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Question 5 (Intermediate):
In a neural network, what does the ReLU activation function return?
A) 1 / (1 + e^-x)
B) max(0, x)
C) x^2
D) e^x / (e^x + 1)
#NeuralNetworks #DeepLearning #ActivationFunctions #ReLU #AI
In a neural network, what does the ReLU activation function return?
A) 1 / (1 + e^-x)
B) max(0, x)
C) x^2
D) e^x / (e^x + 1)
#NeuralNetworks #DeepLearning #ActivationFunctions #ReLU #AI
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Question 6 (Advanced):
Which of the following attention mechanisms is used in transformers?
A) Hard Attention
B) Additive Attention
C) Self-Attention
D) Bahdanau Attention
#Transformers #NLP #DeepLearning #AttentionMechanism #AI
Which of the following attention mechanisms is used in transformers?
A) Hard Attention
B) Additive Attention
C) Self-Attention
D) Bahdanau Attention
#Transformers #NLP #DeepLearning #AttentionMechanism #AI
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Question 10 (Advanced):
In the Transformer architecture (PyTorch), what is the purpose of masked multi-head attention in the decoder?
A) To prevent the model from peeking at future tokens during training
B) To reduce GPU memory usage
C) To handle variable-length input sequences
D) To normalize gradient updates
#Python #Transformers #DeepLearning #NLP #AI
✅ By: https://t.me/DataScienceQ
In the Transformer architecture (PyTorch), what is the purpose of masked multi-head attention in the decoder?
A) To prevent the model from peeking at future tokens during training
B) To reduce GPU memory usage
C) To handle variable-length input sequences
D) To normalize gradient updates
#Python #Transformers #DeepLearning #NLP #AI
✅ By: https://t.me/DataScienceQ
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Question 11 (Expert):
In Vision Transformers (ViT), how are image patches typically converted into input tokens for the transformer encoder?
A) Raw pixel values are used directly
B) Each patch is flattened and linearly projected
C) Patches are processed through a CNN first
D) Edge detection is applied before projection
#Python #ViT #ComputerVision #DeepLearning #Transformers
✅ By: https://t.me/DataScienceQ
In Vision Transformers (ViT), how are image patches typically converted into input tokens for the transformer encoder?
A) Raw pixel values are used directly
B) Each patch is flattened and linearly projected
C) Patches are processed through a CNN first
D) Edge detection is applied before projection
#Python #ViT #ComputerVision #DeepLearning #Transformers
✅ By: https://t.me/DataScienceQ
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Question 24 (Advanced - NSFW Detection):
When implementing NSFW (Not Safe For Work) content detection in Python, which of these approaches provides the best balance between accuracy and performance?
A) Rule-based keyword filtering
B) CNN-based image classification (e.g., MobileNetV2)
C) Transformer-based multimodal analysis (e.g., CLIP)
D) Metadata analysis (EXIF data, file properties)
#Python #NSFW #ComputerVision #DeepLearning
✅ By: https://t.me/DataScienceQ
When implementing NSFW (Not Safe For Work) content detection in Python, which of these approaches provides the best balance between accuracy and performance?
A) Rule-based keyword filtering
B) CNN-based image classification (e.g., MobileNetV2)
C) Transformer-based multimodal analysis (e.g., CLIP)
D) Metadata analysis (EXIF data, file properties)
#Python #NSFW #ComputerVision #DeepLearning
✅ By: https://t.me/DataScienceQ
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Question 25 (Advanced - CNN Implementation in Keras):
When building a CNN for image classification in Keras, what is the purpose of Global Average Pooling 2D as the final layer before classification?
A) Reduces spatial dimensions to 1x1 while preserving channel depth
B) Increases receptive field for better feature extraction
C) Performs pixel-wise normalization
D) Adds non-linearity before dense layers
#Python #Keras #CNN #DeepLearning
✅ By: https://t.me/DataScienceQ
When building a CNN for image classification in Keras, what is the purpose of Global Average Pooling 2D as the final layer before classification?
A) Reduces spatial dimensions to 1x1 while preserving channel depth
B) Increases receptive field for better feature extraction
C) Performs pixel-wise normalization
D) Adds non-linearity before dense layers
#Python #Keras #CNN #DeepLearning
✅ By: https://t.me/DataScienceQ
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Question 30 (Intermediate - PyTorch):
What is the purpose of
A) Disables model training
B) Speeds up computations by disabling gradient tracking
C) Forces GPU memory cleanup
D) Enables distributed training
#Python #PyTorch #DeepLearning #NeuralNetworks
✅ By: https://t.me/DataScienceQ
What is the purpose of
torch.no_grad() context manager in PyTorch? A) Disables model training
B) Speeds up computations by disabling gradient tracking
C) Forces GPU memory cleanup
D) Enables distributed training
#Python #PyTorch #DeepLearning #NeuralNetworks
✅ By: https://t.me/DataScienceQ
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Question 32 (Advanced - NLP & RNNs):
What is the key limitation of vanilla RNNs for NLP tasks that led to the development of LSTMs and GRUs?
A) Vanishing gradients in long sequences
B) High GPU memory usage
C) Inability to handle embeddings
D) Single-direction processing only
#Python #NLP #RNN #DeepLearning
✅ By: https://t.me/DataScienceQ
What is the key limitation of vanilla RNNs for NLP tasks that led to the development of LSTMs and GRUs?
A) Vanishing gradients in long sequences
B) High GPU memory usage
C) Inability to handle embeddings
D) Single-direction processing only
#Python #NLP #RNN #DeepLearning
✅ By: https://t.me/DataScienceQ
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Python Data Science Jobs & Interviews
Your go-to hub for Python and Data Science—featuring questions, answers, quizzes, and interview tips to sharpen your skills and boost your career in the data-driven world.
Admin: @Hussein_Sheikho
Admin: @Hussein_Sheikho
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Are you preparing for AI interviews or want to test your knowledge in Vision Transformers (ViT)?
Basic Concepts (Q1–Q15)
Architecture & Components (Q16–Q30)
Attention & Transformers (Q31–Q45)
Training & Optimization (Q46–Q55)
Advanced & Real-World Applications (Q56–Q65)
Answer Key & Explanations
#VisionTransformer #ViT #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #MCQ #InterviewPrep
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🚀 Comprehensive Guide: How to Prepare for a Graph Neural Networks (GNN) Job Interview – 350 Most Common Interview Questions
Read: https://hackmd.io/@husseinsheikho/GNN-interview
#GNN #GraphNeuralNetworks #MachineLearning #DeepLearning #AI #DataScience #PyTorchGeometric #DGL #NodeClassification #LinkPrediction #GraphML
Read: https://hackmd.io/@husseinsheikho/GNN-interview
#GNN #GraphNeuralNetworks #MachineLearning #DeepLearning #AI #DataScience #PyTorchGeometric #DGL #NodeClassification #LinkPrediction #GraphML
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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