Ömer Berat Sezer's repositories

Fast-Kubernetes

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

Language:PowerShellLicense:MITStargazers:1678Issues:29Issues:1

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..

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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.

Language:JinjaLicense:MITStargazers:580Issues:10Issues:1

Fast-Docker

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

Language:DockerfileLicense:MITStargazers:526Issues:12Issues:1

Fast-Pytorch

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

Language:Jupyter NotebookStargazers:427Issues:17Issues:3

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..

Language:Jupyter NotebookStargazers:323Issues:17Issues:0

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.

Language:HCLLicense:MITStargazers:248Issues:5Issues:0

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)

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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)

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TimeSeries2DBarChartImageCNN

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

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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

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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

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ml-monitoring

A demo of Prometheus+Grafana for monitoring an ML model served with FastAPI.

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NeuralStyleTransfer

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

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SentimentAnalysis

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

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)

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BasicLSTM

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

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MusicGeneration

Music generation with LSTM model (Keras)

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QLearning_CartPole

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

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modelbuild_pipeline

Test Repo for Model Build

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BasicRNN

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

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keras

Deep Learning for humans

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modeldeploy_pipeline

Test Repo for Model Deploy

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