Artificial Intelligence

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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM

Network:Zero To Mastery India4 344 Technologies & Applications1 724...

📈 Analytical overview of Telegram channel Artificial Intelligence

Channel Artificial Intelligence (@artificial_intelligence_com) in the English language segment is an active participant. Currently, the community unites 72 406 subscribers, ranking 1 724 in the Technologies & Applications category and 4 344 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 72 406 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 363 over the last 30 days and by -19 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.53%. Within the first 24 hours after publication, content typically collects 1.94% reactions from the total number of subscribers.
Post reach: On average, each post receives 4 727 views. Within the first day, a publication typically gains 1 407 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 13.
Thematic interests: Content is focused on key topics such as learning, linkedin, linux, udemy, 040k|.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM”

Thanks to the high frequency of updates (latest data received on 01 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

72 406

Subscribers

-1924 hours

-1267 days

+36330 days

4 727

Post views

~ 1 40724 hours

~ 1 90648 hours

6.53%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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📱Artificial Intelligence and Machine Learning 📱Programming Foundations: Artificial Intelligence

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📂 Full description AI is driving innovation and efficiency in the tech industry. As businesses and organizations seek to leverage AI, there's a high demand for skilled professionals who can understand, develop, and ethically implement AI technologies. In this course, award-winning tech innovator and AI/ML leader Kesha Williams helps developers to upskill and merge their existing programming knowledge with AI competencies. Learn about the concept of artificial intelligence and how it revolutionizes traditional programming methodologies. Explore the tools you need to interpret, evaluate, and harness AI technologies effectively. Through Python code examples, get an introduction to the fundamental pillars of AI, including machine learning, neural networks, and computer vision, while addressing ethical considerations for responsible development. By the end of the course, you will be ready to tackle the technological challenges of today and tomorrow with confidence and creativity.

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🔅 Programming Foundations: Artificial Intelligence 🌐 Author: Kesha Williams 🔰 Level: Beginner ⏰ Duration: 1h 15m

🌀 Explore AI fundamentals, ethical implications, and practical skills, to ensure you remain at the forefront of technological innovation and ethical responsibility.

📗 Topics: Programming, AI Software Development, Artificial Intelligence 📤 Join Artificial Intelligence and Machine Learning for more courses

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💡 Your First Step is Simpler Than You Think

If you're an absolute beginner, don't jump straight into building a neural network. The most successful journeys are built on a steady progression.

1. Start with introductory Python. 2. Build your confidence. 3. Then, and only then, move into data science, machine learning, and AI. Your path will be unique. Your "why" is your compass, and these courses can be your map. The rest is up to you. So, what's your why? Once you have it, take that first step. The world of AI is waiting for you.

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💡 Got Your "Why"? Time for the "How."

Alright, you’ve got your motivation locked in. Now we can talk about the hard skills. A word of caution: the landscape of online courses is vast and a new "game-changing" program launches every week. It's impossible to declare one single "best" course.

I can only recommend what has worked for me. As a visual learner who needs to see concepts in action, the following resources were world-class for my style. I recommend this progression: A Simple Learning Path to Get You Started: 1⃣ The Foundation: Learn Python. You can’t build a house without a foundation. Start with an introduction to Python programming. It’s the lingua franca of AI and ML. - Where to go: Treehouse or the vast, free tutorials on YouTube. 🔢 The Core Concepts: Dive into ML & AI. Once you're comfortable with Python, it's time to dive in. I combined a structured university-style approach with a practical, code-first method. - Udacity: Their Deep Learning & AI Nanodegree provides a fantastic, well-structured overview of the field. - fast.ai: For a more practical, "top-down" approach where you code first and understand the theory later, Practical Deep Learning for Coders (Part 1 & Part 2) is incredible and free.

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💡 Before the "How," You Must Answer the "Why" What’s more important than *how* you start is ***why*** you start. Take a moment and ask yourself: * Do you want to future-proof your career and make more money? * Are you driven by a burning curiosity to build cool, intelligent things? * Do you want to solve a pressing world problem and make a genuine difference? Let me be clear: There is no "right" reason. A desire for financial stability is just as valid as a passion for innovation. Your "why" is your fuel.

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💡 Your AI & ML Journey Starts With a Single Question (Not a Course) You see the headlines, you feel the hype, and you’ve decided you want in. The question echoes in your mind: "How do I start with AI and Machine Learning?" Your next instinct is to search for the "best" course, the perfect textbook, or the ultimate roadmap. You’ll find a million answers, and that’s the problem. The truth is, there is no single "best" path. 🥺 Everyone’s learning journey is different. Some minds thrive on the structured depth of a book, while others come alive with the visual storytelling of a video tutorial. The "how" is personal. But what if you’re asking the wrong question first?

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🚨 🇦🇺 World’s first “Biological Computer” - Human brain meets AI Australian company Cortical Labs has unveiled the CL1, the first-ever biological computer combining human brain cells with silicon to create adaptive, energy-efficient neural networks. The CL1 learns faster than traditional AI chips and could revolutionize fields like drug discovery, robotics, and clinical testing. Researchers can access it via "Wetware-as-a-Service" (WaaS) or buy the system outright. Set to launch in late 2025, this breakthrough could redefine computing, intelligence, and AI itself.

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💡 Master the Top 10 Machine Learning Topics

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👨🏻‍💻 This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents. 📝 The idea of Agentic Document Extraction is that unlike common methods like OCR that only read text, it can also understand the structure and relationships between different parts of the document. For example, it understands which title belongs to which table or image. ✅ Works with PDFs, images, and website links. ☑️ Can chunk and process very large documents (up to 1000 pages) by itself. ✔️ Outputs both JSON and Markdown formats. ☑️ Even specifies the exact location of each section on the page. ✔️ Supports parallel and batch processing.

pip install agentic-doc

┌ 🥵 Agentic Document Extraction ├ 🌎 Website └ 🐱 GitHub Repos

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🔗 Keras vs. TensorFlow vs. PyTorch: The ultimate showdown for deep learning supremacy! 🚀 🤔 Keras: The user-friendly champion! Perfect for beginners and rapid prototyping. ⚡️ TensorFlow: The powerhouse! Great for complex projects with extensive capabilities. 🔥 PyTorch: The flexible innovator! With its dynamic computation graph, it’s a favorite among researchers.

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🎬✨ A historic event in the world of cinema OpenAI has announced its support for the production of the first full-length animated film that heavily relies on artificial intelligence tools. The film is titled “Critterz” and tells an exciting adventure story about a group of forest creatures whose peaceful lives change after a strange appearance among them. 🌟 What makes this project special is that it is not just an artistic experiment, but it shows how artificial intelligence can be a creative partner in all stages of production: • Character and background design • Scriptwriting • Animation creation 🎥 The film is scheduled to premiere at the Cannes Film Festival – May 2026, and then be released worldwide in theaters. This work represents a bold step that may open the way for a new generation of AI-produced films, potentially causing a real revolution in the entertainment industry.

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GMB Crush - Reddit Sniper Method.zip146.71 MB

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Reddit Sniper Method - AI SEO from Real Reddit Threads 🥉 🏄‍♂️ What you'll get/learn inside:

Google’s AI learns from Reddit. Now you can too. This sniper GPT finds what your market feels, fears, and buys - then turns it into content AI ranks and buyers act on. 👉Module 1: The $60 Million Intel Google Doesn’t Want You to Use (But You Can) 👉Module 02: The Underrated Goldmine: How Reddit Outsmarts Your SEO Tools 👉Module 03: Don’t Touch That Page (Until You Do This...) 👉Module 4: The AI Alignment Move That 99% Ignore 👉Module 05: Google Ads That Sell (Because Reddit Already Wrote Them) 👉Module 06: Facebook Ads That Hit Deep (Scroll-Stopping Pain Points Included) 👉Module 07: The YouTube Shorts Goldmine You’re (Still) Ignoring 👉Module 08: Lead Magnets That Sound Like They Were Written in Your Customer’s Head 👉Module 09: Local Domination Starts Here (No Tool or Course Has Ever Shown You This Way) 👉Module 10: Works with any US VPN - Opal AI Workflow Automation> The Reddit-Post Writer 👉Module 11: Works with any US VPN - Opal AI Workflow Automation> The Geo-Ranker Accelerator 👉Module 12: Works with any US VPN - Opal AI Workflow Automation> The Geo-Copy Generator

📱 Google Drive | 🌐 Sale Page

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🔗 10 Loss Functions in Machine Learning (and when to use them) Regression Losses 1️⃣ Mean Bias Error (MBE) – Captures average bias in predictions. Rarely used since positive and negative errors cancel out. 2️⃣ Mean Absolute Error (MAE) – Average absolute difference between predicted and actual values. Treats small and large errors equally since gradient magnitude is constant. 3️⃣ Mean Squared Error (MSE) – Squares errors, making large errors count more. Useful, but sensitive to outliers. 4️⃣ Root Mean Squared Error (RMSE) – Square root of MSE. Keeps loss in the same units as the target variable. 5️⃣ Huber Loss – Hybrid of MAE and MSE. Acts like MSE for small errors and MAE for large ones. Needs a hyperparameter to define the transition point. 6️⃣ Log-Cosh Loss – Smooth, non-parametric alternative to Huber. More stable but a bit more computationally expensive. Classification Losses 1️⃣ Binary Cross-Entropy (BCE) – Standard for binary classification. Measures mismatch between predicted probabilities and true labels. 2️⃣ Hinge Loss – Based on the margin between points and decision boundary. Penalizes wrong predictions and low-confidence correct ones. Used in training SVMs. 3️⃣ Cross-Entropy Loss – Generalization of BCE for multi-class classification tasks. 4️⃣ KL Divergence – Measures how one probability distribution diverges from another. For classification, minimizing KL is equivalent to minimizing cross-entropy, but it’s widely used in t-SNE and knowledge distillation.

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🚀 Want to speed up training in PyTorch by several times? DataLoader has two bad defaults that slow down the process. By fixing them, I got almost 5x speedup. ❌ Problem - .to(device) transfers data to the GPU. - While the GPU is computing, the CPU does nothing. - While the CPU prepares data, the GPU is idle. ⚡️ Solution You need to make the CPU and GPU work in parallel: - In DataLoader, set pin_memory=True - When transferring data, use .to(device, non_blocking=True) - Add num_workers to DataLoader for background loading. ✅ As a result, the CPU prepares the next batch while the GPU is busy with the current one. This eliminates idle time, and training goes noticeably faster.

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🔗 Machine Learning Roadmap

Whether you're just starting out or looking to refine your skills, this Machine Learning Roadmap breaks down every step

1️⃣ Build a solid foundation in math and stats 2️⃣ Dive into ML algorithms like Linear Regression, SVM, and Clustering 3️⃣ Choose your ML focus, from supervised learning to recommender systems 4️⃣ Master popular libraries like PyTorch, TensorFlow, and Scikit-learn 5️⃣ Gain real-world experience with projects and side gigs