This Presentation covers Data Mining: Classification and Prediction, NEURAL NETWORK REPRESENTATION, NEURAL NETWORK APPLICATION DEVELOPMENT, BENEFITS AND LIMITATIONS OF NEURAL NETWORKS, Neural Networks, Real Estate Appraiser, Kinds of Data Mining Problems, Data Mining Techniques, Learning in ANN, Elements of ANN, Neural Network Architectures Recurrent Neural Networks and ANN Software.
This document discusses postfix notation for evaluating mathematical expressions. It begins by explaining that postfix notation, also called reverse polish notation, writes operators after their operands. It then covers the precedence of operators in postfix notation and the fundamental principles of evaluating a postfix expression using a stack. Key advantages of postfix notation are that operator precedence is unnecessary and the order of evaluation is determined by the position of operators and operands. An example is provided to demonstrate how to manually evaluate a postfix expression step-by-step using a stack.
The document discusses object-oriented testing strategies and techniques. It covers unit testing of individual classes, integration testing of groups of classes, validation testing against requirements, and system testing. Interclass testing focuses on testing collaborations between classes during integration. Test cases should uniquely identify the class under test, state the test purpose and steps, and list expected states, messages, exceptions, and external dependencies.
The document discusses for loops in Java including their syntax, examples of using for loops to print numbers, infinite loops, breaking and continuing loops, and nested for loops. It provides examples of using for loops to generate Fibonacci sequences, factorials, prime numbers, and nested patterns.
This document provides an overview of message authentication and integrity. It discusses the need for authentication in network security and outlines different authentication functions including message encryption, message authentication codes (MACs), and hash functions. It describes how MACs are generated using a secret key and message and provides the requirements for MACs. The document also discusses the MD5 and SHA hash algorithms, explaining their processes and analyzing their security strengths and weaknesses.
Slide presentasi ini dibawakan oleh Imron Zuhri dalam acara Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
The document discusses priority queues, which are data structures that allow elements to be inserted and removed based on priority. Elements with higher priority are served before those with lower priority. There are two main types - ascending order queues prioritize lower numbers, while descending order queues prioritize higher numbers. Priority queues can be implemented using linked lists, arrays, binary heaps, and binary search trees. Common applications include shortest path algorithms, heap sorting, and operating system processes.
Conditional statements in Java include if-else statements, nested if-else statements, and switch statements. If-else statements execute code based on a boolean condition, while switch statements allow testing multiple conditions. Type conversion in Java includes widening (automatic) conversions between compatible types like int to double, and narrowing (manual) conversions between incompatible types using explicit casting like double to int. Methods like parseInt() allow converting between types like String to int.
This is presentation, that covers all the important topics related to strings in python. It covers storing, slicing, format, concatenation, modification, escape characters and string methods.
The file attatched also includes examples related to the slides shown.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
The document discusses encapsulation in Java. It defines encapsulation as binding data and methods together under a single unit, and as a process of hiding information and protecting data through restricting access. It provides examples of how to implement encapsulation in Java by declaring class variables as private and providing public get and set methods to access and update private variables. The advantages of encapsulation discussed are data hiding, increased flexibility, reusability, and easier testing. Useful links on the topic are also provided.
NumPy is a library for working with multidimensional arrays and matrices in Python. It allows mathematical and logical operations on arrays to be performed. This tutorial explains the basics of NumPy, including its architecture, data types, array attributes, array creation, indexing and slicing, broadcasting, and array manipulation functions. The audience is those looking to learn the basics of NumPy, which is useful for algorithm developers. A basic understanding of Python is recommended.
The document discusses sorting algorithms. It begins by defining sorting as arranging data in logical order based on a key. It then discusses internal and external sorting methods. For internal sorting, all data fits in memory, while external sorting handles data too large for memory. The document covers stability, efficiency, and time complexity of various sorting algorithms like bubble sort, selection sort, insertion sort, and merge sort. Merge sort uses a divide-and-conquer approach to sort arrays with a time complexity of O(n log n).
What is Multithreading In Python | Python Multithreading Tutorial | EdurekaEdureka!
This document discusses multithreading in Python. It defines multitasking as the ability of an operating system to perform different tasks simultaneously. There are two types of multitasking: process-based and thread-based. A thread is a flow of execution within a process. Multithreading in Python can be achieved by importing the threading module. Multithreading is useful when multiple independent tasks need to be performed. The document outlines three ways to create threads in Python: without creating a class, by extending the Thread class, and without extending the Thread class. The advantages of multithreading include enhanced performance, simplified coding, and better CPU utilization.
The document discusses non-linear data structures, specifically trees. It provides definitions and terminology related to trees, including root, parent, child, ancestors, descendants, path, height, depth, degree, siblings, and subtrees. It also describes the tree abstract data type (ADT) and common tree traversal algorithms like level order traversal. Key topics covered include the tree ADT methods (accessor, generic, query, update), exceptions that may occur, and binary trees as a specific type of tree where each node has no more than two children.
This document discusses tuples in Python. Some key points:
- Tuples are ordered sequences of elements that can contain different data types. They are defined using parentheses.
- Elements can be accessed using indexes like lists and strings. Tuples are immutable - elements cannot be changed.
- Common tuple methods include count, index, sorting, finding min, max and sum.
- Nested tuples can store related data like student records with roll number, name and marks.
- Examples demonstrate swapping numbers without a temporary variable, returning multiple values from a function, and finding max/min from a user-input tuple.
The document discusses classical encryption techniques, including symmetric encryption which uses the same key for encryption and decryption. It describes ciphers like the Caesar cipher which substitutes letters by shifting the alphabet, the monoalphabetic cipher with one substitution table, and the polyalphabetic Vigenère cipher which uses multiple substitution alphabets. The document also covers the Playfair cipher which encrypts letters in pairs using a 5x5 keyword matrix, and discusses cryptanalysis techniques for breaking classical ciphers.
Quicksort is a sorting algorithm that works by partitioning an array around a pivot value, and then recursively sorting the sub-partitions. It chooses a pivot element and partitions the array based on whether elements are less than or greater than the pivot. Elements are swapped so that those less than the pivot are moved left and those greater are moved right. The process recursively partitions the sub-arrays until the entire array is sorted.
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
Linked lists are linear data structures where elements are linked using pointers. The three main types are singly, doubly, and circular linked lists. Linked lists allow dynamic memory allocation and fast insertion/deletion compared to arrays but slower access. A linked list contains nodes, each with a data field and pointer to the next node. Basic operations on linked lists include insertion, deletion, traversal, and search. Doubly linked lists include pointers to both the next and previous nodes.
Structure and union differ in how they allocate memory and access members. Structure allocates memory for all members and allows accessing all members at any time, while union allocates space for the largest member and only allows accessing one member at a time. Both use struct and union keywords respectively to declare them, with struct defining elements and union defining a single element that shares the same memory space.
RSA is an asymmetric cryptographic algorithm used for encrypting and decrypting messages. It uses a public key for encryption and a private key for decryption such that a message encrypted with the public key can only be decrypted with the corresponding private key. The RSA algorithm involves three steps: key generation, encryption, and decryption. It addresses issues of key distribution and digital signatures.
This document discusses the usage of command-line arguments in Java programs. Command-line arguments allow information to be passed into a program when it runs. In Java, command-line arguments are stored as strings in the string array passed to the main() method. An example program is provided that loops through the arguments and prints each one. Users are advised to convert arguments to the desired format using wrapper classes if needed. The key differences between command-line arguments in C and Java are also outlined.
The document discusses linear data structures and lists. It describes list abstract data types and their two main implementations: array-based and linked lists. It provides examples of singly linked lists, circular linked lists, and doubly linked lists. It also discusses applications of lists, including representing polynomials using lists.
Lists in Python allow storing and manipulating multiple items in a single variable. They can contain elements of different data types like strings, integers, and booleans. Lists can be accessed using indexes, sorted, copied, and joined. Common list methods include append(), insert(), remove(), pop(), sort(), and reverse() to add, remove, and rearrange list elements.
This document contains information about a mentoring program run by Baabtra-Mentoring Partner. It includes:
- A disclaimer that this is not an official Baabtra document
- A table showing a mentee's typing speed progress over 5 weeks
- An empty table to track jobs applied to by the mentee
- An explanation of sets in Python, including how to construct, manipulate, perform operations on, and iterate over sets
- Contact information for Baabtra
This is presentation, that covers all the important topics related to strings in python. It covers storing, slicing, format, concatenation, modification, escape characters and string methods.
The file attatched also includes examples related to the slides shown.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
The document discusses encapsulation in Java. It defines encapsulation as binding data and methods together under a single unit, and as a process of hiding information and protecting data through restricting access. It provides examples of how to implement encapsulation in Java by declaring class variables as private and providing public get and set methods to access and update private variables. The advantages of encapsulation discussed are data hiding, increased flexibility, reusability, and easier testing. Useful links on the topic are also provided.
NumPy is a library for working with multidimensional arrays and matrices in Python. It allows mathematical and logical operations on arrays to be performed. This tutorial explains the basics of NumPy, including its architecture, data types, array attributes, array creation, indexing and slicing, broadcasting, and array manipulation functions. The audience is those looking to learn the basics of NumPy, which is useful for algorithm developers. A basic understanding of Python is recommended.
The document discusses sorting algorithms. It begins by defining sorting as arranging data in logical order based on a key. It then discusses internal and external sorting methods. For internal sorting, all data fits in memory, while external sorting handles data too large for memory. The document covers stability, efficiency, and time complexity of various sorting algorithms like bubble sort, selection sort, insertion sort, and merge sort. Merge sort uses a divide-and-conquer approach to sort arrays with a time complexity of O(n log n).
What is Multithreading In Python | Python Multithreading Tutorial | EdurekaEdureka!
This document discusses multithreading in Python. It defines multitasking as the ability of an operating system to perform different tasks simultaneously. There are two types of multitasking: process-based and thread-based. A thread is a flow of execution within a process. Multithreading in Python can be achieved by importing the threading module. Multithreading is useful when multiple independent tasks need to be performed. The document outlines three ways to create threads in Python: without creating a class, by extending the Thread class, and without extending the Thread class. The advantages of multithreading include enhanced performance, simplified coding, and better CPU utilization.
The document discusses non-linear data structures, specifically trees. It provides definitions and terminology related to trees, including root, parent, child, ancestors, descendants, path, height, depth, degree, siblings, and subtrees. It also describes the tree abstract data type (ADT) and common tree traversal algorithms like level order traversal. Key topics covered include the tree ADT methods (accessor, generic, query, update), exceptions that may occur, and binary trees as a specific type of tree where each node has no more than two children.
This document discusses tuples in Python. Some key points:
- Tuples are ordered sequences of elements that can contain different data types. They are defined using parentheses.
- Elements can be accessed using indexes like lists and strings. Tuples are immutable - elements cannot be changed.
- Common tuple methods include count, index, sorting, finding min, max and sum.
- Nested tuples can store related data like student records with roll number, name and marks.
- Examples demonstrate swapping numbers without a temporary variable, returning multiple values from a function, and finding max/min from a user-input tuple.
The document discusses classical encryption techniques, including symmetric encryption which uses the same key for encryption and decryption. It describes ciphers like the Caesar cipher which substitutes letters by shifting the alphabet, the monoalphabetic cipher with one substitution table, and the polyalphabetic Vigenère cipher which uses multiple substitution alphabets. The document also covers the Playfair cipher which encrypts letters in pairs using a 5x5 keyword matrix, and discusses cryptanalysis techniques for breaking classical ciphers.
Quicksort is a sorting algorithm that works by partitioning an array around a pivot value, and then recursively sorting the sub-partitions. It chooses a pivot element and partitions the array based on whether elements are less than or greater than the pivot. Elements are swapped so that those less than the pivot are moved left and those greater are moved right. The process recursively partitions the sub-arrays until the entire array is sorted.
This describes the supervised machine learning, supervised learning categorisation( regression and classification) and their types, applications of supervised machine learning, etc.
Linked lists are linear data structures where elements are linked using pointers. The three main types are singly, doubly, and circular linked lists. Linked lists allow dynamic memory allocation and fast insertion/deletion compared to arrays but slower access. A linked list contains nodes, each with a data field and pointer to the next node. Basic operations on linked lists include insertion, deletion, traversal, and search. Doubly linked lists include pointers to both the next and previous nodes.
Structure and union differ in how they allocate memory and access members. Structure allocates memory for all members and allows accessing all members at any time, while union allocates space for the largest member and only allows accessing one member at a time. Both use struct and union keywords respectively to declare them, with struct defining elements and union defining a single element that shares the same memory space.
RSA is an asymmetric cryptographic algorithm used for encrypting and decrypting messages. It uses a public key for encryption and a private key for decryption such that a message encrypted with the public key can only be decrypted with the corresponding private key. The RSA algorithm involves three steps: key generation, encryption, and decryption. It addresses issues of key distribution and digital signatures.
This document discusses the usage of command-line arguments in Java programs. Command-line arguments allow information to be passed into a program when it runs. In Java, command-line arguments are stored as strings in the string array passed to the main() method. An example program is provided that loops through the arguments and prints each one. Users are advised to convert arguments to the desired format using wrapper classes if needed. The key differences between command-line arguments in C and Java are also outlined.
The document discusses linear data structures and lists. It describes list abstract data types and their two main implementations: array-based and linked lists. It provides examples of singly linked lists, circular linked lists, and doubly linked lists. It also discusses applications of lists, including representing polynomials using lists.
Lists in Python allow storing and manipulating multiple items in a single variable. They can contain elements of different data types like strings, integers, and booleans. Lists can be accessed using indexes, sorted, copied, and joined. Common list methods include append(), insert(), remove(), pop(), sort(), and reverse() to add, remove, and rearrange list elements.
This document contains information about a mentoring program run by Baabtra-Mentoring Partner. It includes:
- A disclaimer that this is not an official Baabtra document
- A table showing a mentee's typing speed progress over 5 weeks
- An empty table to track jobs applied to by the mentee
- An explanation of sets in Python, including how to construct, manipulate, perform operations on, and iterate over sets
- Contact information for Baabtra
This document provides step-by-step instructions for installing Python on Linux. It discusses selecting a Python version, downloading the installer from python.org, extracting and configuring the installer, making and installing Python, and testing the installation by printing "Hello World!". The instructions cover installing both Python 2 and Python 3 on Ubuntu systems.
This document provides instructions for setting up a virtual lab environment to explore network security monitoring and compliance standards. It involves creating two Ubuntu client VMs, a Security Onion VM for security monitoring, and installing Splunk on the Security Onion VM. Detailed steps are provided to install and configure Security Onion and Splunk, including enabling various security monitoring tools on Security Onion and downloading required Splunk apps. The purpose is to expose students to security monitoring details required by compliance standards and assist in creating a final logging standard.
This document provides an introduction and overview of using Python on the Raspberry Pi. It discusses that Python is a general purpose language created in the late 1980s that is supported on many operating systems and hardware, including the Raspberry Pi. It then provides tips and recommendations for learning Python, using popular Python libraries, virtual environments, best coding practices, and web development frameworks. Specific libraries and tools mentioned include IPython, Requests, Pandas, Matplotlib, Scikit-Learn, Bottle, Flask, and Django. Source code examples are also included.
Python modules allow programmers to split code into multiple files for easier maintenance. A module is simply a Python file with a .py extension. The import statement is used to include modules. Modules can be organized into packages, which are directories containing an __init__.py file. Popular third party modules like ElementTree, Psyco, EasyGUI, SQLObject, and py.test make Python even more powerful.
This is a python course for beginners, intended both for frontal class learning as well as self-work.
The Course is designed for 2 days and then another week of HW assignments.
This document provides instructions for setting up Python on Windows 10. It assumes the user has basic privileges on their computer. The steps outlined are for beginner Python users. It discusses downloading the correct Python version from python.org, installing it with default settings, and verifying the installation. It also covers installing additional Python packages both online and offline using pip and downloading source/wheel files. The document provides examples of installing common packages like NumPy, Pandas, and NLTK individually and multiple packages at once using a requirements.txt file.
This is the detail guide of MakePython ESP32, With the 12 experiments, the readers will be able to make simple electronic projects with MicroPython on ESP32, and the basic knowledge& setting up of IoT projects.
This is the detailed guide of MakePython ESP32 dev kit, With the 12 experiments, the readers will be able to make simple electronic projects with MicroPython on ESP32, and the basic knowledge& setting up of IoT projects.
How to Install Odoo 18 with Pycharm - Odoo 18 SlidesCeline George
In this slide we’ll discuss the installation of odoo 18 with pycharm. Odoo 18 is a powerful business management software known for its enhanced features and ability to streamline operations. Built with Python 3.10+ for the backend and PostgreSQL as its database, it provides a reliable and efficient system.
This explains the following questions.
1. How to install Aptana in Windows 8 64 bit os?
2. How to install Python in Windows 8 64 bit os?
3. How to install Django in Windows 8 64 bit os?
4. How to run a sample application in Django in Windows 8 64 bit os?
This will be useful for candidates who are learning Django framework.
JIMS Vasant Kunj-II, BCA Department teaches Python in the Second Semester.
JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR.
BCA Admissions 2022 are Open. Interested Students can apply.
Visit us at https://www.jimssouthdelhi.com/
BCA Course:https://www.jimssouthdelhi.com/course-bca.html
BCA Placements:https://www.jimssouthdelhi.com/bca-placement.html
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1. The document provides instructions on setting up a Python environment and programming in Python. It explains how to install Python on Windows, Mac, and Ubuntu operating systems.
2. The basics of Python programming are covered, including how to use print commands, arithmetic operators, variables, and formatting strings.
3. An exercise is provided to write a Python program to calculate the total number of fruits someone has before and after selling some quantities.
PVS-Studio Now Supports Any Build System under Windows and Any Compiler. Easy...Andrey Karpov
The next PVS-Studio version 5.15 will be able to analyze projects built by absolutely any build system: Makefile, Visual Studio Project, user-made build systems based on Python, Bash or whatever else... It will allow you to simply "watch" compiler calls and collect all the necessary information to run the analyzer in automatic mode. Moreover, it works with any (sensible) C/C++ compiler under Windows. Want to know more?
CrashCourse: Python with DataCamp and Jupyter for BeginnersOlga Scrivner
Crash course for beginners is based on Python Introduction by Philip Schowenaars from DataCamp and Jupyter Introduction adapted from Adapted from Pryke, B. (2018). Jupyter Notebook for Beginners: A Tutorial. DataQuest. https://www.dataquest.io/blog/jupyter-notebook-tutorial/
Penetrating Windows 8 with syringe utilityIOSR Journals
This document discusses penetrating Windows 8 remotely using Metasploit framework and syringe utility. It begins with an introduction to penetration testing and Windows 8 security. It then describes using Metasploit to generate a payload, encoding it to evade detection, and injecting it into a Windows 8 system using syringe. This allows establishing a meterpreter session and compromising the system by migrating processes and accessing the C drive. It concludes that Windows 8 has strong security but syringe injections allow compromising it, and more exploits could be found to enhance efficacy.
To ensure a smooth class, students should upgrade to the latest versions of R and RStudio. R version 3.6.2 and RStudio version 1.2.5033 are the latest. Users can check their versions by entering commands in the terminal or by selecting "Help" then "About RStudio" in RStudio. Upgrading instructions for Windows, Linux, and MacOS are provided.
This document provides instructions for installing R on various operating systems using different methods. For Windows, it describes using Anaconda, downloading from CRAN directly, or using Microsoft Open. For Mac, it provides instructions for downloading from CRAN or using Homebrew. For Linux, it recommends using apt-get to install from open labs. Across operating systems, the instructions recommend checking the R version after installation to confirm successful setup.
The document provides instructions for installing the reshape2 package in R on different operating systems. The preferred method is to install directly from R or RStudio using install.packages("reshape2") and library("reshape2"). For Linux (Ubuntu), commands are given to install via apt-get. For Mac, specific commands using install.binaries are provided. For Windows, multiple commands using install.binaries from different CRAN links are listed as alternatives.
The document provides instructions for installing the dplyr package in R using different methods. The preferred method is to use install.packages("dplyr") in RStudio. If that does not work, alternatives include installing from GitHub using devtools or installing via CRAN, with operating system-specific commands for Ubuntu, Mac, and Windows.
This document provides instructions for installing the ggplot2 package in R on different operating systems. The easiest method is to use install.packages("ggplot2") in RStudio. If that does not work, an alternative is to install from GitHub using devtools::install_github("tidyverse/ggplot2"). For different operating systems like Ubuntu, Mac, and Windows, there are additional commands provided to install via CRAN.
Python is an old programming language that has gained new popularity for machine learning. It exists in two versions, Python 2 and Python 3. The tutorial explains how to install both versions on a Mac by downloading them from python.org, running through an interactive installation process, and testing the installation by running sample Python code in the terminal.
This document provides instructions for installing Python version 2 or 3 on Windows. It explains that Python is commonly used for machine learning and deep learning. The user should select the version based on compatibility with their intended projects. The steps then outline downloading the installer from python.org, selecting options during installation like adding to the system PATH, and testing the installation by checking the version and running a simple print command in the terminal.
This document provides information about past events and expertise in the fields of data science, machine learning, deep learning, Python, R programming, and data science. It notes that a past event in Bangalore on AI and machine learning included an award for best paper on security at an international conference.
Tijn van der Heijden is a business analyst with Deloitte. He learned about process mining during his studies in a BPM course at Eindhoven University of Technology and became fascinated with the fact that it was possible to get a process model and so much performance information out of automatically logged events of an information system.
Tijn successfully introduced process mining as a new standard to achieve continuous improvement for the Rabobank during his Master project. At his work at Deloitte, Tijn has now successfully been using this framework in client projects.
Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation.
Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.
This comprehensive Data Science course is designed to equip learners with the essential skills and knowledge required to analyze, interpret, and visualize complex data. Covering both theoretical concepts and practical applications, the course introduces tools and techniques used in the data science field, such as Python programming, data wrangling, statistical analysis, machine learning, and data visualization.
Defense Against LLM Scheming 2025_04_28.pptxGreg Makowski
https://www.meetup.com/sf-bay-acm/events/306888467/
A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold.
While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence?
The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces.
However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces.
Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)
Telangana State, India’s newest state that was carved from the erstwhile state of Andhra
Pradesh in 2014 has launched the Water Grid Scheme named as ‘Mission Bhagiratha (MB)’
to seek a permanent and sustainable solution to the drinking water problem in the state. MB is
designed to provide potable drinking water to every household in their premises through
piped water supply (PWS) by 2018. The vision of the project is to ensure safe and sustainable
piped drinking water supply from surface water sources
GenAI for Quant Analytics: survey-analytics.aiInspirient
Pitched at the Greenbook Insight Innovation Competition as apart of IIEX North America 2025 on 30 April 2025 in Washington, D.C.
Join us at survey-analytics.ai!
This project demonstrates the application of machine learning—specifically K-Means Clustering—to segment customers based on behavioral and demographic data. The objective is to identify distinct customer groups to enable targeted marketing strategies and personalized customer engagement.
The presentation walks through:
Data preprocessing and exploratory data analysis (EDA)
Feature scaling and dimensionality reduction
K-Means clustering and silhouette analysis
Insights and business recommendations from each customer segment
This work showcases practical data science skills applied to a real-world business problem, using Python and visualization tools to generate actionable insights for decision-makers.
How to Install numpy, scipy, matplotlib, pandas and scikit-learn on Windows
1. How to Install Numpy, Scipy,
Matplotlib, Pandas & Scikit-Learn
on Windows
Python comes loaded with powerful packages that make machine learning tasks easier. This is
why it is the language of choice among data scientists. Of the vast collection of libraries that you
can choose from, there are a set of basic libraries you should be familiar with as a beginner.
In this tutorial we are going to install these basic libraries on our system using Python’s built in
package manager PIP.
Numpy:
NumPy (stands for Numerical Python) provides useful features for operations on n-arrays and
matrices in Python. It provides vectorization of mathematical operations on the NumPy array type.
Installation:
1. In the terminal type the command pip install numpy
2. For security reasons, you will be asked to enter your password.
3. Installation will take only a few seconds.
Numpy is now installed on your system.
Testing:
1. In the terminal, start Python by typing the command python
2. Use the following error handling block:
try:
import numpy
except ImportError:
print (“numpy is not installed”)
3. If numpy is installed successfully, then you will not get any message in the terminal.
Otherwise you will get an error message saying “numpy is not installed”.
Troubleshooting:
If you get the error message, try this command pip install -U numpy
Scipy:
SciPy contains modules for linear algebra, optimization, integration, and statistics. It is built upon
NumPy. It provides efficient numerical routines as numerical integration, optimization, and more
via specific submodules.
Installation:
2. 1. In the terminal type the command pip install scipy
2. For security reasons, you will be asked to enter your password.
3. Installation will take only a few seconds.
Scipy is now installed on your system.
Testing:
1. In the terminal, start Python by typing the command python
2. Use the following error handling block:
try:
import scipy
except ImportError:
print (“scipy is not installed”)
3. If scipy is installed successfully, then you will not get any message in the terminal.
Otherwise you will get an error message saying “scipy is not installed”.
Troubleshooting:
If you get the error message, try this command pip install -U scipy
Matplotlib:
It is used for the generation of simple and powerful visualizations.
You can make just about any visualizations such as bar charts, histograms &
pie charts. There are facilities for creating labels, grids and other formatting elements.
Installation:
1. In the terminal type the command pip install matplotlib
2. For security reasons, you will be asked to enter your password.
3. Installation will take only a few seconds.
Matplotlib is now installed on your system.
Testing:
1. In the terminal, start Python by typing the command python
2. Use the following error handling block:
try:
import matplotlib
except ImportError:
print (“matplotlib is not installed”)
3. If matplotlib is installed successfully, then you will not get any message in the terminal.
Otherwise you will get an error message saying “matplotlib is not installed”.
Troubleshooting:
If you get the error message, try this command pip install -U matplotlib
3. Pandas:
Pandas works with “labeled” and “relational” data. Pandas is primarily used for data wrangling. It
was designed for quick and easy data manipulation, aggregation, and visualization.
Installation:
1. In the terminal type the command pip install pandas
2. For security reasons, you will be asked to enter your password.
3. Installation will take only a few seconds.
Pandas is now installed on your system.
Testing:
1. In the terminal, start Python by typing the command python
2. Use the following error handling block:
try:
import pandas
except ImportError:
print (“pandas is not installed”)
3. If pandas is installed successfully, then you will not get any message in the terminal.
Otherwise you will get an error message saying “pandas is not installed”.
Troubleshooting:
If you get the error message, try this command pip install -U pandas
Scikit-Learn:
This package is built on the top of SciPy and makes heavy use of its mathematical operations.
It provides access to common machine learning algorithms, making it simple to bring machine
learning into any project. It is easy to use and is great for playing around with machine learning
concepts.
Installation:
1. In the terminal type the command pip install scikit-learn
2. For security reasons, you will be asked to enter your password.
3. Installation will take only a few seconds.
Scikit-Learn is now installed on your system.
Testing:
1. In the terminal, start Python by typing the command python
2. Use the following error handling block:
try:
import scikit-learn
except ImportError:
print (“scikit-learn is not installed”)
4. 3. If scikit-learn is installed successfully, then you will not get any message in the terminal.
Otherwise you will get an error message saying “scikit-learn is not installed”.
Troubleshooting:
If you get the error message, try this command pip install -U scikit-learn
You are now armed with the basic tools that you need to begin your data science journey.