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Hyper Phronesis on Innovation

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Data Science, Machine Learning, and Deep Learning

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Series:
Basic Intuitions for Machine Learning & Deep Learning

Throughout this series, I would like to outline basic intuitions and inspirations that I gained from my past Machine Learning journey. At the beginning it was very difficult for me to grasp the high-level conceptual framework of Machine Learning and Deep Learning. Whatever the real reason it might have been, to get right intuitions and inspirations was very helpful for me to grasp the subject.

Now, reflecting my own experience, my intention for this series is to share those intuitions with beginners. In this context, as a general policy throughout this series, I would like to draw a rough sketch rather than to go into details. Fortunately, there are oceans of open-resources that you can dive deeper into for these topics. Instead of creating redundant works, my intention in this series is to present guiding intuitions that help the readers navigate their own ML & DL journeys.

The content of this series is divided into several chapters as below:

• Introduction of Intuitions of Machine Learning & Deep Learning
  
• Chapter 1: Machine Learning Algorithm Paradigm
• Chapter 2: Generalization, the Ultimate Goal of Machine Learning Project
• Chapter 3: Deep Learning Connectionism Foundation—Philosophy of Connectionism
• Chapter 4: Deep Learning’s Learning Mechanism-- Optimization Paradigm driven by 3 Step Iteration Cycle
• Chapter 5: Deep Learning Revolution-- Engineering Achievement on Steroid
• Chapter 6: Deep Learning’s Carbon Footprint -- Imperative for Energy Efficiency Revolution
  
• Chapter 7: Deep Learning’s Blind Spots - Underspecification
• Chapter 8: Deep Learning’s 2 Basic Models: Sequence Models and Convolutional Neural Networks.

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Machine Learning Capstone Project:

Map Risk Clusters of Neighbourhoods in the time of Pandemic:
Three Different Lessons from Three Different Clustering Analysis

Here is the link to my first Machine Learning Capstone Project article at Medium.

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Blog Series:
"Confusing Blockchain"

In the ocean of blockchain topics in the web space, we frequently encounter blockchain-idealists’ mantras. Some cast a radical notion that a blockchain-base distributed system can eliminate rent-seeking middlemen and, as a result, deter, or even eliminate an abuse of system by monopolist and oligarchs.

However, the reality of blockchain as of today is far from, is even contrary to, such radical notions. The gap between the popular blockchain-idealist’s mantras and reality has caused confusions about blockchain.

Despite the gap, their beautiful mantras continue to echo and have strong influence in shaping our collective perception about and our collective behaviour toward blockchain.

Are they hypnotising us with those deceptive mantras? Or, are we in the middle of a long journey to realise such a radical revolution in the way we organize transactions?

The series shares a part of my journey in understanding causes for the gap. Although it does not aim to cover a comprehensive list of issues, it intends to promote a better understanding about the confusion.

The series consists of the following topics.
    Chapter 1: Linguistic ambiguity;
    Chapter 2: Limitation of Consensus Protocols (Trilemma and Concentration Curse);
    Chapter 3: Blockchain Myth-conceptions:
         a) Disintermediation Myth-Conception;
         b) “autonomous self-regulating governance” Myth-Conceptions
                  Smart Contract Security Myth-Conception;
                  Immutability Myth-Conception;
                  Byzantine Fault Tolerant Myth-Conception

Blog 1:
Chapter 1: Linguistic Ambiguity

Blog 2:
Chapter 2: Limitations in Consensus Protocols (Trilemma and Concentration Curse)

Blog 3:
Chapter 3-1: Blockchain Myth-conception Part 1: Disintermediation Myth—Distributed Kingdoms of Intermediaries

Blog 4:
Chapter 3-2: Blockchain Myth-conceptions Part 2: “Autonomous Self-Regulating Governance” Myth-conceptions

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Contents
Innovation

Deflationary Innovation

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