Seeking Wisdom
Hello there!
I maintain this Blog to record my learnings from different fields of Investing and Technology.
It falls into four areas: building machine learning systems, the infrastructure they run on, investing, and the books I have read along the way. The newest posts from each are below.
You can also have a look at some of the tech projects i have worked on the over the years:Projects
New series · 14 parts
Kubernetes by Example
Everything you need to run something real, taught in order, against one project small enough to fit on a laptop and complete enough to break properly.
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AI/ML Engineering
- Kubernetes by Example
Everything you need to run something real, taught in order, against one project small enough to fit on a laptop and complete enough to break properly.
- Kubernetes by Example 01: The Object Model
Why kubectl apply saying “created” tells you almost nothing — and what is really going on in the ten seconds afterwards.
- Kubernetes by Example 02: Pods and Containers
It ran perfectly in Docker. You put it in a Pod and it died. Here is what a Pod actually is, and why that difference matters more than it looks.
- Kubernetes by Example 03: Labels and Selectors
Nothing in Kubernetes finds anything by name. Once you know what it does instead, half the surprising behaviour stops being surprising.
- Kubernetes by Example 04: Deployments and Rollouts
You asked for a rolling update and got a gap anyway. Here is what actually moves during a deploy, and what sets its pace.
- Kubernetes by Example 05: DaemonSets
Three replicas gave you two pods on one machine and none on another. When the whole point is to be there , you need a different question.
- Kubernetes by ExampleEverything you need to run something real, taught in order, against one project small enough to fit on a laptop and complete enough to break properly.
- Kubernetes by Example 01: The Object ModelWhy kubectl apply saying “created” tells you almost nothing — and what is really going on in the ten seconds afterwards.
- Kubernetes by Example 02: Pods and ContainersIt ran perfectly in Docker. You put it in a Pod and it died. Here is what a Pod actually is, and why that difference matters more than it looks.
- Kubernetes by Example 03: Labels and SelectorsNothing in Kubernetes finds anything by name. Once you know what it does instead, half the surprising behaviour stops being surprising.
- Kubernetes by Example 04: Deployments and RolloutsYou asked for a rolling update and got a gap anyway. Here is what actually moves during a deploy, and what sets its pace.
- Kubernetes by Example 05: DaemonSetsThree replicas gave you two pods on one machine and none on another. When the whole point is to be there , you need a different question.
Investing
- UX Design Principles and Investing
Applying UX design principles to investing
- Market Phases
Nature likes to follow cycles and markets are no different.
- Costly Common Investor Mistakes
The reason people either lose money or achieve mediocre results in the stock market is that they simply make too many mistakes.
- CANSLIM - Picking winners in the stock market
CANSLIM stands for seven chief characteristics of the greatest winning stocks at their developing early stages
- Experts, Education, and Egos
Never fight the market, Its bigger than you are.
- Walter Schloss Investment Tenets
Key Investment rules from super investor of Graham-and-Doddsville
- UX Design Principles and InvestingApplying UX design principles to investing
- Market PhasesNature likes to follow cycles and markets are no different.
- Costly Common Investor MistakesThe reason people either lose money or achieve mediocre results in the stock market is that they simply make too many mistakes.
- CANSLIM - Picking winners in the stock marketCANSLIM stands for seven chief characteristics of the greatest winning stocks at their developing early stages
- Experts, Education, and EgosNever fight the market, Its bigger than you are.
Walter Schloss Investment TenetsKey Investment rules from super investor of Graham-and-Doddsville
Books
- Kaizen (改善)- The art of continuous life improvement
The art of continuous life improvement
- How to Lie with Statistics
A well-wrapped statistic is better than Hitler's big lie; it misleads yet it cannot be pinned down
- Law of Returns
There are two laws related to increasing and diminishing returns about how markets and businesses operate.
- Punctuated Equilibrium , Tipping point, Growth
There is a order in chaos to understand technological transformations
- 80/20 Pareto Principle
To create wealth, we must do more with less
- Sources of Power
A "Good strategy" is an approach that magnifies the effectiveness of actions by finding and using sources of power.
- Kaizen (改善)- The art of continuous life improvementThe art of continuous life improvement
- How to Lie with StatisticsA well-wrapped statistic is better than Hitler's big lie; it misleads yet it cannot be pinned down
- Law of ReturnsThere are two laws related to increasing and diminishing returns about how markets and businesses operate.
- Punctuated Equilibrium , Tipping point, GrowthThere is a order in chaos to understand technological transformations
- 80/20 Pareto PrincipleTo create wealth, we must do more with less
- Sources of PowerA "Good strategy" is an approach that magnifies the effectiveness of actions by finding and using sources of power.
Tech
- How to Lie with Statistics
There is something about numbers isn’t there? They have an uncanny ability to suspend our common sense.
- Your Guide to Sentiment Analysis
Sentiment Analysis is used to discover people’s opinions, emotions and feelings about a product or service.
- Sentiment Analysis for User Reviews
We create a system to assign sentiment scores to reviews (0.0 to 1.0 where a zero score is a full negative sentiment)
- Deep Learning based Email Spam Filter
We create Deep Learning based Email Spam Filter on Enron and Spam assassin dataset and compare results with Xgboost, SVM and Random forest
- Semantic Role Labeling
Semantic Role Labeling consists of the detection of the semantic arguments associated with the predicate or verb of a sentence and their classification into their specific roles.
- Statistical Inference
Statistics are the foundations of Data Analysis. Statistical Inference is a process of deducing properties of an underlying distribution by analysis of data.
- How to Lie with StatisticsThere is something about numbers isn’t there? They have an uncanny ability to suspend our common sense.
- Your Guide to Sentiment AnalysisSentiment Analysis is used to discover people’s opinions, emotions and feelings about a product or service.
- Sentiment Analysis for User ReviewsWe create a system to assign sentiment scores to reviews (0.0 to 1.0 where a zero score is a full negative sentiment)
- Deep Learning based Email Spam FilterWe create Deep Learning based Email Spam Filter on Enron and Spam assassin dataset and compare results with Xgboost, SVM and Random forest
- Semantic Role LabelingSemantic Role Labeling consists of the detection of the semantic arguments associated with the predicate or verb of a sentence and their classification into their specific roles.
- Statistical InferenceStatistics are the foundations of Data Analysis. Statistical Inference is a process of deducing properties of an underlying distribution by analysis of data.