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Hi there 
, I'm Jay
💎 Hello there! I'm thrilled to have you visit my GitHub profile. Here, you'll find a collection of my projects, contributions, and explorations in the world of software development 💎
💎 Welcome to my GitHub Profile 💎
🔭 I’m currently studying on Artificial Intelligence, Machine Learning, Quantum Technology, A.I Cloud and Biology.
📓 I am currently a 4th year student pursuing a BS in Computer Science with a specialization in Machine Learning at National University.
📓 I'm planning to take a Master Degree for Artificial Intelligence or BioInformatics by the year of 2027
🧠 In addition to my studies at National University, I am also enrolled in the "CS50’s
🧠 Introduction to Artificial Intelligence with Python" course at Harvard University.
🧊 I have accumulated 8 years of experience as a Software Engineer and Blockchain Developer, starting from 2018.
👯 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
🌐 I’m also exploring some revolutionary technology such as Web 4.0, Generative AI, IoT, Cloud Computing and Augmented Reality
🦾 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 and write documentation everyday before I begin to code
⚡Fun fact : I love Final Fantasy, Science Fiction, Biology, Architecture, Astrology, Mutants and Galaxy Adventure
⚡Fun fact : I also play League of Legends, Teamfight Tactics, Wild Rift, Legends of Runeterra, NBA2K
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React |
Python |
JavaScript |
C++ |
Webpack |
MySQL |
TypeScript |
AWS |
C# |
|
Django |
Github |
Git |
Laravel |
HTML5 |
CSS |
Bootstrap |
Tailwind |
jQuery |
|
MongoDB |
Nodejs |
PHP |
VsCode |
WordPress |
Vue |
Sass |
GraphQL |
PostgreSQL |
| Certification | Verify | |
|---|---|---|
| 🎓 | Legacy Full Stack | View |
| 🎓 | Legacy Responsive Web Design V8 | View |
| 🎓 | Legacy JavaScript Algorithms and Data Structures V7 | View |
| 🎓 | Back End Development and APIs V8 | View |
| 🎓 | Legacy Information Security and Quality Assurance | View |
| 🎓 | Intro to Python for Data Science Course | View |
♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️
♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️
| 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 |
| 🔥 - Published Paper Link |
| 👀 - Getting Started |
| 💻 - System Overview or Architecture Image |
| ⚡ - Our Solution |
| ⏯️ - Explanation + Demo Video |
| 💊 - Pharmaceutical |
| 🔒 - LICENSE |
 - GitHub Project Link |
🖨️ 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 |
🖨️ Development Icons :
| Fullstack | Blockchain |
|---|---|
| ➡️ ⚡ Next.js 15 | ➡️ 🧩 Component Library |
| ➡️ 🎨 Tailwind CSS | ➡️ 🎮 AI Playground |
| ➡️ 📘 TypeScript | ➡️ 📊 Dashboard Template |
| ➡️ 🔒 Authentication | ➡️ 🔍 SEO Optimized |
| ➡️ 🎭 Shadcn/ui | ➡️ ✨ UI/UX |
| ➡️ 💾 Convex DB | ➡️ 🎬 Custom Video Player |
| ➡️ 💳 Polar.sh | ➡️ 🎮 Game Development |
| ➡️ 🚀 Route Prefetching | ➡️ 📝 Blog Support |
| ➡️ 🖼️ Optimized Images | ➡️ 🔄 State Management |
| ➡️ 🌓 Dark/Light Mode | ➡️ 💾 Database |
| ➡️ 📱 Responsive Design | ➡️ ☁️ Cloud |
| ➡️ 💾 State Persistence | ➡️ 🌐 API Integration |
| ➡️ 🔄 Real-time Updates | ➡️ 🤿 DevOps |
| ➡️ 🍼 How to use this icons? | ➡️ 🚶 Contribution |
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# 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)
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# 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)
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