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1. Introduction to Artificial Intelligence 2. What is Generative AI? 3. Generative AI: The Evolution of Thoughtful Online Search 4. Get Your Work Done with Bing Chat 5. Ethics of Artificial Intelligence Generative Your interaction encourages us to provide you with more ️ ENJOY & HAPPY LEARNING!

Welcome to all new members, thank you for joining us and looking forward to us! Enjoy your stay and never forget to read the FAQ and rules, it shall help you to move around easily

- Title fixed and corrected - Topic Moved to its correct category

CBTNuggets - OpenJS Node.js Application Developer (JSNAD) Course details This intermediate OpenJS Node.js Application Developer (JSNAD) training prepares software developers to build high-performance Node.js applications, integrate databases and test and debug apps for production readiness. This JSNAD exam prep outlines the topics you'll find on the OpenJS Node.js Application Developer exam. It will also give you lots of opportunities to implement buffer and streams, control flow, error handling, the module system, unit testing — as well as help you earn your JSNAD certification. What you'll learn - Designing and developing web applications in Node.js - Implementing and navigating Node.js core APIs - Debugging Node.js - Managing asynchronous operations - Controlling JS and Node.js processes Who is this for? This OpenJS Node.js Application Developer (JSNAD) training is considered professional-level JavaScript training, which means it was designed for software developers. This Node.js skills course is designed for software developers with three to five years of experience with back-end JavaScript runtime environment. General Details: Instructor: Shaun Wassell Duration: 32h 2m 33s Updated: 11 July 2024 Language: English Source: [Hidden Content] MP4 | Video: AVC, 1920x1080p | Audio: AAC, 48.000 KHz, 2 Ch [Hidden Content]

Coursera - Python and Statistics Foundations 2025 What you'll learn - Write Python programs using core concepts like variables, data types, and control flow. - Apply NumPy and Pandas to manipulate and analyze data efficiently. - Create insightful data visualizations using Matplotlib, Seaborn, and Plotly for effective reporting. - Perform statistical analysis and probability tests to solve data-driven problems and validate hypotheses. There are 4 modules in this course This course introduces Python programming and fundamental statistics concepts, equipping learners with essential skills for data-driven roles in tech and AI. Through hands-on experience, you'll learn how to manipulate data, visualize insights, and apply statistical techniques for data analysis. By the end of this course, you will be able to: - Understand and apply Python programming concepts such as data types, operators, and control structures - Manipulate data using popular libraries like NumPy and Pandas - Visualize data with Python libraries such as Matplotlib, Seaborn, and Plotly - Analyze data using statistical techniques, including measures of central tendency, dispersion, and probability - Perform hypothesis testing and draw insights from the data This course is designed for beginners, data enthusiasts, and aspiring data scientists who want to build a strong foundation in Python programming and statistical analysis. No prior programming experience is required, although familiarity with basic statistics will be helpful. Join us to start your journey into data analysis and programming with Python! General Details: Duration: 5h 50m 51s Updated: 3/2025 Language: English Source: [Hidden Content] Instructor: [Hidden Content] MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch [Hidden Content]

Welcome home mate, good to see you here, have fun and enjoy your stay!

How to protect yourself from digital fraud? With the increase in digital fraud attempts, it's essential to know how to act smart to protect your data. Here are the most important steps: 1- Check before you react! If you receive a suspicious message, don't rush to respond. Verify the source through official channels before taking any action. 2- Beware of unknown links! Don't click on any unfamiliar link without verifying its authenticity, as it may be an attempt to hack your data. 3- Activate two-factor authentication! Strengthen the security of your accounts by enabling two-factor authentication, as it adds an additional layer of protection against hacks. 4- Monitor your accounts constantly! Monitor your bank and digital accounts regularly, and if you notice any suspicious activity, take appropriate action immediately. 5- Change your passwords regularly! Use strong, diverse passwords, and change them periodically to keep your accounts secure. Always remember: Don't trust anyone you don't trust, and when in doubt, check before you act. Share these tips to protect yourself and those around you from digital fraud!

So, it sounds to me like this every time someone jumps without considering anything such..... 1. Your Username at GloDLS: whatever you call 2. Content you want to upload: I planned to post music this Saturday, Sunday maybe e-books, next week applications, on April 23th i might be assumed to post TV show, on my birthday I will dump without you knowing none. 3. Do you have account to cross-check: Good question, yes, he/she uploaded my torrents, you can find Avengers 2012 at 1337x it was me who first uploaded it, go to this no name no best no known site lime lemon.xd.lol.rolf.com and search me with tv show which I last watched alone. 4. Provide 2 profile links of existing sites: Youtube, Google.com.it's me, trust me. 5. Tell us why your application should be approved: You dare, Because I want you to approve without giving me a shit. I have read the rules and agree to them: WTF, what rules, who read that crap?. I'm just kidding, please, make it real if you want us to deal with it normally! LOL, I told you it's just a fun post to help newbies, Welcome onboard and continue uploading, feel free to contact me for any assistance

Motion Design School - Rigging and Animation with Moho Course details Character Rigging and Animation in MOHO. Starting from program features to animating a full scene for your videos. Course by Pawel Granatowski What is this Moho course about? Character rigging and animation in MOHO. You will learn all important features of MOHO, rigging 2,5d characters, use of smartphone actions, practical use of principles of animation in cut-out character animation, and how to animate a full scene for your explainer video. - Learn the pipeline of the Moho - Get familiar with Rigging - Learn advanced techniques General Details: Duration: 2h+ Updated: 03/2025 Language: English Material: Included Everything Source: [Hidden Content] MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch [Hidden Content]

Coursera - Practical Deep Learning With Python 2025 Course details Welcome to the Practical Deep Learning with Python course, where you'll gain hands-on experience with cutting-edge deep learning techniques to model and ... What you'll learn - Understand the core components of deep learning models and their role in AI. - Apply CNN, R-CNN, and Faster R-CNN for object detection tasks. - Implement RNNs and LSTMs for sequential data processing. - Optimize and evaluate deep learning models for improved performance. There are 4 modules in this course Welcome to the Practical Deep Learning with Python course, where you'll gain hands-on experience with cutting-edge deep learning techniques to model and analyze complex datasets. Unlock the power of deep learning to solve real-world problems and uncover actionable insights from massive data volumes. This course explores industry-specific applications and equips you with the practical skills needed to build and optimize advanced models. By the end of this course, you’ll be able to: - Describe the foundational components of deep learning models and their significance in artificial intelligence. - Illustrate the working of CNNs, R-CNNs, and Faster R-CNNs for object detection and related applications. - Understand the limitations of Perceptrons and how Multi-Layer Perceptrons (MLPs) address them. - Implement Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures for sequential data analysis. - Optimize and evaluate deep learning models to achieve higher accuracy and efficiency. This course is designed for data scientists, machine learning engineers, and AI enthusiasts with a foundational knowledge of Python and machine learning who aim to expand their expertise in deep learning. Experience in building machine learning models, along with knowledge of statistics and proficiency in Python programming, is recommended for this course. Embark on this educational journey to enhance your expertise in deep learning and elevate your capabilities in building intelligent systems for the future of artificial intelligence. General Details: Duration: 6h 10m Updated: 03/2025 Language: English Source: [Hidden Content] Instructor: [Hidden Content] MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch [Hidden Content]

Indeed, I couldn't agree to it more what exactly battlestar had just said I was research to at least find rumor's post anywhere on the internet to keep a hope, but honestly, it really don't feel good, as far my experience, it's been more than 10 days, any group who wants to stay they would not take that longer to fix the thing such as host, server, or any manner bug or say glitch, 10 days are enough to recover the entire website, in case it was hacked? Or supposed to be rebuilt? 10 days are enough I believe, though, there might be 2 reasons if they don't get back, 1, they don't want to keep it or invest on something that doesn't fulfil their need (which obvious), 2, there is no 2 because it's 2025, nothing hard to get anything back to work, no matter you need a coder, developer, new server, host, domain or even a hacker, so, as far I concerned, reason 1 is obvious, if that's not the cause, then they are out of BUDGET, every task starts and ends here (these are thoughts, I wish they prove me wrong 100%) If I see past, it's similar to every last stand good P2P sources that we had lost recently, Pirateiro, ettv, prostylex, dnoid, same issue budget issue, in fact, KAT/ET had different cause. Anyway, Torrentgalaxy is must need for most of the pirates around the globe, I wish it come back otherwise it shall be uncompleted story for all of us which left no such sense > why they left just like that! > why, many questions remain unsolved!

Da besta musicino week Thank you so much!

Edureka - Applied Machine Learning With Python 2025 Course details This course provides an in-depth, hands-on introduction to machine learning using Python. You'll explore core concepts and methods, diving into supervised, unsupervised, and semi-supervised learning. Through practical exercises and examples, you'll master key algorithms including decision trees and random forests for classification, regression for predictive modeling, and K-means clustering for uncovering hidden patterns in unlabeled data. Additionally, you’ll gain insights into using model-boosting techniques to enhance model accuracy and apply strategies for leveraging unlabeled data effectively. By the end of this course, you’ll be able to: - Explain and implement decision trees and random forests as classification algorithms. - Define and differentiate various types of machine learning algorithms. - Analyze the working of regression for predictive tasks. - Apply K-means clustering to explore and discover patterns in unlabeled data. - Strategically use unlabeled data to improve model training. - Manipulate boosting algorithms to achieve higher model accuracy. This course is ideal for learners with foundational knowledge in Python programming and some familiarity with basic statistical concepts. Prior experience in data analysis or working with data libraries (such as Pandas or NumPy) is beneficial. This course is designed for aspiring data scientists, machine learning enthusiasts, and Python programmers who want to deepen their understanding of machine learning and enhance their data-driven decision-making skills. Equip yourself with practical machine learning skills and advance your journey in AI. Enroll in "Applied Machine Learning with Python" today and bring predictive power to your projects. What you'll learn - Explore machine learning algorithms, including supervised, unsupervised, and semi-supervised methods. - Apply decision trees, random forests, and K-means clustering for classification and clustering. - Develop machine learning models to gain insights and make predictions from real-world data. - Enhance model accuracy by applying model-boosting techniques and evaluating their effectiveness. There are 4 modules in this course This course provides an in-depth, hands-on introduction to machine learning using Python. You'll explore core concepts and methods, diving into supervised, unsupervised, and semi-supervised learning. Through practical exercises and examples, you'll master key algorithms including decision trees and random forests for classification, regression for predictive modeling, and K-means clustering for uncovering hidden patterns in unlabeled data. Additionally, you’ll gain insights into using model-boosting techniques to enhance model accuracy and apply strategies for leveraging unlabeled data effectively. By the end of this course, you’ll be able to: - Explain and implement decision trees and random forests as classification algorithms. - Define and differentiate various types of machine learning algorithms. - Analyze the working of regression for predictive tasks. - Apply K-means clustering to explore and discover patterns in unlabeled data. - Strategically use unlabeled data to improve model training. - Manipulate boosting algorithms to achieve higher model accuracy. This course is ideal for learners with foundational knowledge in Python programming and some familiarity with basic statistical concepts. Prior experience in data analysis or working with data libraries (such as Pandas or NumPy) is beneficial. This course is designed for aspiring data scientists, machine learning enthusiasts, and Python programmers who want to deepen their understanding of machine learning and enhance their data-driven decision-making skills. Equip yourself with practical machine learning skills and advance your journey in AI. Enroll in "Applied Machine Learning with Python" today and bring predictive power to your projects. General Details: Duration: 6h 46m 42s Updated: 03/2025 Language: English Source: [Hidden Content] Instructor: [Hidden Content] MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch [Hidden Content]

Coursera - NVIDIA: Prompt Engineering and Data Analysis Course details NVIDIA: Prompt Engineering and Data Analysis is the fifth course of the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs - Associate Specialization. This course equips learners with a solid foundation in prompt engineering, data analysis, and visualization techniques for optimizing Large Language Models (LLMs). The course covers essential concepts such as prompt engineering fundamentals, effective prompt creation, and P-tuning for enhanced LLM performance. It also delves into techniques for analyzing text data, different plot types, and their role in effective data visualization. Learners will gain hands-on experience with tools like NVIDIA NeMo for prompt engineering and cuDF and Dask cuDF for accelerated data analysis workflows. The course is divided into two modules with Lessons and Video Lectures. Learners will engage in approximately 3:00-3:30 hours of video content, covering both theoretical concepts and practical applications. Each module is paired with quizzes to assess understanding and reinforce learning. Module 1: Foundations of Prompt Engineering Module 2: Data Analysis and Visualization By the end of this course, learners will be able to: - Understand prompt engineering and its role in LLM optimization. - Apply P-tuning and RAG architecture for improved model performance. - Utilize data analysis and visualization techniques for effective NLP tasks. This course is ideal for learners interested in enhancing their skills in prompt engineering and data analysis for LLM optimization, with a focus on practical implementation. What you'll learn - Understand prompt engineering and its role in LLM optimization. - Apply P-tuning and RAG architecture for improved model performance. - Utilize data analysis and visualization techniques for effective NLP tasks. There are 2 modules in this course NVIDIA: Prompt Engineering and Data Analysis is the fifth course of the Exam Prep (NCA-GENL): NVIDIA-Certified Generative AI LLMs - Associate Specialization. This course equips learners with a solid foundation in prompt engineering, data analysis, and visualization techniques for optimizing Large Language Models (LLMs). The course covers essential concepts such as prompt engineering fundamentals, effective prompt creation, and P-tuning for enhanced LLM performance. It also delves into techniques for analyzing text data, different plot types, and their role in effective data visualization. Learners will gain hands-on experience with tools like NVIDIA NeMo for prompt engineering and cuDF and Dask cuDF for accelerated data analysis workflows. The course is divided into two modules with Lessons and Video Lectures. Learners will engage in approximately 3:00-3:30 hours of video content, covering both theoretical concepts and practical applications. Each module is paired with quizzes to assess understanding and reinforce learning. Module 1: Foundations of Prompt Engineering Module 2: Data Analysis and Visualization By the end of this course, learners will be able to: - Understand prompt engineering and its role in LLM optimization. - Apply P-tuning and RAG architecture for improved model performance. - Utilize data analysis and visualization techniques for effective NLP tasks. This course is ideal for learners interested in enhancing their skills in prompt engineering and data analysis for LLM optimization, with a focus on practical implementation. General Details: Duration: 1h 9m Updated: 03/2025 Language: English Source: [Hidden Content] Instructor: [Hidden Content] MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch [Hidden Content]