omerbsezer/modelbuild_pipeline

Stars
1
Language
Python
Created over 1 year ago
Updated over 1 year ago

omerbsezer/modelbuild_pipeline

omerbsezer

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Test Repo for Model Build

Fast-Kubernetes

This repo covers Kubernetes with LABs: Kubectl, Pod, Deployment, Service, PV, PVC, Rollout, Multicontainer, Daemonset, Taint-Toleration, Job, Ingress, Kubeadm, Helm, etc.

Reinforcement_learning_tutorial_with_demo

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

Jupyter Notebook

LSTM_RNN_Tutorials_with_Demo

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

Fast-Ansible

This repo covers Ansible with LABs: Multipass, Commands, Modules, Playbooks, Tags, Managing Files and Servers, Users, Roles, Handlers, Host Variables, Templates and details.

Fast-Docker

This repo covers containerization and Docker Environment: Docker File, Image, Container, Commands, Volumes, Networks, Swarm, Stack, Service, possible scenarios.

Fast-Pytorch

Pytorch Tutorial, Pytorch with Google Colab, Pytorch Implementations: CNN, RNN, DCGAN, Transfer Learning, Chatbot, Pytorch Sample Codes

Jupyter Notebook

Generative_Models_Tutorial_with_Demo

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

Jupyter Notebook

Fast-Terraform

This repo covers Terraform (Infrastructure as Code) with LABs using AWS and AWS Sample Projects: Resources, Variables, Meta Arguments, Provisioners, Dynamic Blocks, Modules, Provisioning AWS Resources (EC2, EBS, EFS, VPC, IAM Policies, Roles, ECS, ECR, Fargate, EKS, Lambda, API-Gateway, ELB, S3, etc.

CNN-TA

Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach: A novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties.

Fast-Kubeflow

This repo covers Kubeflow Environment with LABs: Kubeflow GUI, Jupyter Notebooks on pods, Kubeflow Pipelines, Experiments, KALE, KATIB (AutoML: Hyperparameter Tuning), KFServe (Model Serving), Training Operators (Distributed Training), Projects, etc.

SparkDeepMlpGADow30

A Deep Neural-Network based (Deep MLP) Stock Trading System based on Evolutionary (Genetic Algorithm) Optimized Technical Analysis Parameters (using Apache Spark MLlib)

TimeSeries2DBarChartImageCNN

Conversion of the time series values to 2-D stock bar chart images and prediction using CNN (using Keras-Tensorflow)

SparkMlpDow30

A new stock trading and prediction model based on a MLP neural network utilizing technical analysis indicator values as features (using Apache Spark MLlib)

AIMap

Map of Artificial Intelligence: Classifications, Approaches, Algorithms, Libraries, Tools, State of Art Studies, Awesome Repos, etc..

PolicyGradient_PongGame

Pong Game problem solving using RL - Policy Gradient with OpenAI Gym Framework and Tensorflow

Qlearning_MountainCar

Mountain Car problem solving using RL - QLearning with OpenAI Gym Framework

IoTSmartHomeOntologySimulator

A smart home sensor ontology (that is a specialized ontology based on the Semantic Sensor Networks (SSN) ontology) and simulation evironment of a smart home use case

SentimentAnalysis

Sentences are classified in 5 different sentiment using LSTM (Keras). Results are expressed with emoji characters.

NeuralStyleTransfer

Art/Painting Generation using AI (Neural Style Transfer) using Tensorflow

BasicLSTM

The aim of this implementation is to help to learn structure of basic LSTM (LSTM cell forward, LSTM cell backward, etc..)

IoTWeatherSensorsAnalysis

Proposed "An Extended IoT Framework" learning part is presented with a use case on weather data clustering analysis. Sensor faults and anomalies are determined using K-means clustering (using scikit-learn)

MusicGeneration

Music generation with LSTM model (Keras)

QLearning_CartPole

Cart Pole problem solving using RL - QLearning with OpenAI Gym Framework