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

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

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.iss.one/DataScienceQ

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.iss.one/DataScienceQ

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.iss.one/DataScienceQ

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.iss.one/DataScienceQ

Question 30 (Intermediate - PyTorch):
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.iss.one/DataScienceQ

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.iss.one/DataScienceQ

❓ Interview question :
What is the Transformer architecture, and why is it considered a breakthrough in NLP?

❓ Interview question :
How does self-attention enable Transformers to capture long-range dependencies in text?

❓ Interview question :
What are the main components of a Transformer model?

❓ Interview question :
Why are positional encodings essential in Transformers?

❓ Interview question :
How does multi-head attention improve Transformer performance compared to single-head attention?

❓ Interview question :
What is the purpose of feed-forward networks in the Transformer architecture?

❓ Interview question :
How do residual connections and layer normalization contribute to training stability in Transformers?

❓ Interview question :
What is the difference between encoder and decoder in the Transformer model?

❓ Interview question :
Why can Transformers process sequences in parallel, unlike RNNs?

❓ Interview question :
How does masked self-attention work in the decoder of a Transformer?

❓ Interview question :
What is the role of key, query, and value in attention mechanisms?

❓ Interview question :
How do attention weights determine which parts of input are most relevant?

❓ Interview question :
What are the advantages of using scaled dot-product attention in Transformers?

❓ Interview question :
How does position-wise feed-forward network differ from attention layers in Transformers?

❓ Interview question :
Why is pre-training important for large Transformer models like BERT and GPT?

❓ Interview question :
How do fine-tuning and transfer learning benefit Transformer-based models?

❓ Interview question :
What are the limitations of Transformers in terms of computational cost and memory usage?

❓ Interview question :
How do sparse attention and linear attention address scalability issues in Transformers?

❓ Interview question :
What is the significance of model size (e.g., number of parameters) in Transformer performance?

❓ Interview question :
How do attention heads in multi-head attention capture different types of relationships in data?

#️⃣ tags: #Transformer #NLP #DeepLearning #SelfAttention #MultiHeadAttention #PositionalEncoding #FeedForwardNetwork #EncoderDecoder

By: t.iss.one/DataScienceQ 🚀

#️⃣ CNN Basics Quiz ❓

What is the primary purpose of a Convolutional Neural Network (CNN)?
A CNN is designed to process data with a grid-like topology, such as images, by using convolutional layers to automatically and adaptively learn spatial hierarchies of features.

What does the term "convolution" refer to in CNNs?
It refers to the mathematical operation where a filter (or kernel) slides over the input image to produce a feature map that highlights specific patterns like edges or textures.

Which layer in a CNN is responsible for reducing the spatial dimensions of the feature maps?
The **pooling layer**, especially **max pooling**, reduces dimensionality while retaining important information.

What is the role of the ReLU activation function in CNNs?
It introduces non-linearity by outputting the input directly if it's positive, otherwise zero, helping the network learn complex patterns.

Why are stride and padding important in convolutional layers?
Stride controls how much the filter moves at each step, while padding allows the output size to match the input size when needed.

What is feature extraction in the context of CNNs?
It’s the process by which CNNs identify and isolate relevant patterns (like shapes or textures) from raw input data through successive convolutional layers.

How does dropout help in CNN training?
It randomly deactivates neurons during training to prevent overfitting and improve generalization.

What is backpropagation used for in CNNs?
It computes gradients of the loss function with respect to each weight, enabling the network to update parameters and minimize error.

What is the main advantage of weight sharing in CNNs?
It reduces the number of parameters by allowing the same filter to be used across different regions of the image, improving efficiency.

What is a kernel in the context of CNNs?
A small matrix that slides over the input image to detect specific features, such as corners or lines.

Which layer typically follows the convolutional layers in a CNN architecture?
The **fully connected layer**, which combines all features into a final prediction.

What is overfitting in neural networks?
It occurs when a model learns the training data too well, including noise, leading to poor performance on new data.

What is data augmentation and why is it useful in CNNs?
It involves applying transformations like rotation or flipping to training images to increase dataset diversity and improve model robustness.

What is the purpose of batch normalization in CNNs?
It normalizes the inputs of each layer to stabilize and accelerate training by reducing internal covariate shift.

What is transfer learning in the context of CNNs?
It involves using a pre-trained CNN model and fine-tuning it for a new task, saving time and computational resources.

Which activation function is commonly used in the final layer of a classification CNN?
The **softmax function**, which converts raw scores into probabilities summing to one.

What is zero-padding in convolutional layers?
Adding zeros around the borders of the input image to maintain the spatial dimensions after convolution.

What is the difference between local receptive fields and global receptive fields?
Local receptive fields cover only a small region of the input, while global receptive fields capture broader patterns across the entire image.

What is dilation in convolutional layers?
It increases the spacing between kernel elements without increasing the number of parameters, allowing the network to capture larger contexts.

What is the significance of filter size in CNNs?
It determines the spatial extent of the pattern the filter can detect; smaller filters capture fine details, larger ones detect broader structures.

#️⃣ #CNN #DeepLearning #NeuralNetworks #ComputerVision #MachineLearning #ArtificialIntelligence #ImageRecognition #AI

By: @DataScienceQ 🚀