12 Essential Math Theories for AI
Understanding AI requires a foundation in core mathematical concepts. Here are twelve key theories that deepen your AI knowledge:

Curse of Dimensionality:
Challenges with high-dimensional data.
Law of Large Numbers:
Reliability improves with larger datasets.
Central Limit Theorem:
Sample means approach a normal distribution.
Bayes' Theorem:
Updates probabilities with new data.
Overfitting & Underfitting:
Finding balance in model complexity.
Gradient Descent:
Optimizes model performance.
Information Theory:
Efficient data compression.
Markov Decision Processes:
Models for decision-making.
Game Theory:
Insights on agent interactions.
Statistical Learning Theory:
Basis for prediction models.
Hebbian Theory:
Neural networks learning principles.
Convolution:
Image processing in AI.

Familiarity with these theories will greatly enhance understanding of AI development and its underlying principles. Each concept builds a foundation for advanced topics and applications.

Software Engineers vs AI Engineers: 👊

Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.

1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a “Novice” is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.

The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.

Here is an example.

Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. 🚀

Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.

In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."

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2. 🔵 TypeScript — 78% adoption

3. 🟢 Node.js — 75% backend choice

4. ⚛️ React — 70% frontend framework

5. 🅰️ Angular — 55% enterprise use

6. 💚 Vue.js — 49% growing popularity

7. 🐍 Python — 48% for full-stack

8. 💎 Ruby on Rails — 45% rapid development

9. 🐘 PHP — 43% widespread use

10. ☕ Java — 40% enterprise solutions

Why open-source AI models are good for the world

Open innovation lies at the heart of the artificial-intelligence (ai) boom. The neural network “transformer”—the t in GPT—that underpins OpenAI’s was first published as research by engineers at Google. TensorFlow and PyTorch, used to build those neural networks, were created by Google and Meta, respectively, and shared with the world. Today, some argue that AI is too important and sensitive to be available to everyone, everywhere. Models that are “open-source”—ie, that make underlying code available to all, to remix and reuse as they please—are often seen as dangerous.

WhatsApp is no longer a platform just for chat.

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Don't overwhelm to learn Git,🙌

Git is only this much👇😇

1.Core:
• git init
• git clone
• git add
• git commit
• git status
• git diff
• git checkout
• git reset
• git log
• git show
• git tag
• git push
• git pull

2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick

3.Merging:
• git merge
• git rebase

4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop

5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror

6.Configuration:
• git config
• git global config
• git reset config

7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref

8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag

9.Alias:
• git config --global alias.<alias> <command>

10.Hook:
• git config --local core.hooksPath <path>


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Here is the list of latest trending tech stacks in 2024 👇👇

1. Frontend Development:
- React.js: Known for its component-based architecture and strong community support.
- Vue.js: Valued for its simplicity and flexibility in building user interfaces.
- Angular: Still widely used, especially in enterprise applications.

2. Backend Development:
- Node.js: Popular for building scalable and fast network applications using JavaScript.
- Django: Preferred for its rapid development capabilities and robust security features.
- Spring Boot: Widely used in Java-based applications for its ease of use and integration capabilities.

3. Mobile Development:
- Flutter: Known for building natively compiled applications for mobile, web, and desktop from a single codebase.
- React Native: Continues to be popular for building cross-platform applications with native capabilities.

4. Cloud Computing and DevOps:
- AWS (Amazon Web Services), Azure, Google Cloud: Leading cloud service providers offering extensive services for computing, storage, and networking.
- Docker and Kubernetes: Essential for containerization and orchestration of applications in a cloud-native environment.
- Terraform: Infrastructure as code tool for managing and provisioning cloud infrastructure.

5. Data Science and Machine Learning:
- Python: Dominant language for data science and machine learning, with libraries like NumPy, Pandas, and Scikit-learn.
- TensorFlow and PyTorch: Leading frameworks for building and training machine learning models.
- Apache Spark: Used for big data processing and analytics.

6. Cybersecurity:
- SIEM Tools (Security Information and Event Management): Such as Splunk and ELK Stack, crucial for monitoring and managing security incidents.
- Zero Trust Architecture: A security model that eliminates the idea of trust based on network location.

7. Blockchain and Cryptocurrency:
- Ethereum: A blockchain platform supporting smart contracts and decentralized applications.
- Hyperledger Fabric: Framework for developing permissioned, blockchain-based applications.

8. Artificial Intelligence (AI) and Natural Language Processing (NLP):
- GPT (Generative Pre-trained Transformer) Models: Such as GPT-4, used for various natural language understanding tasks.
- Computer Vision: Frameworks like OpenCV for image and video processing tasks.

9. Edge Computing and IoT (Internet of Things):
- Edge Computing: Technologies that bring computation and data storage closer to the location where it is needed.
- IoT Platforms: Such as AWS IoT, Azure IoT Hub, offering capabilities for managing and securing IoT devices and data.

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Python

How to protect yourself from phishing sites with Google?

Password Alert is an extension which is activated when a user enters his password on a site with the form accounts.google.com. The plugin from Google developers does not store the password, but creates an imprint of it in the browser's local storage.

So far, the plugin is only available on Google Chrome, but there are alternatives - Chrome, Opera, Firefox. They are suitable for phishing protection with different forms of password filling.

#security

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Advanced Python Scheduler (APScheduler) is a Python library for scheduling code to run later, once or periodically.
You can add new "jobs" or delete old ones on the fly at your discretion.

If you save your jobs to the database, they will also outlast a program restart and keep their state.

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Ages of Operating Systems👨🏻‍💻😎

📂 Windows 11 (3 years old)
🪟 Windows 10 (8 years old)
🍎 macOS Yosemite (10 years old)
🐉 Kali Linux (11 years old)
💻 Windows 8 (12 years old)
🌐 Manjaro (11 years old)
💻 Windows 7 (14 years old)
🖥️ Windows Vista (17 years old)
🌿 Linux Mint (18 years old)
🐧 Ubuntu (20 years old)
⚙️ Fedora (20 years old)
🔧 OpenSUSE (20 years old)
⚙️ CentOS (20 years old)
🐧 Arch Linux (22 years old)
🍏 macOS (22 years old)
💻 Windows XP (23 years old)
🖥️ Windows 2000 (24 years old)
📱 Windows 98 (25 years old)
🌍 Windows 95 (28 years old)
💻 Windows 3.1 (29 years old)
🖥️ OS/2 (32 years old)
🐧 Debian (31 years old)
🔴 Red Hat Linux (30 years old)
🎮 AmigaOS (34 years old)
🖥️ Xenix (40 years old)
📀 VMS (44 years old)
💾 MS-DOS (42 years old)
💾 CP/M (49 years old)
🖥️ Unix (54 years old)

#techinfo

Generative AI isn't easy!

It’s the groundbreaking technology that creates new content—whether it’s images, text, music, or even entire virtual worlds.

To truly master Generative AI, focus on these key areas:

0. Understanding the Basics: Learn the foundational concepts of generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models.


1. Mastering Neural Networks: Dive deep into the types of neural networks used in generative AI, such as convolutional neural networks (CNNs) for image generation and transformer models for text.


2. Exploring Text Generation Models: Understand the mechanics behind language models like GPT and BERT, and how they generate human-like text.


3. Creating Images with AI: Learn how models like DALL-E and Stable Diffusion generate realistic images from textual prompts.


4. Working with Audio and Music Generation: Explore models like Jukedeck and OpenAI’s MuseNet to create music and sound using AI.


5. Building Custom AI Models: Get hands-on experience with frameworks like TensorFlow, PyTorch, and Hugging Face to train your own generative models.


6. Fine-Tuning Pre-Trained Models: Learn how to adapt large pre-trained models to specific tasks by fine-tuning them with domain-specific data.


7. Ethics and Bias in Generative AI: Understand the ethical implications of creating content using AI, including issues of plagiarism, bias, and misinformation.


8. Evaluating and Enhancing Generated Content: Learn how to assess the quality of generated content and fine-tune models to improve their results.


9. Staying Updated with Cutting-Edge Developments: Generative AI is rapidly evolving—keep up with new advancements, techniques, and applications in the field.



Generative AI is a creative force that blends technology with imagination.

💡 Embrace the challenge of creating innovative, AI-powered content that can transform industries and art.

⏳ With practice, patience, and creativity, you’ll unlock the potential of generative AI to create something truly unique!

#genai

How To Hide Personal Photos and Videos

For Android:

To natively hide your photos on an Android device, open up your File Manager. Add a new folder, but start the name with a period. Example: .Secrets.

When you move your photos and videos into this folder, they won't appear in your gallery or albums. ❗️You can only access them through your file manager. 👌

For iPhone:

Just select the photo you want to conceal, and tap "Hide" at the bottom of the screen. This will move the image to your Hidden" folder, and it'll stay out of any Moments, Collections, or Albums.

These device-native hiding techniques aren't password protected or encrypted, though, so using them is more risky. If someone knows your phone passcode, they can still access it.

Also there are apps for this task (password protected or encrypted). Would it be interesting for you to know about them?

#security

Ages of Operating Systems👨🏻‍💻😎

📂 Windows 11 (3 years old)
🪟 Windows 10 (8 years old)
🍎 macOS Yosemite (10 years old)
🐉 Kali Linux (11 years old)
💻 Windows 8 (12 years old)
🌐 Manjaro (11 years old)
💻 Windows 7 (14 years old)
🖥️ Windows Vista (17 years old)
🌿 Linux Mint (18 years old)
🐧 Ubuntu (20 years old)
⚙️ Fedora (20 years old)
🔧 OpenSUSE (20 years old)
⚙️ CentOS (20 years old)
🐧 Arch Linux (22 years old)
🍏 macOS (22 years old)
💻 Windows XP (23 years old)
🖥️ Windows 2000 (24 years old)
📱 Windows 98 (25 years old)
🌍 Windows 95 (28 years old)
💻 Windows 3.1 (29 years old)
🖥️ OS/2 (32 years old)
🐧 Debian (31 years old)
🔴 Red Hat Linux (30 years old)
🎮 AmigaOS (34 years old)
🖥️ Xenix (40 years old)
📀 VMS (44 years old)
💾 MS-DOS (42 years old)
💾 CP/M (49 years old)
🖥️ Unix (54 years old)

#coding