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Hi there , I'm Jay

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Coding

💫 About Me:

🔭 I’m currently studying on Artificial Intelligence, Machine Learning, Quantum Technology and Biology
👯 I’m looking to collaborate on any Data Science, LLM and Web3 projects
🤝 I’m looking for help to work with Cloud Computing, Artificial Intelligence, Machine Learning, and Blockchain Development
🤝 I would love to level-up my knowledge in BioInformatics, Cyber Security, Quantum Computing, Robotic Process Automation
🌱 I’m currently learning more about Rust, Go, Consensus Algorithm of Blockchain Technology and other Blockchain EVM
🦾 Programming: I'm currently learning more on programming languages such as Python, R, Java & C++ so I can build and implement models.
📈 Probability, statistics, and linear algebra: These are my math buddy needed to implement different AI and machine learning models.
🧊 Big data technologies: AI engineers work with large amounts of data, so I’ll be required to know Apache Spark, Hadoop, and MongoDB.
🤖 Algorithms & frameworks: I'm currently self studying some machine learning algorithms such as linear regression and Naive Bayes,
🤖 as well as deep learning algorithms such as recurrent neural networks and generative adversarial networks, and be able to implement
🤖 them with a framework. Common AI frameworks include Theano, TensorFlow, Caffe, Keras, and PyTorch.
💬 Ask me about Artificial Intelligence and Machine Learning
🎮 I'm a Dallas Mavericks fan since 2011, guess my idol 🤫
 

🌐 Kindly visit my other GitHub profile: flexyledger for more content related to blockchain development
📫 How to reach me flexycode.dev@gmail.com, flexycode@protonmail.com, flexyledger@gmail.com

⚡Fun fact : I'm good at learning new things and adapting easily
⚡Fun fact : I always read documentation everyday before I begin to code
⚡Fun fact : I love Final Fantasy, Science Fiction, Biology, Architecture, Mutants and Galaxy Adventure

   

Connect with me on  

Languages & Tools  

python node java android react-native kotlin react vue  angular php golang rust solidity Github linux bash

💻 My favorite tools and technologies for Full Stack Devevelopment

icon
React
icon
Python
icon
JavaScript
icon
C++
icon
Webpack
icon
MySQL
icon
TypeScript
icon
AWS
icon
C#
icon
Django
icon
Github
Git
Git
Laravel
Laravel
HTML5
HTML5
css
CSS
bootstrap
Bootstrap
tailwind
Tailwind
jQuery
jQuery
MongoDB
MongoDB
Nodejs
Nodejs
PHP
PHP
VsCode
VsCode
WordPress
WordPress
Vue
Vue
Sass
Sass
MySQL
GraphQL
PostgreSQL
PostgreSQL


💻 My tools and technologies for Blockchain Devevelopment

ethereum ethereum-classic solidity openzeppelin rust bitcoin linux ubuntu remix hardhat

💻 My tools and technologies for AI and ML Devevelopment

python  R Julia  colab lua huggingface

💻 My tools and technologies for Game Devevelopment, Architecture and Visualization

C++ lua  autocad  directX  unreal-engine

💻 Some Technology Stacks that I used:

C++ Go GraphQL Rust Solidity TypeScript JavaScript HTML5 CSS3 Netlify Heroku Vercel Google Cloud AWS Azure DigitalOcean Bootstrap Fastify Gatsby Next JS NodeJS NPM React Redux React Router Threejs Svelte Semantic UI React Nginx MySQL Supabase MongoDB Affinity Designer Adobe Photoshop Confluence Kubernetes Docker Rancher                   

📈 GitHub Contribution:

GitHub Workflow Status GitHub release  GitHub marketplace type definitions code style

My GitHub contributions graph

snake gif

GitHub Stats  

Flexycode GitHub stats

Flexycode GitHub stats

Flexycode GitHub stats

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

🏆 GitHub Trophies  

   ♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

🏆 GitHub Repository

Gist Card ♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

Icons for Commiting message

Icons
♻️ - refactor getGithubUserContribution
✨ - read contribution calendar from github api or remove some attributes
📓 - commit or add readme
👷 - add manual run, repair
🚑 - import or also commit
🔨 - fix algorithm priority
🚀 - add emojis and style
🤫 - smiley face can also use for indicator for running or stopping some container
⛓️ - for linking file or repo
💱 - using solidity, hardhat or crypto related function
🧊 - blockchain
🌐 - networking setting, YAML file
📋 - List of Content

Some Programming Language & Technologies Icons for Commiting message

🖨️ Technologies Icons :

Flexycode Flexyledger
➡️ 🚍 Communication ➡️ 🧮 Fortran
➡️ 🧰 Version Control ➡️ ☎️ Erlang/Elixir
➡️ 🔨 Tools ➡️ 🧪 Testing
➡️ 🌐 Web Dev ➡️ 📱 Mobile Dev
➡️ 📜 JavaScript ➡️ ✨ UI/UX
➡️ ☕ Java ➡️ 🧊 Apache
➡️ ©️ C/C++ ➡️ 🎮 Game Development
➡️ 🪒 C# ➡️ 🔬 Analytics
➡️ 🐍 Python ➡️ 🤖 AI
➡️ 🐘 PHP ➡️ 💾 Database
➡️ 💎 Ruby ➡️ ☁️ Cloud
➡️ 🦾 Rust ➡️ 🖥️ Operating system
➡️ 🐿️ Go ➡️ 🤿 DevOps
➡️ 🍼 How to use this icons? ➡️ 🚶 Contribution

✍️ Random Dev Quote 

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

🧠🧠 Artificial Intelligence 🛸🛸

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️
♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️
# Import the necessary libraries for AI
import numpy as np
import pandas as pd 
import tensorflow as tf 

# Define the AI model architecture
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu', input_dim=10))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

# Compile and train the AI model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32)  

# Use the AI model for predictions
predictions = model.predict(X_test) 

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 

🌐⛓️ Blockchain Technology 💱🧊  

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 

🤖🦾 Machine Learning 📈💡

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 
# Import the necessary libraries for ML
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the dataset
data = pd.read_csv('data.csv') 
X = data.drop('target', axis=1)
y = data['target']

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
predictions = model.predict(X_test)

# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, predictions)

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️  ♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 

🔑🔐 Cryptography and Cybersecurity 🔒🕵️   

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️  

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