This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

The most complete list of video courses on Computer Science on the internet.

cs-video-courses — 78K+ stars.

MIT.
Stanford University.
University of California, Berkeley.
Harvard University.
Carnegie Mellon University.
Indian Institutes of Technology.
Princeton University.
California Institute of Technology.

Everything is free. All lectures are in video format. Everything is collected in one repository.

Topics:

→ Data structures and algorithms
→ Operating systems
→ Distributed systems
→ Database systems
→ Computer networks
→ Machine learning
→ Deep learning
→ Natural language processing (NLP)
→ Computer vision
→ Computer graphics
→ Security
→ Quantum computing
→ Robotics
→ Blockchain

From beginner level (CS50) to advanced (6.824 Distributed Systems).

The curriculum is free. 🤙
https://github.com/Developer-Y/cs-video-courses

https://t.me/CodeProgrammer ⚡️
Save & Share & Like 🏃‍♀️

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

📝 12 Essential Articles for Data Scientists

🏷 Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.

🏷 Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.

🏷 Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.

🏷 Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.

🏷 Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.

🏷 Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.

🏷 Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.

🏷 Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.

🏷 Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.

🏷 Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.

🏷 Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.

🏷 Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.

https://t.me/CodeProgrammer 🌟

🚀 LLM Architectures 🧠
Transformer architectures may look similar, but they solve very different problems once data starts flowing through them. 🔄

The four main Transformer families in simple terms. 📚

👉 Decoder-only models like GPT and LLaMA generate text one token at a time. Each new token looks only at previous tokens. This makes them great for chat, code generation, and text completion. 💬💻

👉 Encoder-only models like BERT and RoBERTa focus on understanding text. Every token sees the full sentence at once. These models are used for classification, search, and extracting meaning rather than generating text. 🔍📖

👉 Encoder-decoder models like T5 and BART first understand the input, then generate an output. This setup is common for translation, summarization, and question answering. 🌐📝

👉 Mixture of Experts (MoE) models like Mixtral and GLaM scale smarter, not harder. A router sends tokens to a small set of expert networks, allowing very large models to run efficiently. ⚡️🤖

Example:
Summarizing a document 📄
- Decoder-only generates fluent text ✍️
- Encoder-only ranks important sentences 🏷
- Encoder-decoder produces a clean summary 🧹
- MoE scales the process with lower compute cost 💰

Choosing the right Transformer matters more than choosing the largest one. ⚖️✨

https://t.me/DataAnalyticsX 🔰

Everyone wants to become a 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫… 📊 But very few follow a structured path. 🛤

They keep learning random tools, watching endless tutorials and still feel unprepared. 🤯

Meanwhile, some people are quietly transitioning into roles like:
💼 Azure Data Engineer
💼 Data Architect
💼 Senior Data Engineer

What are they doing differently? 🤔
They’re not doing more.
They’re doing the right things consistently. ✨

Here’s what’s working for them:
✔️ A step-by-step Azure Data Engineering roadmap 🗺
✔️ Mastering SQL & Python (not just basics) 💻
✔️ Hands-on with Azure tools (ADF, Synapse, Data Lake) ☁️
✔️ Building real-world, portfolio-ready projects 🏗
✔️ Preparing specifically for interviews 🎯
✔️ Learning with a focused community 🤝