Brax Digital Notepad
  • About me
  • Contact
  • Travel
    • East Hokkaido and pow riding
    • Faroe Islands
    • Nepal on foot
    • Climbing Gran Paradiso
    • Mongolia by Motorbike
    • Tanzania – Zanzibar
    • Tanzania – Safari
    • Tanzania – Kilimanjaro
    • Climbing Mont Blanc
    • Backpacking Vietnam
    • Namibia by Land Rover
    • USA West coast roadtrip
    • Iceland Roadtrip
  • Reading notes
    • The Lean product playbook
    • The Product Book: How to Become a Great Product Manager
    • Book notes – Factfulness
    • Book notes – Why we sleep
    • Book Notes – The Internet of money
    • Book Notes – The age of surveillance capitalism
    • Book Notes – Une brève histoire du temps
    • Book Notes – How to win friends & influence people
    • Book notes – Bankless Banking
    • Book notes – Thinking, fast and slow
    • Book notes – The shortest history of Germany
    • Book notes – Prisoners of Geography
    • Book notes – La guerre des métaux rares
    • Book notes – La panthère des neiges
    • Book notes – The end is always near
    • Book notes – The Smartest Guys in the Room ENRON
    • Book notes – Make it stick
    • Book notes – 12 rules for life
    • Book notes – The Outsiders
    • Book notes – The subtle art of not giving a fuck
    • Book notes – The coaching habit
    • Book notes – La Saga des Romanov
  • Artificial Intelligence
    • Andrew NG Machine Learning Course
      • ML 00 : Course Summary
      • ML 01 and 02: Introduction, Regression Analysis, and Gradient Descent
      • ML 03: Linear Algebra – Review
      • ML 04: Linear Regression with Multiple Variables
      • ML 05: Logistic Regression
      • ML 06: Regularization
      • ML 07: Neural Networks – Representation
      • ML 08:Neural Networks – Learning
      • ML 09: Advice for applying Machine Learning
      • ML 10: Machine Learning System Design
      • ML 11: Support Vector Machines (SVMs)
      • ML 12: Clustering
      • ML 13: Dimensionality Reduction (PCA)
      • ML 14: Anomaly Detection
      • ML 15: Recommender Systems
      • ML 16: Large Scale Machine Learning
      • ML 17: Application Example OCR
    • Deep Learning
      • Deep Learning with Tensor Flow and Keras – MNIST
      • Deep Learning with Tensor Flow and Keras – Cats and Dogs
      • QLearning – The mountain cart
    • Starcraft
      • Python SC2 – Rule Based Bot 1
      • Python Sc2 – Advanced bot
      • Python Sc2 – 3 Final rule based bot and data collection
    • Self driving car
      • Carla Agent – Environment exploration
      • Carla Agent – End to End Imitation learning
      • Carla Agent – Exploring Reinforcement learning
    • Twitter sentiment analysis
      • Twitter sentiment analysis in the context of Bitcoin price (1/3)
      • Twitter sentiment analysis in the context of Bitcoin price (2/3)
      • Twitter sentiment analysis in the context of Bitcoin price (3/3)
  • Cloud
    • GCP Cheat Sheet
    • 1 Google Cloud Platform Big Data and Machine Learning Fundamentals w1
    • 2 Google Cloud Platform Big Data and Machine Learning Fundamentals w2
    • 3 Leveraging Unstructured Data with Cloud Dataproc w1
    • 4 Serverless Data Analysis with Google BigQuery and Cloud Dataflow
    • 5 Serverless Machine Learning with Tensorflow on Google Cloud Platform
    • 6 Building Resilient Streaming Analytics Systems on GCP
    • 7 Modernizing Data Lakes and Data Warehouses with GCP
    • 8 Building Batch Data Pipelines on GCP
    • 9 Analytics and AI
    • 10 Preparing for the GCP exam
    • 11 Next Steps
    • 12 Exam essentials
    • 13 Passed the GCP Data professional exam in 2020 – tips
  • Python
    • Data Science Cheat Sheet
    • Python Cheat Sheets
    • Introduction to Python
    • Intermediate Python
      • Matplotlib
      • Dictionaries & Pandas
      • Logic, Control Flow and Filtering
      • Loops
      • Case Study: Hacker Statistics
    • Python data science Toolbox (Part 1)
      • Writing your own functions
      • Lambda functions
      • Default arguments, variable-length arguments and scope
    • Python Data Science Toolbox (Part 2)
      • Using iterators in PythonLand
      • List comprehensions
      • Case study
  • Blockchain
    • Blockchain developer Udacity
      • Part 1 – Blockchain Basics
      • Part 2 : Project – Create Your Own Private Blockchain
      • Part 3 – Ethereum Fundamentals and Development tools
      • Part 4 – Smart contracts with Solidity
      • Part 5 – Ethereum DAPP
    • Blockchain Revolution
      • Blockchain Design Principles
      • Blockchain transparency and Privacy
      • Blockchain ecosystem
      • Blockchain implementation challenges
      • Blockchain types of crypto assets
      • Blockchain mining explained in 7 steps
      • Blockchain Smart Contracts
      • Blockchain Identity and Identifiers
      • Blockchain Rethinking Financial Services
      • Blockchain and business : Applications and Implications
    • CAS Blockchain
      • CAS Blockchain notes – Introduction to Blockchains
      • CAS Blockchain notes – Platforms and Architectures
    • CAS Blockchain notes – Smart Contracts
    • Banking and Tokenization – Concrete requirements
    • Building your own Bitcoin price LED board
    • Making your very own token on the Ethereum blockchain
    • Create an artwork with artificial intelligence and publish it as an NFT on Opensea
    • DeFi, an opportunity or a threat for traditional institutions?
      • Decentralized finance, an opportunity, or a threat for traditional institutions? (1/2)
      • Decentralized finance, an opportunity, or a threat for traditional institutions? (2/2)
    • Sentcrypt
    • NFTs – New Frenzy Tokens
    • Podcast : Let’s talk Crypto custody solutions development
    • Webinar : How Tokenization will Change the Art Industry and Creative Markets
    • Creating your Own ERC-1155 NFT Avatar
    • Podcast : Let’s talk NFTs!
    • CV Summit 2022 panel on stable coins and CBDCs
    • Online Gaming : Gateway to the metaverse
    • Play to Earn and GameFI : Building blocks of the metaverse
    • DAO-WN the rabbit hole : a primer on daos
  • Misc pages
    • Building presentations
    • Quotes
    • New words – Def
    • List of reads
    • List of Reads – Images
    • Scrum Guide
    • Agile
    • SAFe for teams
    • RFI / RFP
  • Tip me !

Tech

All the Tech stuff I learn or do

Andrew NG Machine Learning Course

ML 10: Machine Learning System Design

Machine learning systems design In this section we’ll touch on how to put together a system Previous sections have looked at a wide range of different issues in significant focus This section is less mathematical, but material will be very useful non-the-less Consider the system approach You can understand all Read more…

By Brax, 5 years5 years ago
Andrew NG Machine Learning Course

ML 09: Advice for applying Machine Learning

Deciding what to try next We now know many techniques But, there is a big difference between someone who knows an algorithm vs. someone less familiar and doesn’t understand how to apply them Make sure you know how to chose the best avenues to explore the various techniques Here we focus deciding Read more…

By Brax, 5 years5 years ago
Andrew NG Machine Learning Course

ML 08:Neural Networks – Learning

Neural network cost function NNs – one of the most powerful learning algorithms Is a learning algorithm for fitting the derived parameters given a training set   Let’s have a first look at a neural network cost function Focus on application of NNs for classification problems Here’s the set up Training Read more…

By Brax, 5 years5 years ago
Andrew NG Machine Learning Course

ML 07: Neural Networks – Representation

Neural networks – Overview and summaryWhy do we need neural networks? Say we have a complex supervised learning classification problem Can use logistic regression with many polynomial terms Works well when you have 1-2 features If you have 100 features e.g. our housing example 100 house features, predict odds of Read more…

By Brax, 5 years5 years ago
Andrew NG Machine Learning Course

ML 06: Regularization

The problem of overfitting So far we’ve seen a few algorithms – work well for many applications, but can suffer from the problem of overfitting What is overfitting? What is regularization and how does it help Overfitting with linear regression Using our house pricing example again Fit a linear function Read more…

By Brax, 5 years5 years ago
Andrew NG Machine Learning Course

ML 05: Logistic Regression

Classification Where y is a discrete value Develop the logistic regression algorithm to determine what class a new input should fall into Classification problems Email -> spam/not spam? Online transactions -> fraudulent? Tumor -> Malignant/benign Variable in these problems is Y Y is either 0 or 1 0 = negative class (absence of something) Read more…

By Brax, 5 years5 years ago
Andrew NG Machine Learning Course

ML 04: Linear Regression with Multiple Variables

Linear regression with multiple features Multiple variables = multiple features In original version we had X = house size, use this to predict y = house price If in a new scheme we have more variables (such as number of bedrooms, number floors, age of the home) x1, x2, x3,x4 are the four features Read more…

By Brax, 5 years5 years ago
Andrew NG Machine Learning Course

ML 03: Linear Algebra – Review

Matrices – overview Rectangular array of numbers written between square brackets 2D array Named as capital letters (A,B,X,Y) Dimension of a matrix are [Rows x Columns] Start at top left To bottom left To bottom right R[r x c] means a matrix which has r rows and c columns Is a [4 Read more…

By Brax, 5 years5 years ago

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  • About me
  • Agile
  • Contact
  • Library
  • List of reads
  • Misc pages
  • New words
  • Quotes
  • RFI / RFP
  • SAFe for teams
  • Scrum Guide
  • Tip me !