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

Learn more

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)

Learn more

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

Learn more

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)

Learn more

Data science specialization

Data science specialization

Coursera training, MOOC (2020). This specialization covers the concepts and tools needed throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. Topics covered include using R to clean, analyze, and visualize data, navigating the entire data science pipeline from data acquisition to publication, using GitHub to manage data science projects, and performing regression analysis, least squares and inference using regression models.

Course 1: Data scientist’s toolbox

Main topics:

  • Data science fundamentals;
  • R and Rstudio;
  • Version control and GitHub;
  • R Markdown, scientific thinking and big data.

Course 2: R programming

Main topics:

  • Background and getting started;
  • Programming with R;
  • Loop functions and debugging;
  • Simulation and code profiling.

Course 3: Getting and cleaning data

Main topics:

  • Finding data and reading different file types;
  • Data storage systems;
  • Organizing, merging and managing data;
  • Text and data manipulation in R.

Course 4: Exploratory data analysis

Main topics:

  • Analytic graphics and base plotting in R;
  • Lattice and ggplot2;
  • Data dimension reduction;
  • Cluster analysis techniques.

Course 5: Reproducible research

Main topics:

  • Concepts, ideas and structure;
  • Markdown and knitr;
  • Reproducible research checklist;
  • Evidence-based data analysis.

Course 6: Statistical inference

Main topics:

  • Probability and expected values;
  • Variability, distribution and asymptote;
  • Intervals, testing and p-value;
  • Power, bootstrapping and permutation tests.

Course 7: Regression models

Main topics:

  • Least squares and linear regression;
  • Linear and multivariate regression;
  • Residuals and diagnostics;
  • Logistic and Poisson regression.

Course 8: Practical machine learning

Main topics:

  • Prediction, errors and cross validation;
  • Caret package;
  • Decision trees and random forests;
  • Regularized regression and combining predictors.

Course 9: Developing data products

Main topics:

  • Shiny, GoogleVis and Plotly;
  • R Markdown and Leaflet;
  • R Pakages and Swirl.

References

Related articles

Spark fundamentals (Cognitive Class training)
Hadoop fundamentals (Cognitive Class training)
AWS: foundations and machine learning (AWS training)

Learn more

Blockchain essentials

Blockchain essentials

Cognitive Class training, MOOC (2020). This online training presents blockchain for business and explores use cases that demonstrate how this technology is radically improving supply chains and banking, as well as creating new opportunities for innovation. Blockchain technology provides a shared ledger to save time when recording transactions between parties, remove costs associated with intermediaries and reduce risks of fraud and tampering.

Module 1: What is blockchain?

Main topics:

  • Business networks;
  • Ledgers, transactions and contracts;
  • The problem with existing networks;
  • Different types of blockchain;
  • Requirements of a blockchain for business.

Module 2: Examples of blockchain networks

Main topics:

  • Improving global trade;
  • Supply chain transparency;
  • Global payments;
  • Decentralized and trusted identity;
  • Key players for blockchain adoption.

Module 3: IBM and blockchain

Main topics:

  • IBM's blockchain strategy;
  • IBM Blockchain Platform;
  • Hyperledger project;
  • Hyperledger Fabric.

References

Training

Blockchain essentials (course certificate)
IBM Blockchain Essentials V2 (certification badge)

Related article

Learn more