Jck's repositories
bayesla-link-adaptation
Bayesian Link Adaptation under a BLER Target
DRL_for_DDBC
Simulation codes for the manuscript "Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination" submitted to IEEE Transactions on Communications
aes
A basic AES implementation to perform the basic operations in Rijndael's finite field, using the extended Euclidean algorithm.
bandit_simulations
Bandit algorithms simulations for online learning
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
Deep-Reinforcement-Learning-for-5G-Networks
Code for my publication: Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination. Paper accepted for publication to IEEE Transactions on Communications.
Hands-On-Reinforcement-Learning-for-Games
Hands-On Reinforcement Learning for Games, published by Packt
Hands-On-Reinforcement-Learning-with-Python
Hands-On Reinforcement Learning with Python, published by Packt
Introduction-to-Machine-Learning
This repo will house all our course material and code snippets from the Introduction to Machine Learning Class
LearningX
Deep & Classical Reinforcement Learning + Machine Learning Examples in Python
markov-decision-problem
Learning about MDPs, implementing policies
Markov-Decision-Processes
Implementing Markov Decision Process from scratch in Python
MDP-Basics
Using MDP based models (Value Iteration and Policy Iteration) on toy environments.
ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
openwifi
open-source IEEE802.11/Wi-Fi baseband chip/FPGA design
Plotly-Dashboards-with-Dash
This is the repo for the Udemy Course Python Dashboards with Plotly's Dash
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
Q-Learning-Algorithm
Implemented deterministic FrozenLake ‘grid world’ problem where Q-learning agent learned a defined policy to optimally navigate through the lake. Python was used to program two classes which setup the state and agent respectively. Q-values are set state-action pairs and the algorithm chooses an optimal action for the current state based on estimates of this value. The reward and next state for this action is observed which allows for the Q value to be updated. Over many epochs this algorithm can learn the best path to take for this problem as long as the strategy balances exploration and exploitation correctly.
rfml
Radio Frequency Machine Learning with PyTorch
rl_notes
Notes of Reinforcement Learning MOOC by University of Alberta
srsLTE
Open source SDR LTE software suite from Software Radio Systems (SRS)
tau-epsilon-greedy-RL
The code for the article "(\tau,\epsilon)-GREEDY REINFORCEMENT LEARNING FOR ANTI-JAMMING WIRELESS COMMUNICATIONS"
ThinkDSP
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
Time-Series-Analysis
code and data for the time series analysis vids on my YouTube channel
verilog-starter-tutorials
Tutorial series on verilog with code examples. Contains basic verilog code implementations and concepts.
WhirlwindTourOfPython
The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"