Statistics – Introduction to Graphs
Book – Statistics an Introduction, 4th Edition, Roger E. Kirk, Page 42 Shapes of distributions.
Calculus – Derivatives
Book Calculus by Thomas/Finney, 9th Edition, Page 109 Derivates is one of the most important concept in calculus. It measures the rate at which a function changes. It is defined actually as the limit of secant slopes which is the slope of curve at a point. Derivative of the funtion $f$ with respect to variable ... Read More
Calculus – Limits and Continuity
Book Calculus by Thomas/Finney, 9th Edition; Page 51 Limit: One of the main concept that differentiates calculus with algebra and trigonometry (page 51). Core concepts: limit, limits to describe how different functions changes or varies (continuously or erratically), how limits can be used to define tangent lines to the graph of the functions, derivative of ... Read More
Calculus – Trignometric Functions
Book: Calculus by Thomas/Finney. 9th Edition Radian is a unit of measurment. In calculus, angle is measured in radian. (Navigation and astronomy uses degrees for angles) – page 35 Radian measure $\theta$ is given by $\frac{s}{r}$ wher $s$ is arc length AB and $r$ is the radius. Six basic trigonometric functions. Some other functions. For ... Read More
Federated Learning Algorithm Averaging Methods FedAvgM, FedProx, FedAvg
FedAvg: https://arxiv.org/abs/1602.05629 __________________________________________________________________________________________________________ FedAvgM: https://arxiv.org/pdf/1909.06335 stands for Federated Averaging with Server Momentum. It is an upgrade to the original FedAvg algorithm designed specifically to solve the “Client Drift” problemwhere the model gets confused because different users have very different data (Non-IID). The Formula: How it works mathematically The FedAvgM update happens in three steps on ... Read More
Introduction to statistics
Book: Statistics – An Introduction (4th Edition) – Roger E. Kirk What is statistics? Why statistics? Basic Concepts Terms: Types of Statistics: Descriptive and Inferential Random Sampling – draw sample from population; chance of drawing sample has equal chance Characteristics: Variable and Constant Measurment: Assign number to characteristics of people etc.
Deutsch’s Algorithm
https://quantum.cloud.ibm.com/learning/en/courses/fundamentals-of-quantum-algorithms/quantum-query-algorithms/deutsch-algorithmhttps://en.wikipedia.org/wiki/Deutsch%E2%80%93Jozsa_algorithm Implementaion link: https://github.com/computingnotes/QuantumAlgorithmsUsingQiskit/blob/main/deutsch_algorithm_using_qiskit.ipynbGitHub: https://github.com/computingnotes deutsch_algorithm_using_qiskit (1) Source: https://quantum.cloud.ibm.com/learning/en/courses/fundamentals-of-quantum-algorithms/quantum-query-algorithms/deutsch-algorithm Deutsch’s Algorithm Implementation in Qiskit¶ In [45]: # --- 1. Installation and Setup --- # Install necessary libraries silently !pip install qiskit qiskit_aer pylatexenc -q In [46]: from qiskit import QuantumCircuit from qiskit_aer import AerSimulator In [47]: # Imports from qiskit import QuantumCircuit from qiskit_aer import AerSimulator from typing ... Read More
Quantum Parallelism
Quantum Computation and Quantum Information, Machael A. Nielsen, Isaac L. Chuang Quantum Parallelism is foundational technique that gives quantum computers their potential speed advantage. It explains how a quantum computer can evaluate a function f(x) for many different input values x simultaneously, a feat impossible for a classical computer. The oracle Uf maps the basis ... Read More
Deep Reinforcement Learning Parameters
Epsilon (ϵepsilon 𝜖): This is a hyperparameter (a value between 0 and 1) that defines the probability of an agent taking a random action (exploration) instead of the action believed to have the highest reward (exploitation). Epsilon Decay: This is the process of gradually reducing the value of 𝜖 over time or a number of ... Read More
All Machine Learning MOdels
https://www.ibm.com/think/topics/machine-learning-types Machine learning models are grouped in one of 5 types, supervised, unsupervised, self-supervised, semi-supervised and reinforcement learning. Machine learning libraries, useful.