1. What is the main numerical reason batch normalization accelerates training?
A. It increases model capacity
B. It reduces internal covariate shift
C. It removes the need for regularization
D. It replaces activation functions
Correct answer: B.
2. Why are sigmoid activations problematic in deep networks?
A. They are non-differentiable
B. They produce sparse activations
C. They saturate and cause vanishing gradients
D. They require large learning rates
Correct answer: C.
3. What happens when the learning rate is set too high?
A. Training converges slowly
B. The model overfits
C. The loss oscillates or diverges
D. Gradients vanish
Correct answer: C.
4. In convolutional layers, what determines the receptive field size?
A. Number of filters
B. Kernel size and depth
C. Activation function
D. Optimizer type
Correct answer: B.
5. Why is weight sharing important in CNNs?
A. It increases model depth
B. It reduces computational cost and parameters
C. It improves gradient descent accuracy
D. It prevents exploding gradients
Correct answer: B.
6. What is the primary function of padding in convolutional networks?
A. Increase number of channels
B. Reduce overfitting
C. Preserve spatial dimensions
D. Normalize input values
Correct answer: C.
7. Which condition most strongly indicates data leakage?
A. High training accuracy
B. Low training loss
C. Validation performance better than training
D. Slow convergence
Correct answer: C.
8. Why are recurrent neural networks difficult to train on long sequences?
A. High memory usage
B. Nonlinear activations
C. Vanishing and exploding gradients
D. Large batch sizes
Correct answer: C.
9. What architectural feature allows LSTMs to mitigate vanishing gradients?
A. Residual connections
B. Gated cell state
C. Dropout layers
D. Weight decay
Correct answer: B.
10. In sequence modeling, what does teacher forcing refer to?
A. Using larger batch sizes
B. Feeding ground-truth outputs during training
C. Freezing embedding layers
D. Shuffling time steps
Correct answer: B.
11. Why is softmax unsuitable for multi-label classification?
A. It is not differentiable
B. It enforces mutually exclusive class probabilities
C. It cannot handle sparse targets
D. It causes gradient explosion
Correct answer: B.
12. What does L2 regularization mathematically penalize?
A. Absolute values of weights
B. Squared magnitude of weights
C. Number of parameters
D. Gradient variance
Correct answer: B.
13. Why does mean squared error perform poorly for classification?
A. It is computationally expensive
B. It ignores class imbalance
C. It provides weak gradients for confident wrong predictions
D. It cannot be minimized
Correct answer: C.
14. What is the main advantage of global average pooling?
A. Increases spatial resolution
B. Adds trainable parameters
C. Reduces overfitting by eliminating dense layers
D. Improves gradient flow
Correct answer: C.
15. Why are pretrained embeddings useful in NLP tasks?
A. They reduce input sequence length
B. They encode semantic relationships learned from large corpora
C. They eliminate the need for tokenization
D. They prevent overfitting entirely
Correct answer: B.
16. What does gradient clipping primarily prevent?
A. Overfitting
B. Vanishing gradients
C. Exploding gradients
D. Data leakage
Correct answer: C.
17. Why is shuffling training data between epochs important?
A. To increase batch size
B. To improve memory usage
C. To reduce bias in gradient updates
D. To stabilize validation loss
Correct answer: C.
18. What is the main risk of excessive model capacity?
A. Slow inference
B. Underfitting
C. Overfitting
D. Numerical instability
Correct answer: C.
A. It increases model capacity
B. It reduces internal covariate shift
C. It removes the need for regularization
D. It replaces activation functions
Correct answer: B.
2. Why are sigmoid activations problematic in deep networks?
A. They are non-differentiable
B. They produce sparse activations
C. They saturate and cause vanishing gradients
D. They require large learning rates
Correct answer: C.
3. What happens when the learning rate is set too high?
A. Training converges slowly
B. The model overfits
C. The loss oscillates or diverges
D. Gradients vanish
Correct answer: C.
4. In convolutional layers, what determines the receptive field size?
A. Number of filters
B. Kernel size and depth
C. Activation function
D. Optimizer type
Correct answer: B.
5. Why is weight sharing important in CNNs?
A. It increases model depth
B. It reduces computational cost and parameters
C. It improves gradient descent accuracy
D. It prevents exploding gradients
Correct answer: B.
6. What is the primary function of padding in convolutional networks?
A. Increase number of channels
B. Reduce overfitting
C. Preserve spatial dimensions
D. Normalize input values
Correct answer: C.
7. Which condition most strongly indicates data leakage?
A. High training accuracy
B. Low training loss
C. Validation performance better than training
D. Slow convergence
Correct answer: C.
8. Why are recurrent neural networks difficult to train on long sequences?
A. High memory usage
B. Nonlinear activations
C. Vanishing and exploding gradients
D. Large batch sizes
Correct answer: C.
9. What architectural feature allows LSTMs to mitigate vanishing gradients?
A. Residual connections
B. Gated cell state
C. Dropout layers
D. Weight decay
Correct answer: B.
10. In sequence modeling, what does teacher forcing refer to?
A. Using larger batch sizes
B. Feeding ground-truth outputs during training
C. Freezing embedding layers
D. Shuffling time steps
Correct answer: B.
11. Why is softmax unsuitable for multi-label classification?
A. It is not differentiable
B. It enforces mutually exclusive class probabilities
C. It cannot handle sparse targets
D. It causes gradient explosion
Correct answer: B.
12. What does L2 regularization mathematically penalize?
A. Absolute values of weights
B. Squared magnitude of weights
C. Number of parameters
D. Gradient variance
Correct answer: B.
13. Why does mean squared error perform poorly for classification?
A. It is computationally expensive
B. It ignores class imbalance
C. It provides weak gradients for confident wrong predictions
D. It cannot be minimized
Correct answer: C.
14. What is the main advantage of global average pooling?
A. Increases spatial resolution
B. Adds trainable parameters
C. Reduces overfitting by eliminating dense layers
D. Improves gradient flow
Correct answer: C.
15. Why are pretrained embeddings useful in NLP tasks?
A. They reduce input sequence length
B. They encode semantic relationships learned from large corpora
C. They eliminate the need for tokenization
D. They prevent overfitting entirely
Correct answer: B.
16. What does gradient clipping primarily prevent?
A. Overfitting
B. Vanishing gradients
C. Exploding gradients
D. Data leakage
Correct answer: C.
17. Why is shuffling training data between epochs important?
A. To increase batch size
B. To improve memory usage
C. To reduce bias in gradient updates
D. To stabilize validation loss
Correct answer: C.
18. What is the main risk of excessive model capacity?
A. Slow inference
B. Underfitting
C. Overfitting
D. Numerical instability
Correct answer: C.
❤2
19. Why is cross-entropy preferred over accuracy as a training objective?
A. Accuracy is non-differentiable
B. Accuracy requires larger datasets
C. Cross-entropy reduces model size
D. Cross-entropy prevents overfitting
Correct answer: A.
20. What is the core assumption behind convolutional neural networks?
A. Features are independent
B. Data is linearly separable
C. Local patterns are spatially correlated
D. Labels are mutually exclusive
Correct answer: C.
https://news.1rj.ru/str/DataScienceM
A. Accuracy is non-differentiable
B. Accuracy requires larger datasets
C. Cross-entropy reduces model size
D. Cross-entropy prevents overfitting
Correct answer: A.
20. What is the core assumption behind convolutional neural networks?
A. Features are independent
B. Data is linearly separable
C. Local patterns are spatially correlated
D. Labels are mutually exclusive
Correct answer: C.
https://news.1rj.ru/str/DataScienceM
Telegram
Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
Admin: @HusseinSheikho || @Hussein_Sheikho
Admin: @HusseinSheikho || @Hussein_Sheikho
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100+ LLM Interview Questions and Answers (GitHub Repo)
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
https://news.1rj.ru/str/DataScienceM✅
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
https://news.1rj.ru/str/DataScienceM
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🚀 Top 9 Predictive Models Every Data Scientist Should Know in 2025
In the world of Machine Learning, selecting the right predictive model is crucial for solving real-world problems effectively.
Here’s a deep dive into the top 9 models and when to use them :-
1️⃣ Regularized Linear/Logistic Regression
• Best for: Tabular data with mostly linear effects
• Why: Fast, interpretable, strong baseline
• Watch out: Multicollinearity, feature scaling
• Key knobs: L1/L2/Elastic Net strength
2️⃣ Decision Trees
• Best for: Simple rules and quick interpretability
• Why: Captures nonlinearity and feature interactions
• Watch out: Overfitting
• Key knobs: max_depth, min_samples_leaf
3️⃣ Random Forest
• Best for: Mixed-type tabular data
• Why: Robust, handles missingness, low tuning effort
• Watch out: Slower inference for large models
• Key knobs: n_estimators, max_features
4️⃣ Gradient Boosting Trees
• Best for: Structured data requiring top performance
• Why: Handles complex patterns and interactions
• Watch out: Overfitting if not tuned carefully
• Key knobs: learning_rate, n_estimators, max_depth, regularization
5️⃣ Support Vector Machines (linear/RBF)
• Best for: Medium-sized datasets with clear margins
• Why: Strong performance after scaling
• Watch out: Kernel choice and cost at scale
• Key knobs: C, kernel, gamma
6️⃣ k-Nearest Neighbors (k-NN)
• Best for: Small datasets with local structure
• Why: Simple, non-parametric
• Watch out: Poor scaling, sensitive to feature scaling
• Key knobs: k, distance metric, weighting
7️⃣ Naive Bayes
• Best for: High-dimensional sparse features (like text)
• Why: Very fast, competitive for many applications
• Watch out: Independence assumption
• Key knobs: smoothing (alpha)
8️⃣ Multilayer Perceptrons (Feedforward Neural Networks)
• Best for: Nonlinear relationships with sufficient data & compute
• Why: Flexible universal approximators
• Watch out: Tuning, overfitting without regularization
• Key knobs: layers/neurons, dropout, learning rate
9️⃣ Classical Time-Series Models
• Best for: Univariate or small-multivariate forecasting with seasonality
• Why: Transparent baselines, good for limited data
• Watch out: Stationarity, careful feature engineering
• Key knobs: (p, d, q), seasonal terms, exogenous variables
💡 Pro Tip: Each model has its strengths and trade-offs. Understanding when to use which model and how to tune its hyperparameters is key to building robust and interpretable predictive systems.
https://news.1rj.ru/str/DataScienceM✅
In the world of Machine Learning, selecting the right predictive model is crucial for solving real-world problems effectively.
Here’s a deep dive into the top 9 models and when to use them :-
1️⃣ Regularized Linear/Logistic Regression
• Best for: Tabular data with mostly linear effects
• Why: Fast, interpretable, strong baseline
• Watch out: Multicollinearity, feature scaling
• Key knobs: L1/L2/Elastic Net strength
2️⃣ Decision Trees
• Best for: Simple rules and quick interpretability
• Why: Captures nonlinearity and feature interactions
• Watch out: Overfitting
• Key knobs: max_depth, min_samples_leaf
3️⃣ Random Forest
• Best for: Mixed-type tabular data
• Why: Robust, handles missingness, low tuning effort
• Watch out: Slower inference for large models
• Key knobs: n_estimators, max_features
4️⃣ Gradient Boosting Trees
• Best for: Structured data requiring top performance
• Why: Handles complex patterns and interactions
• Watch out: Overfitting if not tuned carefully
• Key knobs: learning_rate, n_estimators, max_depth, regularization
5️⃣ Support Vector Machines (linear/RBF)
• Best for: Medium-sized datasets with clear margins
• Why: Strong performance after scaling
• Watch out: Kernel choice and cost at scale
• Key knobs: C, kernel, gamma
6️⃣ k-Nearest Neighbors (k-NN)
• Best for: Small datasets with local structure
• Why: Simple, non-parametric
• Watch out: Poor scaling, sensitive to feature scaling
• Key knobs: k, distance metric, weighting
7️⃣ Naive Bayes
• Best for: High-dimensional sparse features (like text)
• Why: Very fast, competitive for many applications
• Watch out: Independence assumption
• Key knobs: smoothing (alpha)
8️⃣ Multilayer Perceptrons (Feedforward Neural Networks)
• Best for: Nonlinear relationships with sufficient data & compute
• Why: Flexible universal approximators
• Watch out: Tuning, overfitting without regularization
• Key knobs: layers/neurons, dropout, learning rate
9️⃣ Classical Time-Series Models
• Best for: Univariate or small-multivariate forecasting with seasonality
• Why: Transparent baselines, good for limited data
• Watch out: Stationarity, careful feature engineering
• Key knobs: (p, d, q), seasonal terms, exogenous variables
💡 Pro Tip: Each model has its strengths and trade-offs. Understanding when to use which model and how to tune its hyperparameters is key to building robust and interpretable predictive systems.
https://news.1rj.ru/str/DataScienceM
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📌 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-18 | ⏱️ Read time: 11 min read
With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate…
#DataScience #AI #Python
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-18 | ⏱️ Read time: 11 min read
With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate…
#DataScience #AI #Python
❤2
📌 The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs
🗂 Category: ALGORITHMS
🕒 Date: 2025-12-18 | ⏱️ Read time: 31 min read
An optimal solution to the well-known NP-complete problem, when the input values are close enough…
#DataScience #AI #Python
🗂 Category: ALGORITHMS
🕒 Date: 2025-12-18 | ⏱️ Read time: 31 min read
An optimal solution to the well-known NP-complete problem, when the input values are close enough…
#DataScience #AI #Python
❤2
📌 Generating Artwork in Python Inspired by Hirst’s Million-Dollar Spots Painting
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-18 | ⏱️ Read time: 6 min read
Using Python to generate art
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-18 | ⏱️ Read time: 6 min read
Using Python to generate art
#DataScience #AI #Python
❤2
📌 The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-18 | ⏱️ Read time: 12 min read
Understanding forward propagation and backpropagation through explicit formulas
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-18 | ⏱️ Read time: 12 min read
Understanding forward propagation and backpropagation through explicit formulas
#DataScience #AI #Python
❤1
📌 The Machine Learning “Advent Calendar” Day 19: Bagging in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-19 | ⏱️ Read time: 11 min read
Understanding ensemble learning from first principles in Excel
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-19 | ⏱️ Read time: 11 min read
Understanding ensemble learning from first principles in Excel
#DataScience #AI #Python
📌 Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC)
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-19 | ⏱️ Read time: 27 min read
Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering and…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-19 | ⏱️ Read time: 27 min read
Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering and…
#DataScience #AI #Python
📌 How I Optimized My Leaf Raking Strategy Using Linear Programming
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-19 | ⏱️ Read time: 13 min read
From a weekend chore to a fun application of valuable operations research principles
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-19 | ⏱️ Read time: 13 min read
From a weekend chore to a fun application of valuable operations research principles
#DataScience #AI #Python
❤2
📌 Six Lessons Learned Building RAG Systems in Production
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-19 | ⏱️ Read time: 10 min read
Best practices for data quality, retrieval design, and evaluation in production RAG systems
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-19 | ⏱️ Read time: 10 min read
Best practices for data quality, retrieval design, and evaluation in production RAG systems
#DataScience #AI #Python
❤2
Forwarded from Machine Learning with Python
🚀Stanford just completed a must-watch for anyone serious about AI:
🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.
If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)
🧠 My workflow: I usually take the trannoscripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
🎥 Watch these now:
- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ
🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.
Let’s level up.
https://news.1rj.ru/str/CodeProgrammer😅
🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.
If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)
🧠 My workflow: I usually take the trannoscripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
🎥 Watch these now:
- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ
🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.
Let’s level up.
https://news.1rj.ru/str/CodeProgrammer
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📌 Understanding the Generative AI User
🗂 Category: PRODUCT MANAGEMENT
🕒 Date: 2025-12-20 | ⏱️ Read time: 11 min read
What do regular technology users think (and know) about AI?
#DataScience #AI #Python
🗂 Category: PRODUCT MANAGEMENT
🕒 Date: 2025-12-20 | ⏱️ Read time: 11 min read
What do regular technology users think (and know) about AI?
#DataScience #AI #Python
❤2
📌 EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-20 | ⏱️ Read time: 9 min read
Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in…
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-20 | ⏱️ Read time: 9 min read
Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in…
#DataScience #AI #Python
❤1
📌 Tools for Your LLM: a Deep Dive into MCP
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-21 | ⏱️ Read time: 8 min read
MCP is a key enabler into turning your LLM into an agent by providing it…
#DataScience #AI #Python
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-21 | ⏱️ Read time: 8 min read
MCP is a key enabler into turning your LLM into an agent by providing it…
#DataScience #AI #Python
❤3
📌 How to Do Evals on a Bloated RAG Pipeline
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-21 | ⏱️ Read time: 71 min read
Comparing metrics across datasets and models
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-21 | ⏱️ Read time: 71 min read
Comparing metrics across datasets and models
#DataScience #AI #Python
❤1
🚀 Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://news.1rj.ru/str/CodeProgrammer
🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://news.1rj.ru/str/DataScienceM
🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://news.1rj.ru/str/DataScience4
🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ
💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1
🧑🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC
😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT
💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9
🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab
🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://news.1rj.ru/str/DataScienceN
📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV
📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX
🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53
⭐️ Research Papers
Professional Academic Writing & Simulation Services
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Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
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The largest Arabic-speaking group for Python developers to share knowledge and help.
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Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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Admin: @HusseinSheikho
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📌 The Geometry of Laziness: What Angles Reveal About AI Hallucinations
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-22 | ⏱️ Read time: 12 min read
A story about failing forward, spheres you can’t visualize, and why sometimes the math knows…
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-22 | ⏱️ Read time: 12 min read
A story about failing forward, spheres you can’t visualize, and why sometimes the math knows…
#DataScience #AI #Python
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📌 Understanding Vibe Proving
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-22 | ⏱️ Read time: 18 min read
How to make LLMs reason with verifiable, step-by-step logic (Part 1)
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-22 | ⏱️ Read time: 18 min read
How to make LLMs reason with verifiable, step-by-step logic (Part 1)
#DataScience #AI #Python
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Forwarded from Machine Learning with Python
AI-ML Roadmap from Scratch
👉 https://github.com/aadi1011/AI-ML-Roadmap-from-scratch?tab=readme-ov-file
https://news.1rj.ru/str/CodeProgrammer🌟
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