1. What is the primary purpose of a loss function in a neural network?
A. To initialize model weights
B. To measure the modelโs prediction error
C. To update training data
D. To visualize model performance
Correct answer: B.
2. Which component is responsible for updating model weights during training?
A. Loss function
B. Activation function
C. Optimizer
D. Metric
Correct answer: C.
3. What does an epoch represent during model training?
A. A single weight update
B. One forward pass only
C. One complete pass over the training dataset
D. One mini-batch
Correct answer: C.
4. Which activation function is commonly used in hidden layers to mitigate vanishing gradients?
A. Sigmoid
B. Tanh
C. ReLU
D. Softmax
Correct answer: C.
5. What is the main role of the validation dataset?
A. To update model weights
B. To test final model performance
C. To tune hyperparameters and monitor overfitting
D. To normalize input data
Correct answer: C.
6. Which technique randomly disables neurons during training to reduce overfitting?
A. Batch normalization
B. Dropout
C. Data augmentation
D. Early stopping
Correct answer: B.
7. What problem does regularization primarily address?
A. Underfitting
B. Exploding gradients
C. Overfitting
D. Data leakage
Correct answer: C.
8. Which type of neural network is best suited for image data?
A. Recurrent Neural Network
B. Fully Connected Network
C. Convolutional Neural Network
D. Autoencoder
Correct answer: C.
9. What is the purpose of convolutional filters in CNNs?
A. To reduce dataset size
B. To detect local patterns in data
C. To normalize pixel values
D. To perform classification directly
Correct answer: B.
10. What does pooling primarily achieve in convolutional neural networks?
A. Increases spatial resolution
B. Reduces overfitting by adding noise
C. Reduces spatial dimensions and computation
D. Converts images to vectors
Correct answer: C.
11. Which loss function is most appropriate for multi-class classification?
A. Mean Squared Error
B. Binary Crossentropy
C. Categorical Crossentropy
D. Hinge Loss
Correct answer: C.
12. What is a common symptom of overfitting?
A. High training loss and high validation loss
B. Low training loss and high validation loss
C. High training accuracy and low training loss
D. Low training accuracy and low validation accuracy
Correct answer: B.
13. What does backpropagation compute?
A. Model predictions
B. Loss values only
C. Gradients of the loss with respect to weights
D. Input feature scaling
Correct answer: C.
14. Which Keras method is used to define the training configuration of a model?
A. fit()
B. compile()
C. evaluate()
D. predict()
Correct answer: B.
15. What is transfer learning primarily based on?
A. Training from scratch on small datasets
B. Reusing pre-trained models or layers
C. Random weight initialization
D. Increasing model depth
Correct answer: B.
16. Which type of layer is used to flatten multidimensional input into a vector?
A. Dense
B. Conv2D
C. Flatten
D. Dropout
Correct answer: C.
17. What is the main advantage of mini-batch gradient descent?
A. Exact gradient computation
B. No memory usage
C. Faster convergence with stable updates
D. Eliminates need for an optimizer
Correct answer: C.
18. Which metric is commonly used to evaluate classification models?
A. Mean Absolute Error
B. R-squared
C. Accuracy
D. Perplexity
Correct answer: C.
19. What is the primary goal of early stopping?
A. Speed up data loading
B. Prevent overfitting by stopping training at the right time
C. Increase model capacity
D. Improve gradient flow
Correct answer: B.
20. Which framework is primarily used in the book to implement deep learning models?
A. PyTorch
B. Scikit-learn
C. Keras with TensorFlow backend
D. MXNet
Correct answer: C.
https://t.me/DataScienceM
A. To initialize model weights
B. To measure the modelโs prediction error
C. To update training data
D. To visualize model performance
Correct answer: B.
2. Which component is responsible for updating model weights during training?
A. Loss function
B. Activation function
C. Optimizer
D. Metric
Correct answer: C.
3. What does an epoch represent during model training?
A. A single weight update
B. One forward pass only
C. One complete pass over the training dataset
D. One mini-batch
Correct answer: C.
4. Which activation function is commonly used in hidden layers to mitigate vanishing gradients?
A. Sigmoid
B. Tanh
C. ReLU
D. Softmax
Correct answer: C.
5. What is the main role of the validation dataset?
A. To update model weights
B. To test final model performance
C. To tune hyperparameters and monitor overfitting
D. To normalize input data
Correct answer: C.
6. Which technique randomly disables neurons during training to reduce overfitting?
A. Batch normalization
B. Dropout
C. Data augmentation
D. Early stopping
Correct answer: B.
7. What problem does regularization primarily address?
A. Underfitting
B. Exploding gradients
C. Overfitting
D. Data leakage
Correct answer: C.
8. Which type of neural network is best suited for image data?
A. Recurrent Neural Network
B. Fully Connected Network
C. Convolutional Neural Network
D. Autoencoder
Correct answer: C.
9. What is the purpose of convolutional filters in CNNs?
A. To reduce dataset size
B. To detect local patterns in data
C. To normalize pixel values
D. To perform classification directly
Correct answer: B.
10. What does pooling primarily achieve in convolutional neural networks?
A. Increases spatial resolution
B. Reduces overfitting by adding noise
C. Reduces spatial dimensions and computation
D. Converts images to vectors
Correct answer: C.
11. Which loss function is most appropriate for multi-class classification?
A. Mean Squared Error
B. Binary Crossentropy
C. Categorical Crossentropy
D. Hinge Loss
Correct answer: C.
12. What is a common symptom of overfitting?
A. High training loss and high validation loss
B. Low training loss and high validation loss
C. High training accuracy and low training loss
D. Low training accuracy and low validation accuracy
Correct answer: B.
13. What does backpropagation compute?
A. Model predictions
B. Loss values only
C. Gradients of the loss with respect to weights
D. Input feature scaling
Correct answer: C.
14. Which Keras method is used to define the training configuration of a model?
A. fit()
B. compile()
C. evaluate()
D. predict()
Correct answer: B.
15. What is transfer learning primarily based on?
A. Training from scratch on small datasets
B. Reusing pre-trained models or layers
C. Random weight initialization
D. Increasing model depth
Correct answer: B.
16. Which type of layer is used to flatten multidimensional input into a vector?
A. Dense
B. Conv2D
C. Flatten
D. Dropout
Correct answer: C.
17. What is the main advantage of mini-batch gradient descent?
A. Exact gradient computation
B. No memory usage
C. Faster convergence with stable updates
D. Eliminates need for an optimizer
Correct answer: C.
18. Which metric is commonly used to evaluate classification models?
A. Mean Absolute Error
B. R-squared
C. Accuracy
D. Perplexity
Correct answer: C.
19. What is the primary goal of early stopping?
A. Speed up data loading
B. Prevent overfitting by stopping training at the right time
C. Increase model capacity
D. Improve gradient flow
Correct answer: B.
20. Which framework is primarily used in the book to implement deep learning models?
A. PyTorch
B. Scikit-learn
C. Keras with TensorFlow backend
D. MXNet
Correct answer: C.
https://t.me/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
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.
โค2
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://t.me/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://t.me/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
โค3๐1
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.
๐ Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
https://t.me/DataScienceMโ
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://t.me/DataScienceM
Please open Telegram to view this post
VIEW IN TELEGRAM
โค4
๐ 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.
Hereโs a deep dive into the top 9 models and when to use them :-
1๏ธโฃ Regularized Linear/Logistic Regression
โข Best for: Tabular data with mostly linear effects
โข Why: Fast, interpretable, strong baseline
โข Watch out: Multicollinearity, feature scaling
โข Key knobs: L1/L2/Elastic Net strength
2๏ธโฃ Decision Trees
โข Best for: Simple rules and quick interpretability
โข Why: Captures nonlinearity and feature interactions
โข Watch out: Overfitting
โข Key knobs: max_depth, min_samples_leaf
3๏ธโฃ Random Forest
โข Best for: Mixed-type tabular data
โข Why: Robust, handles missingness, low tuning effort
โข Watch out: Slower inference for large models
โข Key knobs: n_estimators, max_features
4๏ธโฃ Gradient Boosting Trees
โข Best for: Structured data requiring top performance
โข Why: Handles complex patterns and interactions
โข Watch out: Overfitting if not tuned carefully
โข Key knobs: learning_rate, n_estimators, max_depth, regularization
5๏ธโฃ Support Vector Machines (linear/RBF)
โข Best for: Medium-sized datasets with clear margins
โข Why: Strong performance after scaling
โข Watch out: Kernel choice and cost at scale
โข Key knobs: C, kernel, gamma
6๏ธโฃ k-Nearest Neighbors (k-NN)
โข Best for: Small datasets with local structure
โข Why: Simple, non-parametric
โข Watch out: Poor scaling, sensitive to feature scaling
โข Key knobs: k, distance metric, weighting
7๏ธโฃ Naive Bayes
โข Best for: High-dimensional sparse features (like text)
โข Why: Very fast, competitive for many applications
โข Watch out: Independence assumption
โข Key knobs: smoothing (alpha)
8๏ธโฃ Multilayer Perceptrons (Feedforward Neural Networks)
โข Best for: Nonlinear relationships with sufficient data & compute
โข Why: Flexible universal approximators
โข Watch out: Tuning, overfitting without regularization
โข Key knobs: layers/neurons, dropout, learning rate
9๏ธโฃ Classical Time-Series Models
โข Best for: Univariate or small-multivariate forecasting with seasonality
โข Why: Transparent baselines, good for limited data
โข Watch out: Stationarity, careful feature engineering
โข Key knobs: (p, d, q), seasonal terms, exogenous variables
๐ก 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://t.me/DataScienceMโ
In the world of Machine Learning, selecting the right predictive model is crucial for solving real-world problems effectively.
Hereโs a deep dive into the top 9 models and when to use them :-
1๏ธโฃ Regularized Linear/Logistic Regression
โข Best for: Tabular data with mostly linear effects
โข Why: Fast, interpretable, strong baseline
โข Watch out: Multicollinearity, feature scaling
โข Key knobs: L1/L2/Elastic Net strength
2๏ธโฃ Decision Trees
โข Best for: Simple rules and quick interpretability
โข Why: Captures nonlinearity and feature interactions
โข Watch out: Overfitting
โข Key knobs: max_depth, min_samples_leaf
3๏ธโฃ Random Forest
โข Best for: Mixed-type tabular data
โข Why: Robust, handles missingness, low tuning effort
โข Watch out: Slower inference for large models
โข Key knobs: n_estimators, max_features
4๏ธโฃ Gradient Boosting Trees
โข Best for: Structured data requiring top performance
โข Why: Handles complex patterns and interactions
โข Watch out: Overfitting if not tuned carefully
โข Key knobs: learning_rate, n_estimators, max_depth, regularization
5๏ธโฃ Support Vector Machines (linear/RBF)
โข Best for: Medium-sized datasets with clear margins
โข Why: Strong performance after scaling
โข Watch out: Kernel choice and cost at scale
โข Key knobs: C, kernel, gamma
6๏ธโฃ k-Nearest Neighbors (k-NN)
โข Best for: Small datasets with local structure
โข Why: Simple, non-parametric
โข Watch out: Poor scaling, sensitive to feature scaling
โข Key knobs: k, distance metric, weighting
7๏ธโฃ Naive Bayes
โข Best for: High-dimensional sparse features (like text)
โข Why: Very fast, competitive for many applications
โข Watch out: Independence assumption
โข Key knobs: smoothing (alpha)
8๏ธโฃ Multilayer Perceptrons (Feedforward Neural Networks)
โข Best for: Nonlinear relationships with sufficient data & compute
โข Why: Flexible universal approximators
โข Watch out: Tuning, overfitting without regularization
โข Key knobs: layers/neurons, dropout, learning rate
9๏ธโฃ Classical Time-Series Models
โข Best for: Univariate or small-multivariate forecasting with seasonality
โข Why: Transparent baselines, good for limited data
โข Watch out: Stationarity, careful feature engineering
โข Key knobs: (p, d, q), seasonal terms, exogenous variables
๐ก 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://t.me/DataScienceM
Please open Telegram to view this post
VIEW IN TELEGRAM
โค6
๐ 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio
๐ Category: LLM APPLICATIONS
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 11 min read
With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicateโฆ
#DataScience #AI #Python
๐ Category: LLM APPLICATIONS
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 11 min read
With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicateโฆ
#DataScience #AI #Python
โค2
๐ The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs
๐ Category: ALGORITHMS
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 31 min read
An optimal solution to the well-known NP-complete problem, when the input values are close enoughโฆ
#DataScience #AI #Python
๐ Category: ALGORITHMS
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 31 min read
An optimal solution to the well-known NP-complete problem, when the input values are close enoughโฆ
#DataScience #AI #Python
โค2
๐ Generating Artwork in Python Inspired by Hirstโs Million-Dollar Spots Painting
๐ Category: PROGRAMMING
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 6 min read
Using Python to generate art
#DataScience #AI #Python
๐ Category: PROGRAMMING
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 6 min read
Using Python to generate art
#DataScience #AI #Python
โค2
๐ The Machine Learning โAdvent Calendarโ Day 18: Neural Network Classifier in Excel
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 12 min read
Understanding forward propagation and backpropagation through explicit formulas
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-18 | โฑ๏ธ Read time: 12 min read
Understanding forward propagation and backpropagation through explicit formulas
#DataScience #AI #Python
โค1
๐ The Machine Learning โAdvent Calendarโ Day 19: Bagging in Excel
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 11 min read
Understanding ensemble learning from first principles in Excel
#DataScience #AI #Python
๐ Category: MACHINE LEARNING
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 11 min read
Understanding ensemble learning from first principles in Excel
#DataScience #AI #Python
๐ Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC)
๐ Category: AGENTIC AI
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 27 min read
Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering andโฆ
#DataScience #AI #Python
๐ Category: AGENTIC AI
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 27 min read
Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering andโฆ
#DataScience #AI #Python
๐ How I Optimized My Leaf Raking Strategy Using Linear Programming
๐ Category: DATA SCIENCE
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 13 min read
From a weekend chore to a fun application of valuable operations research principles
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 13 min read
From a weekend chore to a fun application of valuable operations research principles
#DataScience #AI #Python
โค2
๐ Six Lessons Learned Building RAG Systems in Production
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 10 min read
Best practices for data quality, retrieval design, and evaluation in production RAG systems
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-19 | โฑ๏ธ Read time: 10 min read
Best practices for data quality, retrieval design, and evaluation in production RAG systems
#DataScience #AI #Python
โค2
Forwarded from Machine Learning with Python
๐Stanford just completed a must-watch for anyone serious about AI:
๐ โ๐๐ ๐ ๐ฎ๐ต๐ฑ: ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ & ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐โ is now live entirely on YouTube and itโs pure gold.
If youโre building your AI career, stop scrolling.
This isnโt another surface-level overview. Itโs the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
๐ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐ถ๐ป๐ฐ๐น๐๐ฑ๐ฒ:
โข How Transformers actually work (tokenization, attention, embeddings)
โข Decoding strategies & MoEs
โข LLM finetuning (LoRA, RLHF, supervised)
โข Evaluation techniques (LLM-as-a-judge)
โข Optimization tricks (RoPE, quantization, approximations)
โข Reasoning & scaling
โข Agentic workflows (RAG, tool calling)
๐ง My workflow: I usually take the transcripts, feed them into NotebookLM, and once Iโve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
๐ฅ 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
๐ Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If youโre in AI โ whether building infra, agents, or apps โ this is the foundational course you donโt want to miss.
Letโs level up.
https://t.me/CodeProgrammer๐
๐ โ๐๐ ๐ ๐ฎ๐ต๐ฑ: ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ & ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐โ is now live entirely on YouTube and itโs pure gold.
If youโre building your AI career, stop scrolling.
This isnโt another surface-level overview. Itโs the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
๐ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐ถ๐ป๐ฐ๐น๐๐ฑ๐ฒ:
โข How Transformers actually work (tokenization, attention, embeddings)
โข Decoding strategies & MoEs
โข LLM finetuning (LoRA, RLHF, supervised)
โข Evaluation techniques (LLM-as-a-judge)
โข Optimization tricks (RoPE, quantization, approximations)
โข Reasoning & scaling
โข Agentic workflows (RAG, tool calling)
๐ง My workflow: I usually take the transcripts, feed them into NotebookLM, and once Iโve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
๐ฅ 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
๐ Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If youโre in AI โ whether building infra, agents, or apps โ this is the foundational course you donโt want to miss.
Letโs level up.
https://t.me/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
โค5๐ฅ2
๐ Understanding the Generative AI User
๐ Category: PRODUCT MANAGEMENT
๐ Date: 2025-12-20 | โฑ๏ธ Read time: 11 min read
What do regular technology users think (and know) about AI?
#DataScience #AI #Python
๐ Category: PRODUCT MANAGEMENT
๐ Date: 2025-12-20 | โฑ๏ธ Read time: 11 min read
What do regular technology users think (and know) about AI?
#DataScience #AI #Python
โค2
๐ EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas
๐ Category: DATA SCIENCE
๐ Date: 2025-12-20 | โฑ๏ธ Read time: 9 min read
Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends inโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2025-12-20 | โฑ๏ธ Read time: 9 min read
Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends inโฆ
#DataScience #AI #Python
โค1
๐ Tools for Your LLM: a Deep Dive into MCP
๐ Category: LLM APPLICATIONS
๐ Date: 2025-12-21 | โฑ๏ธ Read time: 8 min read
MCP is a key enabler into turning your LLM into an agent by providing itโฆ
#DataScience #AI #Python
๐ Category: LLM APPLICATIONS
๐ Date: 2025-12-21 | โฑ๏ธ Read time: 8 min read
MCP is a key enabler into turning your LLM into an agent by providing itโฆ
#DataScience #AI #Python
โค3
๐ How to Do Evals on a Bloated RAG Pipeline
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-21 | โฑ๏ธ Read time: 71 min read
Comparing metrics across datasets and models
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-21 | โฑ๏ธ Read time: 71 min read
Comparing metrics across datasets and models
#DataScience #AI #Python
โค1
๐ 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!
๐ฐ Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
๐ 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://t.me/DataScienceM
๐ง Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA โ perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
๐ฏ PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
๐พ Kaggle Data Hub
Your go-to hub for Kaggle datasets โ explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
๐งโ๐ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
๐ ML Research Hub
Advancing research in Machine Learning โ practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
๐ฌ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
๐ Python Arab| ุจุงูุซูู ุนุฑุจู
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
๐ Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksโinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
๐บ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
๐ Data Analytics
Dive into the world of Data Analytics โ uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
๐ง Learn Python Hub
Master Python with step-by-step courses โ from basics to advanced projects and practical applications.
https://t.me/Python53
โญ๏ธ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
โโโโโโโโโโโโโโโโโโ
Admin: @HusseinSheikho
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!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
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://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA โ perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets โ explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning โ practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooksโinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics โ uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses โ from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
โโโโโโโโโโโโโโโโโโ
Admin: @HusseinSheikho
Please open Telegram to view this post
VIEW IN TELEGRAM
โค2
๐ The Geometry of Laziness: What Angles Reveal About AI Hallucinations
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 12 min read
A story about failing forward, spheres you canโt visualize, and why sometimes the math knowsโฆ
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 12 min read
A story about failing forward, spheres you canโt visualize, and why sometimes the math knowsโฆ
#DataScience #AI #Python
โค1
๐ Understanding Vibe Proving
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 18 min read
How to make LLMs reason with verifiable, step-by-step logic (Part 1)
#DataScience #AI #Python
๐ Category: LARGE LANGUAGE MODELS
๐ Date: 2025-12-22 | โฑ๏ธ Read time: 18 min read
How to make LLMs reason with verifiable, step-by-step logic (Part 1)
#DataScience #AI #Python
โค1