About
I am Data Scientist and PhD in Dynamical System.
Here I have collected simple notes that I mostly use for educational proposes. Most of things here are about installing and configuring software.
Categories
R
argo
aws
bayes
bootstrap
c#
c++
cloud
- Google Storage
- Google Kubernetes Engine Set Up
- How to deploy Amazon EMR
- How to launch EC2 instance
- AWS CLI, S3 And Boto3
conda
data base
docker
- Pystan with jupyter in docker
- Dockers that talk to each other
- Node.js Hello World with Docker and k8s
- Docker Compose: Flask with Redis
- Deploying docker to kubernetes with Argo CD
- Start with travis
- Docker: Hello World
emacs
fastapi
flask
gcloud
- Creating Ubuntu VM with Jupyter on GCP
- Spark on gcloud with jupyter
- Google Storage
- Google Kubernetes Engine Set Up
go
gunicorn
helm
ide
intelij
jags
java
javascript
jupyter
kubernetes
- Simple chart with helm
- Kubernetes: ingress
- Kubernetes: Flask with redis
- Kubernetes: Namespace and Replication Controller
- Google Kubernetes Engine Set Up
- Deploying docker to kubernetes with Argo CD
- Kubernetes hello world
machine learning
- Machine Learning Part 5: How we evaluate classification algorithms
- Machine Learning Part 4: Bias–Variance Trade-Off in Polinomial Regression
- Machine Learning Part 3: How to choose best multiple linear model
- Machine Learning Part 2: How to train linear model and then test its performance
- Machine Learning Part 1: What Is Machine Learing?
- Serving Model
minikube
mongodb
nginx
node
ohmyzsh
plotly flask
pyspak
pyspark
python
- Pystan with jupyter in docker
- Installing pystan on Mac
- Visual Studio Code on mac
- Simple chart with helm
- Pycharm Community Edition on mac
- Spark in minikube
- How to install pyspark locally
- How to install python for data science with conda
- Pyenv and pipenv on mac
- DynamoDB with python
- AWS Lambda with Zappa
- Python packages
- Creating Ubuntu VM with Jupyter on GCP
- Spark on gcloud with jupyter
- Python pathlib
- Flask with bootstrap and plotly
- Writing Python Script
- How to access S3 from pyspark
- How to install pyspark
- Machine Learning Part 5: How we evaluate classification algorithms
- Machine Learning Part 4: Bias–Variance Trade-Off in Polinomial Regression
- Machine Learning Part 3: How to choose best multiple linear model
- Machine Learning Part 2: How to train linear model and then test its performance
- How to install pytorch with conda
- Serving Model
- AWS CLI, S3 And Boto3
- Pyenv and VirtualEnvs
pytorch
r
redis
rstudio
ruby
rust
sbt
scala
- Self-contained spark application with scala
- IntelliJ Community Edition for Scala on mac
- Scala on mac
- Spark in minikube
- How to create Scala script
- How to create scala project with sbt
scripting
shell
sklearn
- Machine Learning Part 5: How we evaluate classification algorithms
- Machine Learning Part 4: Bias–Variance Trade-Off in Polinomial Regression
- Machine Learning Part 3: How to choose best multiple linear model
- Machine Learning Part 2: How to train linear model and then test its performance
spark
- Self-contained spark application with scala
- Spark in minikube
- Spark on gcloud with jupyter
- How to access S3 from pyspark
- How to install pyspark
- How to deploy Amazon EMR
- Install and Run SparkR - easy way