François Chung, Ph.D.
Deep learning and TensorFlow

Deep learning and TensorFlow

Cognitive Class training, MOOC (2020). This learning path presents the basic concepts of deep learning and TensorFlow with hands-on experience in solving problems. Throughout the training, TensorFlow is used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the deep learning world where TensorFlow is applied for backpropagation to tune the weights and biases while the neural networks are being trained.

Course 1: Deep learning fundamentals

Main topics:

  • Introduction to deep learning;
  • Deep learning models;
  • Additional deep learning models;
  • Deep learning platforms and libraries.

Course 2: Deep learning with TensorFlow

Main topics:

  • Introduction to TensorFlow;
  • CNN - Convolutional Neural Network;
  • RNN - Recurrent Neural Network;
  • Unsupervised learning.

References

Training

Deep learning fundamentals (course certificate)
Deep Learning Essentials (certification badge)
Deep learning with TensorFlow (course certificate)
Deep Learning using TensorFlow (certification badge)

Related articles

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ITIL Foundation

ITIL Foundation

QRP International training, Belgium (2020). This online training introduces participants to the IT service management (ITSM) and prepares them for the ITIL Foundation exam. The training provides an understanding of the common language and key concepts of ITIL (Information Technology Infrastructure Library) and shows how ITSM professionals can improve their work and the work of their organisation thanks to the ITIL 4 guidance.

Module 1: Introduction

Main topics:

  • Guide for the ITIL Foundation exam;
  • ITIL repository and documents;
  • Successive versions of ITIL.

Module 2: Definitions and key concepts

Main topics:

  • Service management;
  • Provision of services;
  • Client, user and sponsor;
  • Services and products;
  • Offer of services;
  • Service relations;
  • Consumption of services;
  • Deliverables and results;
  • Utility and warranty.

Module 3: The 7 guiding principles of ITIL

Main topics:

  • Focus on value;
  • Start where you are;
  • Progress iteratively with feedback;
  • Collaborate and promote visibility;
  • Think and work holistically;
  • Keep it simple and practical;
  • Optimize and automate.

Module 4: The 4 dimensions of service management

Main topics:

  • Organizations and people;
  • Information and technology;
  • Partners and suppliers;
  • Value streams and processes.

Module 5: The service value chain

Main topics:

  • Plan;
  • Engage;
  • Design and transition;
  • Obtain and build;
  • Deliver and support;
  • Improve.

Module 6: General management practices

Main topics:

  • Continual improvement;
  • Information security management;
  • Relationship management;
  • Supplier management.

Module 7: Service management practices

Main topics:

  • Change control;
  • Incident management;
  • IT asset management;
  • Monitoring and event management;
  • Problem management;
  • Release management;
  • Service configuration management;
  • Service desk;
  • Service level management;
  • Service request management.

References

Training

Certification (PeopleCert)

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First aid trainer

First aid trainer

Red Cross training, Belgium (2020). This 10-day training to first aid trainers covers the technical gestures and the teaching methods needed to conduct first aid training sessions for the BEPS (brevet européen de premiers secours). The trainer teaches the participants the saving gestures, who thus acquire the right reflexes that will make them an essential link in the rescue chain.

Day 1: Technical module

Main topics:

  • Positioning of the victim;
  • Cardiopulmonary resuscitation (CPR);
  • Automated external defibrillator (AED).

Day 2: Theoretical reinforcement

Main topics:

  • The human cell;
  • The human body;
  • Pathologies.

Day 3: Organization of trainings

Main topics:

  • Make-up;
  • Cleaning the manikin;
  • Legal framework.

Day 4: First aid reinforcement (I)

Main topics:

  • First aid material;
  • Essential rules of intervention;
  • Approaching an unconscious victim.

Day 5: First aid reinforcement (II)

Main topics:

  • Approaching a conscious victim;
  • Skin lesions;
  • Musculoskeletal injuries.

Day 6: Pedagogy

Main topics:

  • Methodology;
  • Educational goals;
  • Management of an animation.

Day 7: Specific module (I)

Main topics:

  • Electrical accident;
  • Car accident;
  • CO poisoning.

Day 8: Specific module (II)

Main topics:

  • Recovery position;
  • CPR and AED;
  • Airway obstruction.

Day 9: Specific module (III)

Main topics:

  • Wound with foreign body;
  • Head trauma;
  • Severe thermal burn.

Day 10: Specific module (IV)

Main topics:

  • Ankle sprain;
  • Hemorrhage;
  • Chest pain.

References

Related articles

Certified first response (Red Cross training)
First aid (Red Cross training)

Learn more

BEPS – Brevet européen de premiers secours
Animateur.rice premier secours (first aid trainer)
Croix-Rouge de Belgique (Belgian Red Cross)

Google Cloud: big data and machine learning

Google Cloud: big data and machine learning

Coursera training, MOOC (2020). This online training introduces the big data and machine learning (ML) capabilities of Google Cloud Platform (CGP). Through a combination of presentations, demos and hands-on labs, the training gives an overview of Google Cloud and a detailed view of the data processing and ML solutions, such as BigQuery, Cloud SQL, Dataproc, Pub/Sub, Dataflow and Data Studio.

Week 1: Big data and ML fundamentals

Main topics:

  • Exploring a BigQuery public dataset;
  • Choosing the right solution approach;
  • Recommending products using Cloud SQL and Spark;
  • Predicting visitor purchases using BigQuery ML.

Week 2: Modern data pipeline challenges

Main topics:

  • Real-time IoT dashboards;
  • Creating a streaming data pipeline;
  • ML on unstructured datasets;
  • Classifying images with pre-built ML models.

References

Spark fundamentals

Spark fundamentals

Cognitive Class training, MOOC (2020). This learning path addresses the fundamentals of Apache Spark, an open source engine for large scale data processing that is revolutionizing the analytics and big data world. This training is an opportunity to learn from industry leaders about Spark, which is built around speed, ease of use and analytics, and provides hands-on opportunities and projects to build confidence with the Spark toolset.

Course 1: Spark fundamentals I

Main topics:

  • Introduction to Spark;
  • Resilient Distributed Dataset (RDD) and DataFrames;
  • Spark application programming;
  • Introduction to Spark libraries;
  • Spark configuration, monitoring and tuning.

Course 2: Spark fundamentals II

Main topics:

  • Introduction to notebooks;
  • RDD architecture;
  • Optimizing transformations and actions;
  • Caching and serialization;
  • Developing and testing.

Course 3: Spark MLlib

Main topics:

  • Spark MLlib data types;
  • Review of algorithms;
  • Decision trees and random forests;
  • Spark MLlib clustering.

Course 4: Exploring GraphX

Main topics:

  • Introduction to Graph-Parallel;
  • Exploring graph operators;
  • Visualizing and modifying GraphX;
  • Aggregation and caching.

Course 5: Big data in R using Spark

Main topics:

  • Introduction to SparkR;
  • Data manipulation in SparkR;
  • Machine learning in SparkR.

References

Training

Spark fundamentals I (course certificate)
Spark – Level 1 (certification badge)
Spark fundamentals II (course certificate)
Spark MLlib (course certificate)
Exploring GraphX (course certificate)
Big data in R using Spark (course certificate)
Spark - Level 2 (certification badge)

Related articles

Hadoop fundamentals (Cognitive Class training)
Data science specialization (Coursera training)

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Hadoop fundamentals

Hadoop fundamentals

Cognitive Class training, MOOC (2020). This learning path presents Hadoop, which is an open source framework for distributed storage and processing of big data. The training covers content that is critical to anyone's success in this realm by explaining the Hadoop conceptual design, introducing MapReduce, YARN (Yet Another Resource Negotiator) and Hive, then explaining how to use Hadoop and manipulate data without the use of complex coding.

Course 1: Hadoop 101

Main topics:

  • Introduction to Hadoop;
  • Hadoop architecture and HDFS;
  • Hadoop administration;
  • Hadoop components.

Course 2: MapReduce and YARN

Main topics:

  • Introduction to MapReduce and YARN;
  • Limitations of Hadoop v1 and MapReduce v1;
  • YARN architecture.

Course 3: Moving data into Hadoop

Main topics:

  • Load scenarios;
  • Using Sqoop;
  • Flume overview;
  • Using Data Click.

Course 4: Accessing Hadoop data using Hive

Main topics:

  • Introduction to Hive;
  • Hive DDL - Data Definition Language;
  • Hive DML - Data Manipulation Language;
  • Hive operators and functions.

References

Training

Hadoop 101 (course certificate)
Hadoop Foundations – Level 1 (certification badge)
MapReduce and YARN (course certificate)
Hadoop Programming – Level 1 (certification badge)
Moving data into Hadoop (course certificate)
Hadoop Administration – Level 1 (certification badge)
Accessing Hadoop data using Hive (course certificate)
Hadoop Data Access – Level 1 (certification badge)
Hadoop Foundations – Level 2 (certification badge)

Related articles

Spark fundamentals (Cognitive Class training)
Data science specialization (Coursera training)

Learn more

Azure: fundamentals, machine learning and Power BI

Azure: fundamentals, machine learning and Power BI

Microsoft Docs training, MOOC (2020). These 3 online courses present Microsoft Azure and Power BI. The training teaches the basic cloud concepts, along with hands-on exercises, and provides an overview of Azure services, such as Azure Machine Learning (ML), which is a cloud platform for training, deploying, managing and monitoring ML models. Furthermore, the training explains how to use Power BI and build business intelligence reports.

Course 1: Azure fundamentals

Main topics:

  • Principles of cloud computing;
  • Azure architecture and service guarantees;
  • Compute, data storage and networking;
  • Security, responsibility and trust;
  • Infrastructure standards with Azure Policy;
  • Azure resources with Azure Resource Manager.

Course 2: Azure machine learning

Main topics:

  • Working with data;
  • Orchestrate ML with pipelines;
  • Deploy ML models;
  • Automate model selection;
  • Tune hyperparameters;
  • Monitor models and data drift.

Course 3: Power BI

Main topics:

  • Get started building with Power BI;
  • Get data with Power BI Desktop;
  • Model and explore data;
  • Use visuals, publish and share.

References

Training

Microsoft Docs (badges and trophies)

Related articles

Learn more

Microsoft Docs (Azure fundamentals)
Microsoft Docs (Azure machine learning)
Microsoft Docs (Power BI)

Strategic thinking

Strategic thinking

LinkedIn Learning training, MOOC (2020). Working hard is important, but what's really essential is making sure to be working on the right things. Strategic thinking helps managers and leaders guide the direction of their teams and find solutions to key business problems. Furthermore, the training presents frameworks and strategies to make strategic thinking a daily habit, so that we can make the best use of our time, energy and effort at work.

Course 1: Strategic thinking

Main topics:

  • Strategic thinking: the big picture;
  • Developing your strategic thinking;
  • Making implementation happen.

Course 2: How to make strategic thinking a habit

Main topics:

  • Setting yourself up for strategic thinking;
  • Strategic thinking tactics;
  • The most important strategic questions.

References

Learn more

LinkedIn Learning (Strategic thinking)
LinkedIn Learning (How to make strategic thinking a habit)

Trends debate: technology and privacy

Trends debate: technology and privacy

Fujitsu project @Brussels, Belgium (2020). In the midst of the Covid-19 pandemic, and the development of contact tracing applications to help track and stop the spread of the coronavirus, Trends organized an online debate around technology and privacy entitled: “Does saying yes to technology mean saying no to ethics and privacy?”. The debate has been published as an article in Trends, which is a Flemish financial-economic magazine presenting analyses of current economic, political and social news.

Participants in this debate, who are experts in technology, ethics, privacy or legislation, are representing the following organizations:

  • Atos;
  • Everest Law;
  • Fujitsu;
  • Icapps;
  • Ministry of Privacy;
  • Nutanix;
  • Privaco;
  • SiriusLegal;
  • Universiteit Gent.

The main topics of discussion around this debate are as follows:

  • Should a contact tracing application be considered as a Pandora's box whose consequences cannot be estimated?
  • Is GDPR sufficient as a security mechanism? Or do we need to develop a clearer framework to regulate the potential misuse of technology?
  • To what extent does fragmentation, at political, geographic or economic level, hinder the efficiency of contact tracing applications requiring a global reach to be efficient?
  • Who decides what can be allowed with the possibilities offered by the technology? And how can we enforce that?
  • What is the correct mechanism to determine when a technology is needed? And when can a market be considered as ready?

As a Digital Business Analyst representing Fujitsu for this debate, I shared Fujitsu’s vision and values regarding technology and privacy in the context of the global Covid-19 pandemic, with topics such as working from home (technologies and benefits), extending the legal framework, including GDPR, to regulate Artificial Intelligence and building a human centric future with ethical technology. The debate has been published as an article in Trends on 13th August 2020.

References

Publication

Related article

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Quantum computing and physics

Quantum computing and physics

Udemy training, MOOC (2020). This online training presents quantum computing as the next wave of the software industry. Quantum computers are exponentially faster than classical computers of today. Problems that were considered too difficult for computers to solve, such as simulation of protein folding in biological systems and cracking RSA encryption, are now possible through quantum computers. The training is primarily about analyzing the behavior of quantum circuits using math and quantum physics.

Section 1: Introduction

Main topics:

  • Why learn about quantum computing?
  • How is quantum computing different?

Section 2: Quantum cryptography

Main topics:

  • Experiments with photon polarization;
  • No-cloning theorem;
  • Encoding with XOR;
  • Encryption with single-use shared-secrets;
  • Encoding data in photon polarization.

Section 3: Foundation

Main topics:

  • Probability;
  • Complex numbers;
  • Matrix algebra;
  • Matrix multiplication;
  • Logic circuits.

Section 4: Math model for quantum physics

Main topics:

  • Modeling physics with math;
  • Substractive probabilities through complex numbers;
  • Modeling superposition through matrices.

Section 5: Quantum physics of spin states

Main topics:

  • Matrix representation of quantum state;
  • State vector;
  • Experiments with spin.

Section 6: Modeling quantum spin states with math

Main topics:

  • Analysis of experiments;
  • Dirac bra-ket notation;
  • Random behavior.

Section 7: Reversible and irreversible state transformations

Main topics:

  • Irreversible transformations measurement;
  • Reversible state transformations.

Section 8: Multi-qubit systems

Main topic:

  • Multi-qubit systems.

Section 9: Quantum entanglement

Main topic:

  • Quantum entanglement.

Section 10: Quantum computing model

Main topics:

  • Quantum circuits;
  • Reversible gates;
  • CNOT and CCNOT gates;
  • Universal and Fredkin gates;
  • Superposition and entanglement on quantum gates.

Section 11: Quantum programming with Microsoft Q#

Main topics:

  • Q# simulator hardware architecture;
  • Measuring superposition states;
  • Effect of superposition on quantum gates;
  • Toffoli gate;
  • Programming quantum computers.

Section 12: IBM quantum experience

Main topic:

  • IBM quantum experience.

Section 13: Conclusion

Main topic:

  • Speedup revisited.

References

Training

Related articles

Digital Annealer (Fujitsu project)
DataNews 2020 (FR) (magazine article, French version)
DataNews 2020 (NL) (magazine article, Dutch version)

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