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 !

Self driving car

Artificial Intelligence

Carla Agent – Exploring Reinforcement learning

As seen in our previous article, our agent is able to imitate an autopilot, at least a little bit. Driving around in auto pilot sure is fun, but what would be the next step to enhance our agent ? Let’s try to make him learn via Reinforcement learning this time. Read more…

By Brax, 4 years4 years ago
Artificial Intelligence

Carla Agent – End to End Imitation learning

What is Imitation learning So, our environment is ready and we have explored how we can interact with it. Let’s start by trying to develop an imitation learning agent. That being said let’s establish the ground work. Imitation learning is especially useful when it is easier for an actor to Read more…

By Brax, 5 years5 years ago
Artificial Intelligence

Carla Agent – Environment exploration

Introduction Transportation has always been a corner stone of progress, whether to convey goods from the Orient to Europe or enable the population to efficiently commute between cities. Tremendous progress has been achieved in the last century, thanks to breakouts in industry we are now blessed with trains cruising at 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 !