Introduction to Quantum Computing Lecture 1: Fundamentals

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Quantum computing has the potential to transform cybersecurity. Some encryption algorithms are thought to be unbreakable, except by brute-force attacks. Although brute-force attacks may be hard for classical computers, they would be easy for quantum computers making them susceptible to such attacks. All financial institutions, government agencies healthcare information are in danger. How could this new thrust of computing strength give us new tiers of power to analyze IT systems at a more granular level for security vulnerabilities and protect us through more complex layers of quantum cryptography?

Cyber Security and Post Quantum Cryptography By: Professor Lili Saghafi

Professor Lili Saghafi

A confusion matrix is a matrix (table) that can be used to measure the performance of an machine learning algorithm, usually a supervised learning one. Each row of the confusion matrix represents the instances of an actual class and each column represents the instances of a predicted class. This is the way we keep it like this but it can be the other way around as well, i.e. rows for predicted classes and columns for actual classes.

Machine learning by using python lesson 3 Confusion Matrix By : Professor Lil...

Professor Lili Saghafi

When we say "Neural Networks", we mean artificial Neural Networks (ANN). The idea of ANN is based on biological neural networks like the brain. The basic structure of a neural network is the neuron. A neuron in biology consists of three major parts: the soma (cell body), the dendrites, and the axon. The dendrites branch of from the soma in a tree-like way and getting thinner with every branch. They receive signals (impulses) from other neurons at synapses. The axon - there is always only one - also leaves the soma and usually tend to extend for longer distances than the dentrites. The axon is used for sending the output of the neuron to other neurons or better to the synapsis of other neurons.

Machine learning by using python lesson 2 Neural Networks By Professor Lili S...

Professor Lili Saghafi

Machine learning by using python Lesson One Part 2 By Professor Lili Saghafi

Professor Lili Saghafi

Machine learning is the kind of programming which gives computers the capability to automatically learn from data without being explicitly programmed. This means in other words that these programs change their behavior by learning from data. In this course we will cover various aspects of machine learning Of course, everything will be related to Python. So it is Machine Learning by using Python. What is the best programming language for machine learning? Python is clearly one of the top players! k-nearest Neighbor Classifier Neural networks Neural Networks from Scratch in Python Neural Network in Python using Numypy Dropout Neural Networks Neural Networks with Scikit Machine Learning with Scikit and Python Naive Bayes Classifier Introduction into Text Classification using Naive Bayes and Python

Machine learning by using python By: Professor Lili Saghafi

Professor Lili Saghafi

Explain the term "digital humanities," and how it is understood across humanities disciplines. Describe the research journey as a partnership between researcher and library collections and staff. List examples of the limits of classification. Describe the implicit and explicit hierarchies that are created when gathering and analyzing data. Distinguish between what counts as data and what does not. Identify different data formats and how they fit into a research workflow.

What is digital humanities ,By: Professor Lili Saghafi

Professor Lili Saghafi

Effective Algorithm for n Fibonacci Number By: Professor Lili Saghafi

Professor Lili Saghafi

DOS / DDOS introduction How Easy it is to get information Real Life Examples MyDoom , GitHub , Dyn , Windows Server and Windows 10 servers running Internet Information Services (IIS) are vulnerable to denial of service (DOS) attacks Base of Attacks Types of DOS / DDOS Attack Tools , LOIC, XOIC, Stacheldracht DOS/DDOS Weaknesses Category of OS/ DDOS What to defend? Botnets and Botnets mitigations Michael Calce, a.k.a. MafiaBoy Point of entrance / OSI Model ( If time permit)

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Professor Lili Saghafi

Data Science unit 2 By: Professor Lili Saghafi

Professor Lili Saghafi

In This Data Science course ( Graduate Program ) I will focus on understanding business intelligence systems and helping future managers use and understand analytics, Business Intelligence emphasizing the applications and implementations behind the concepts. a solid foundation of BI that is reinforced with hands-on practice. The course is also designed as an introduction to programming and statistics for students from many different majors. It teaches practical techniques that apply across many disciplines and also serves as the technical foundation for more advanced courses in data science, statistics, and computer science.

Data science unit 1 By: Professor Lili Saghafi

Professor Lili Saghafi

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Professor Lili Saghafi

Sorting is a classic subject in computer science. There are three reasons for studying sorting algorithms. First, sorting algorithms illustrate many creative approaches to problem solving Second, sorting algorithms are good for practicing fundamental programming techniques using selection statements, loops, methods, and arrays. Third, sorting algorithms are excellent examples to demonstrate algorithm performance.

Quick Sort By Prof Lili Saghafi

Professor Lili Saghafi

Quantum Computing Discovery Reveals Time Moves Forward And Backward. Time behaves differently in quantum computing models. What is true at the quantum level is true for the rest of the universe as well.Using theoretical quantum computers, the researchers found that Causal Asymmetry does not exist in quantum models. They say that what applies to a quantum computer also applies to massive objects in the universe.

Arrow of time by Professor Lili Saghafi

Professor Lili Saghafi

Pointers by: Professor Lili Saghafi

Professor Lili Saghafi

Just like a classical internet a quantum internet consists of computers attached to an internet. In the case of a quantum internet these are naturally quantum computers. On a quantum internet we don’t send classical bits, 0’s and 1’s, but we will transmit qubits.What makes a quantum internet, or what makes the transmission of qubits so much more powerful than what we have today? Qubits have very special features They cannot be copied, making them ideal for security applications. Two qubits can also be in a very special state: namely an entangled state. An entangled state between two qubits is the essence of the power of a quantum internet.

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Many occasion students are wondering which one should they choose in coding and why .In most cases, we want to avoid inheritance. We should use abstract classes only when we want to include functionality, but parts of the class's purpose need to be implemented by the child class.With abstract classes, you are forcing other developers to implement child classes using inheritance. Interfaces are much easier to add to existing classes, and with defender methods there is little reason to not consider interfaces first.

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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

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