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$ sudo add-apt-repository ppa:gnuradio/gnuradio-releases $ sudo apt-get update $ sudo apt-get install gnuradio How to access the GNU raido on Ubuntu after installation? This is usually done by opening up a terminal window (ctrl+alt+t in Ubuntu) and typing: $ gnuradio-companion

go to /usr/share/uniscan/report Happy finding !

When you do a system update with the update manager, the Virtualbox update should appear. You can also get the same effect by doing it via the terminal: sudo apt-get update sudo apt-get upgrade This should get you the latest version of Virtualbox. This however, will not get you the updated Virtualbox Extension Pack. The pack will only update when you open Virtualbox. You will immediately get a notification that there is an update for virtualbox or there is an update for the Extension Pack. Note that the extension pack is needed if you want proper support for sharing between host and guest, 3D support, USB support and much more. Now, in the case you have some update problems with Virtualbox, the easiest solution I have found is to download the latest version from the original site. You can find the latest Virtualbox version for Ubuntu in: https://www.virtualbox.org/wiki/Linux_Downloads(https://www.virtualbox.org/wiki/Linux_Downloads) It should show the versions of Ubuntu like in the following image: You can also find the latest Extension Pack in: https://www.virtualbox.org/wiki/Downloads(https://www.virtualbox.org/wiki/Downloads) After you download the newest Virtualbox version, simply double click on it and the Ubuntu Software Center will take care of everything else. I should mention that you can only install the Extension Pack to the same version of Virtualbox. So if you have Virtualbox X.X.10, you can only install the X.X.10 of the Extension Pack. After this, Virtualbox should notify you when there is a new update, either by doing an update of the system with the Update Manager or by opening Virtualbox when there is an update available.

Introduction to Machine Learning: Unraveling the Power of Data In an age where data is often referred to as the new gold, Machine Learning (ML) stands as the key that unlocks its hidden potential. Machine Learning is a transformative field that's rapidly shaping our world, from personalized recommendations on Netflix to self-driving cars on our roads. But what exactly is machine learning, and why is it so important? Let's embark on a journey to demystify this exciting technology. What is Machine Learning? At its core, Machine Learning is a subset of artificial intelligence (AI) that empowers computers to learn and make decisions from data without explicit programming. Traditional software relies on a set of rules and instructions, but ML takes a different approach. It learns from examples. Imagine you're building a spam email filter. Instead of writing rules to flag emails with specific keywords, ML enables you to feed the computer a large dataset of labeled emails (spam or not spam) and let it learn the patterns that distinguish the two. Once trained, the model can accurately classify new emails as spam or not spam, even if they contain previously unseen words or phrases. The Machine Learning Workflow To grasp the essence of ML, it's crucial to understand the typical ML workflow: 1. Data Collection and Preprocessing: Machine learning starts with data. You gather relevant data from various sources, ensuring it's clean, well-structured, and error-free. Data preprocessing tasks may include handling missing values, removing outliers, and transforming data into a suitable format. 2. Model Training and Evaluation: With clean data in hand, you choose an appropriate ML algorithm or model. During the training phase, the model learns from the data, adjusting its internal parameters to make accurate predictions. Evaluation is critical; you must assess how well the model performs on unseen data to avoid common pitfalls like overfitting or underfitting. 3. Deployment and Monitoring: Once you have a well-performing model, you deploy it into a real-world environment. Whether it's a recommendation system for an e-commerce website or an autonomous vehicle control system, continuous monitoring is essential to ensure the model's performance remains optimal and adapts to changing data patterns. Machine Learning vs. Traditional Programming One of the most profound distinctions between traditional programming and machine learning lies in how solutions are derived. In traditional programming, developers write explicit instructions and rules to solve a problem. These instructions are static and do not adapt to changing circumstances. Machine learning, however, embraces adaptability. ML systems learn and adapt from data, making them versatile and capable of handling complex tasks. For instance, while traditional translation programs rely on fixed rules and dictionaries, ML-based language translation models analyze vast datasets to produce translations that consider context, idiomatic expressions, and evolving language trends. This fundamental shift from rule-based programming to data-driven learning is what makes machine learning so powerful and versatile. Why Machine Learning Matters The pervasive nature of machine learning underscores its significance. It plays a role in countless aspects of our lives: - Recommendation Systems: Online platforms use ML to personalize content recommendations, improving user engagement. - Healthcare: Machine learning assists in diagnosing diseases, predicting patient outcomes, and discovering patterns in medical data. - Finance: Credit scoring, fraud detection, and algorithmic trading rely heavily on machine learning. - Autonomous Systems: Self-driving cars use ML to process sensor data and make real-time decisions, enhancing safety. - Natural Language Processing: Virtual assistants like Siri and chatbots employ ML to understand and respond to human language, making interactions more natural. These applications are just the beginning. As machine learning continues to evolve, its impact on society and technology will only grow. Conclusion Machine Learning is a technological marvel that's changing the way we interact with our digital world. In this introduction, we've explored the essence of machine learning, its workflow, and how it differs from traditional programming. We've also touched on its pervasive influence in various domains. As we journey deeper into the world of machine learning, you'll discover the various types of ML, explore practical examples, and gain hands-on experience. Whether you're a novice taking your first steps into this exciting field or a seasoned professional expanding your horizons, machine learning promises to be an exhilarating adventure filled with endless possibilities. Stay tuned for more insights and practical knowledge as we delve further into the realm of Machine Learning.

Hello everyone. Did you know that there is a useful tool for information gathering and recon right in your Chrome browser? Meet Google Dorking a way of searching for files or keywords in specific websites to find admin portals, unprotected files, logins, and more! Today I will teach you some basic “commands” for this tool. how to find filetypes on a specific website or keyword Using this you can find filetypes or names in a certain website. This can sometimes expose password lists, logins, and important files. To do so input this syntax: filetype:log or site:youtube.com (http://youtube.com)filetype:pdf or site:https://www.google.com/ (https://www.google.com/)ext:pdf Show all subdirectories or subpages in a website You can use this feature to find hidden pages or just get a more detailed view of a site site:domainname or site:https//domainname/ find a specific text result in a webpageYou can use a command like this to find login pages or info in the site or the sites source code intext:usernames or allintext:"username" "password" This was a tutorial on some simple Google Dorking commands. I hope that you find this helpful and have a great week!

Here is my opinion 1. AI/ML Education: Python is often recommended for educational purposes due to its simplicity. Many AI/ML courses and tutorials use Python, contributing to its popularity. 2. Availability of Pre-trained Models: Python has libraries that provide access to pre-trained AI/ML models, which can save significant development time and resources. 3. Job Market and Career Opportunities: Python's prominence in AI/ML has led to a high demand for Python-skilled AI/ML professionals. Learning Python enhances career prospects in these fields. 4. Ease of Use and Readability: Python's clear syntax and straightforward structure make it easy to read and write, making it accessible even for beginners. This reduces the learning curve for newcomers to AI/ML. 5. Rich Ecosystem of Libraries and Frameworks: Python boasts a vast collection of libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, and Keras, specifically designed for AI/ML tasks. These tools streamline development and provide ready-made solutions for complex tasks. 6. Community and Documentation: Python has a robust and active community of developers, researchers, and practitioners working in AI/ML. This results in extensive documentation, tutorials, forums, and open-source projects, aiding learning and troubleshooting. 7. Flexibility and Versatility: Python can be used for a wide range of AI/ML tasks, from data preprocessing and analysis to building complex models and deploying them in production systems. This versatility simplifies the entire workflow. 8. Data Analysis and Visualization: Python's libraries like NumPy, pandas, and Matplotlib offer powerful tools for data manipulation, analysis, and visualization, which are crucial components of AI/ML projects. 9. Rapid Prototyping and Experimentation: Python's dynamic nature allows for rapid development and experimentation. Developers can quickly prototype ideas, test hypotheses, and iterate on models with relative ease. 10. Machine Learning Frameworks: TensorFlow and PyTorch, two of the most popular deep learning frameworks, offer high-level abstractions and automatic differentiation, simplifying the creation of neural networks. 11. Support for Cloud Services: Python has strong integration with various cloud platforms, enabling seamless deployment of AI/ML models on cloud infrastructure. 12. Deployment and Integration: Python's compatibility with various platforms and programming languages facilitates model deployment and integration with existing software systems. In summary, Python's simplicity, rich ecosystem, strong community support, and applicability to various AI/ML tasks make it an ideal choice for developers and researchers working in the AI and machine learning domains.

Unhide files on Mac with Terminal commands Terminal, a Mac command-line interface, is included in macOS by default and allows you to use command prompts to control your Mac instead of following a potentially complex series of instructions to do the same thing in Finder. If you’re already familiar with using Terminal, then you might prefer to run the following script to reveal your hidden files: Open Terminal 1. Enter the following: defaults write com.apple.(http://com.apple)Finder AppleShowAllFiles true [Press Return] 2. Then type killall Finder 3. To hide files again, change the “true” in the step above to “false”

Linux: To change a Nessus user’s password in Linux, run the following command: Example: mynessususer is the username for which you want to change the password. # /opt/nessus/sbin/nessuscli chpasswd In the Login to change field, enter the username, and then enter the new password twice as prompted. The password will not appear on the screen as you type. Login to change: mynessususer New password: New password (again): Password changed for mynessususer Mac OSX Location will be /Library/Nessus/run/sbin/nessuscli To change a Nessus user’s password in Mac OSX, run the following command as root: Example: mynessususer is the username for which you want to change the password. Location to change password: # /Library/Nessus/run/sbin/nessuscli chpasswd In the Login to change field, enter the username, and then enter the new password twice as prompted. The password will not appear on the screen as you type. Login to change: mynessususer New password: New password (again): Password changed for mynessususer Windows To change a Nessus user’s password in Windows, log in to Windows with an account that has administrative privileges and run the following commands: Example: mynessususer is the username for which you want to change the password. cd c:\Program Files\Tenable\Nessus\ nessuscli.exe chpasswd mynessususer In the Login to change field, enter the username, and then enter the new password twice as prompted. The password will not appear on the screen as you type. New password: New password (again): Password changed for mynessususer