduce environments from 3D point clouds, resulting in very accurate replicated environments, like replicating a whole town inside a video game. It is also used in Agriculture, Archaeology, Mining and many more.

Data Shape And Extensions

The data is usually in 3 dimensions, representing x, y and z of the reflected laser beams.
Additional dimensions could also be present, representing the intensity of reflected light which could help in identifying specific materials and points clustering for single objects.

3D object detection methods

There are two types of 3D object detection methods: region proposal based and single shot methods. The region proposal bases methods work by proposing several possible regions containing objects, and then extract per region features. These features are then used to attempt object detection. These methods could be divided into:

Multi-view based: Used multiple data sources like LiDAR front view, Bird’s Eye View (BEV), and camera images.
Segmentation-based: uses semantic segmentation techniques to remove most background points, and then generate an amount of high-quality proposals on foreground points.
frustum-based methods: uses existing 2D object detectors to generate 2D candidate regions of objects and then extract a 3D frustum proposal for each 2D candidate region.

Single shot methods directly predict class probabilities and regress 3D bounding boxes of objects using a single-stage network. They do not require region proposal generation and post-processing. They generally run at higher speed than Region proposals methods. These methods could be divided into:

BEV-based Methods: These methods mainly take bird-eye view representation as their input. The point cloud is used to create a 2D image representation as if taken from a bird’s view. Normal 2D convolutional layers are then used to attempt object detection.
Discretization-based Methods: These methods transform a point cloud into a standard discrete representation, then use CNN to predict object categories and 3D boxes. Basically the point cloud is transformed into a set of features that are then fed into a CNN network to attempt object detection.
Point-based Method: These methods take raw point clouds as inputs directly.

Dataset: Pandaset

This dataset is released by SCALE AI and Hesai The dataset contains 48,000+ camera images, 16,000+ LiDAR sweeps with ~175,000 point per point cloud, 104 scenes of 8s each, 3D bounding boxes for 28 object classes and a rich set of per object attributes related to activity, visibility, pose and location. The sensor suite used during collection were 1x mechanical spinning LiDAR, 1x forward-facing LiDAR, 6x cameras and On-board GPS/IMU.

Panar64: 64-Channel Mechanical LiDAR

PandarGT: Solid-State LiDAR

References

Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu and M. Bennamoun, "Deep Learning for 3D Point Clouds: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2020.3005434.
PandaSet by Hesai and Scale AI (https://pandaset.org/)

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Introduction to Artificial Intelligence

zrik — Wed, 06 Jan 2021 14:56:23 +0000

Introduction to Artificial Intelligence zrik Wed, 01/06/2021 - 17:56

For sure you have heard the term “Artificial Intelligence” (AI) here or there, very repeatedly. They said it solves many problems, might take the human position in the future.
For what they are speaking about it day and night? for what reason we need it? what is it? How did it start?

Why do we need AI?

In this world we have lots of problems that we wish from the bottom of our hearts that it could be solved. Let’s concentrate on some of them here: Investigating the medical errors in USA shows that 5% of adults are misdiagnosed yearly. This misdiagnosis is the reason for 10% of patients’ deaths. Diagnosis is hard, experienced doctors are not always available and affordable, and will not forget the human errors. Moreover, doctors have their reasons to make errors with high pressure daily tasks.

Going to another disaster happening in the world, buildings collapsing. Please have a look on this chart that shows the number of deaths cause by structural collapses in India during the interval 2003-2012, it exceeds 2,000 persons. Based on the five reasons mentioned in this article, we can tell that the lack of experience, human errors, and cheating are the main reasons for building collapses around the world.

We all know about the high rate of deaths resulted by car accidents yearly, and very clearly 94% of car accidents are caused by human choices or errors.

Yes, the dream came true with AI. With AI we can get personalized-healthcare and person-centered medicine. AI will not only be capable of solving building collapses problem but also will make it possible that each one of us could afford one of Zaha Hadid designs. Self-driving cars usage in USA decreased the accidents count by 90%.

AI solves the problems and comes with extra features that we used to dream in. You will not be forced to waste your time again in shopping, AI will pick exactly the suit that you want for that specific ceremony. AI will also pick the missing food from refrigerator, based on your needs. AI will go beyond that, by reserving your wife's best resort at your anniversary. You do not need to be worry again about forgetting that date, AI will take care about you.
To sum up what AI can do for us:
• Make the most experienced human skills affordable by all human beings
• Saving our time, by performing the tedious work tailored by our needs and wishes

So, what is AI? and how it could solve our problems in this smart way?

What is AI?

We, humans, do like humans. Therefore, we worked on making computers think and learn like us. AI in terms, is the field that enables computers to solve human problems using the smartest human methods. The previous methods used to solve problems with computers was done by programming each case with the related required action. However, this is an impossible task with cases that has various probabilities, and sometimes hidden ones. Therefore, guiding the computers moved from feeding it with instructions to feeding it with examples. Then, the computer job will be to find the reasons for each scenario based on statistics and probabilities. And provide us with its conclusions and do the suitable action for that specific scenario.
When AI research started? by whom? and how did progress till now?

AI History

AI research started many decades ago. Alan Turing described AI literally in the same way it works nowadays in a public lecture in 1947: “What we want is a machine that can learn from experience” and that the “possibility of letting the machine alter its own instructions provides the mechanism for this.”.

Turing also proposed a test for identifying the expected intelligent machine if it has reached to the level that he suggested or not. This test is called Turing Test. There are currently some machines passed the Turing test. I expect that you have used one at least once. It is the chatbot. Chatbot model is a great example of AI machines that replies on human questions in a way that we will not know if it is a human or a machine reply. This was Turing test proposal.

Yes, you are right AI research has started many decades ago. Why only recently we started hearing about it, repeatedly? AI was growing during the past century very slowly and had stopped many times, (many AI winters). The two main reasons for proceeding slowly are the two lacks in: the high-performance machines and the data. In the last two decades with the rise of the interactive sites as social media, there is no lack in data anymore. And the high-performance machines became not only available but affordable to lots of scientists and startups across the world. The following infographic shows the ebbs and flows in AI history. More information about AI history can be found in “The history of Artificial Intelligence”.

AI Types

AI is categorized to two types, based on machine performance in various intelligent tasks, not in one specific task. As both types supposed to exceed human capabilities in one specific task at least.

1. Weak AI or Narrow AI (ANI):
It is the AIs that solves one specific task. It is needed for the time-consuming tasks and the tasks that we are incapable of solving because of the complex relations between the cause and result. We have many of weak AIs in these days, starting from predicting your next word when you write on your smartphone, and ending with the autonomous vehicles (self-driving cars).

2. Strong AI or Artificial General Intelligence (AGI):
It is still a theoretical AI type. As it is the form of human like intelligent machines. AGI mimics the human in terms of self-consciousness, reasoning, solving the problems, acting, and planning. If you want to imagine how AGI will perform in the future I, Robot movie might help.

We still do not know how AI learns from examples, this will be our next topic. It is Machine Learning. Stay tuned!

1.   Why is artificial intelligence important? https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html
2.   Intro to AI: https://www2.slideshare.net/ankit_ppt/lesson-1-intro-to-ai-132216012?from_action=save
3.   AI History: http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/
4.   AI Types: https://www.ibm.com/cloud/learn/strong-ai#toc-strong-ai--YaLcx8oG
5.   Machine Learning

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