Artificial Intelligence Interview Question
Prepare for your AI job interview with our Artificial Intelligence Interview Question covering key AI concepts, ML, deep learning & more.
1. What is Artificial Intelligence (AI)?
Answer:
Artificial Intelligence (AI) is the field of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence. AI systems can analyze data, recognize patterns, make decisions, and even learn from past experiences. AI is broadly classified into:
- Narrow AI (Weak AI): AI designed for specific tasks (e.g., chatbots, recommendation systems).
- General AI (Strong AI): AI with human-like intelligence capable of reasoning and problem-solving.
- Super AI: Hypothetical AI surpassing human intelligence in all aspects.
2. What is the role of activation functions in neural networks?
Answer:
Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns. Common types include:
- Sigmoid: Maps values between 0 and 1; used in binary classification.
- ReLU (Rectified Linear Unit): Outputs zero for negative values, making training efficient.
- Tanh: Similar to sigmoid but maps values between -1 and 1, making it more suitable for centered data.
- Softmax: Used in multi-class classification problems.
3. Explain Artificial Intelligence and give its applications.
Answer:
Artificial Intelligence (AI) is the simulation of human intelligence in machines. It enables computers to perform tasks that typically require human cognition, such as learning, reasoning, problem-solving, perception, and language understanding.
- Healthcare: AI-driven diagnosis, robotic surgery, personalized treatment.
- Finance: Fraud detection, risk assessment, algorithmic trading.
- Retail: Chatbots, recommendation systems, inventory management.
- Autonomous Vehicles: Self-driving cars, traffic monitoring.
- Natural Language Processing: Virtual assistants (Siri, Alexa), sentiment analysis.
4. How are machine learning and AI related?
Answer:
Machine learning (ML) is a subset of AI that focuses on developing algorithms that enable computers to learn patterns from data and make decisions without being explicitly programmed. While AI is the broader concept of machines mimicking human intelligence, ML provides the statistical and computational techniques necessary for this intelligence to evolve.
5. What is Deep Learning based on?
Answer:
Deep Learning is based on artificial neural networks (ANNs) with multiple layers, known as deep neural networks. These networks enable AI to analyze vast amounts of data, recognize patterns, and make decisions. It is inspired by the structure and functionality of the human brain.
6. How many layers are in a Neural Network?
Answer:
A neural network consists of three main layers:
- Input Layer: Receives raw data.
- Hidden Layer(s): Extracts features, performs computations, and transforms data.
- Output Layer: Produces the final result or classification.
The number of hidden layers varies depending on the complexity of the task.
7. Explain TensorFlow.
Answer:
TensorFlow is an open-source machine learning framework developed by Google. It is widely used for AI and deep learning applications due to its efficiency, scalability, and support for both CPU and GPU computations.
Key features:
- Supports deep learning and neural networks.
- Offers flexible APIs (Keras for high-level abstraction).
- Enables distributed computing and model deployment.
8. What are the pros of cognitive computing?
Answer:
Cognitive computing mimics human thought processes to assist decision-making.
Pros:
- Enhances automation and efficiency.
- Improves human-machine interactions.
- Learns and adapts to data changes.
- Enhances problem-solving capabilities.
9. What’s the difference between NLP and NLU?
Answer:
- NLP (Natural Language Processing): Processes, understands, and generates human language.
- NLU (Natural Language Understanding): A subfield of NLP that focuses on understanding context, meaning, and intent in human language.
10. Give some examples of Weak and Strong AI.
Answer:
- Weak AI (Narrow AI): AI systems designed for specific tasks (e.g., Siri, ChatGPT, Google Search).
- Strong AI (General AI): Hypothetical AI capable of reasoning and problem-solving like humans.
11. What is the need for data mining?
Answer:
Data mining is essential for discovering patterns and insights from large datasets. It helps in fraud detection, business intelligence, healthcare diagnostics, and market analysis.
12. Name some sectors where data mining is applicable.
Answer:
- Healthcare: Predicting diseases, drug discovery.
- Finance: Fraud detection, credit scoring.
- Retail: Customer segmentation, recommendation engines.
13. What are the components of NLP?
Answer:
The key components of Natural Language Processing (NLP) include:
- Tokenization: Breaking text into words or phrases.
- Part-of-Speech Tagging: Identifying word types (nouns, verbs, adjectives, etc.).
- Named Entity Recognition (NER): Identifying names, dates, and locations in the text.
- Sentiment Analysis: Determining the emotional tone of the text.
- Machine Translation: Translating text from one language to another.
14. What is the full form of LSTM?
Answer:
LSTM stands for Long Short-Term Memory, a type of recurrent neural network (RNN) designed to handle sequential data and long-term dependencies.
15. What is Artificial Narrow Intelligence (ANI)?
Answer:
Artificial Narrow Intelligence (ANI), also known as Weak AI, refers to AI systems that are designed and trained to perform a specific task. These systems excel in their intended function but lack general intelligence. Examples include virtual assistants, recommendation systems, and facial recognition software.
16. What is a data cube?
Answer:
A data cube is a multidimensional array used in databases and data warehousing to store and analyze data efficiently. It allows users to perform operations like slicing, dicing, and drilling down into data.
17. What is the difference between model accuracy and model performance?
Answer:
- Model Accuracy: Measures the percentage of correct predictions.
- Model Performance: A broader term that includes accuracy, precision, recall, F1-score, and AUC-ROC. High accuracy does not always indicate a good model, especially with imbalanced datasets.
18. What are the different components of GAN?
Answer:
Generative Adversarial Networks (GANs) consist of:
- Generator: Creates synthetic data samples from random noise.
- Discriminator: Differentiates between real and generated samples.
These components compete against each other, improving the quality of generated outputs over time.
19. What are common data structures used in deep learning?
Answer:
- Tensors: Multi-dimensional arrays used in frameworks like TensorFlow and PyTorch.
- Matrices: Two-dimensional arrays used in linear algebra operations.
- Vectors: One-dimensional arrays representing data points or model parameters.
20. What is the role of the hidden layer in a neural network?
Answer:
Hidden layers process input data, extract important features and pass transformed data to the next layer. They enable the model to learn complex patterns.
21. Mention some advantages of neural networks.
Answer:
- Can model non-linear relationships.
- Adapt to new data through retraining.
- Handle unstructured data effectively.
22. What is the difference between stemming and lemmatization?
Answer:
- Stemming: Trims words to their root form (e.g., “running” → “run”).
- Lemmatization: Converts words to their dictionary base form (e.g., “better” → “good”).
23. What are the different types of text summarization?
Answer:
Text summarization can be categorized into:
- Extraction-based Summarization: Identifies key phrases and sentences from the original text.
- Abstraction-based Summarization: Generates new phrases and sentences to capture the essence of the original text.
24. What is the meaning of corpus in NLP?
Answer:
A corpus in NLP refers to a large collection of structured text data used for training and evaluating natural language processing models.
25. Explain the binarizing of data.
Answer:
Binarizing data involves converting numerical or categorical variables into binary format (0s and 1s) to improve the performance of machine learning models.
26. What is perception and its types?
Answer:
Perception in AI refers to how machines interpret sensory data. Types include:
- Visual Perception: Image and video recognition.
- Auditory Perception: Speech recognition and processing.
- Tactile Perception: Haptic feedback in robotics.
27. Give some pros and cons of decision trees.
Answer:
Pros:
- Easy to interpret and visualize.
- Handles both numerical and categorical data.
Cons:
- Prone to overfitting.
- Can become complex with large datasets.
28. Explain the marginalization process.
Answer:
Marginalization is used in probability theory to eliminate certain variables from a joint distribution by summing or integrating them.
29. What is the function of an artificial neural network?
Answer:
Artificial Neural Networks (ANNs) process input data through interconnected neurons to recognize patterns and make decisions.
30. Explain cognitive computing and its types.
Answer:
Cognitive computing mimics human thought processes. Types include:
- Machine Learning-Based Cognitive Computing
- Natural Language Processing-Based Systems
- Computer Vision-Based Systems
31. Explain the function of deep learning frameworks.
Answer:
Deep learning frameworks like TensorFlow and PyTorch provide pre-built functions for designing, training, and deploying neural networks.
32. How are speech recognition and video recognition different?
Answer:
- Speech Recognition: Converts spoken language into text.
- Video Recognition: Analyzes video frames to detect objects, faces, or actions.
33. What is the pooling layer on CNN?
Answer:
A pooling layer in a Convolutional Neural Network (CNN) reduces the spatial dimensions of feature maps to improve computational efficiency.
34. What is the purpose of a Boltzmann machine?
Answer:
A Boltzmann Machine is a probabilistic graphical model used for feature learning and optimization problems.
35. What do you mean by regular grammar?
Answer:
Regular grammar defines formal language rules, used in automata theory and NLP for text parsing.
36. How do you obtain data for NLP projects?
Answer:
Data for NLP projects can be obtained from:
- Public datasets (e.g., Wikipedia, Common Crawl)
- Web scraping
- Proprietary databases
37. Explain regular expression in layman’s terms.
Answer:
A regular expression (regex) is a sequence of characters used to search, match, or manipulate text patterns.
38. How is NLTK different from spaCy?
Answer:
- NLTK: Good for research, and provides multiple NLP tools.
- spaCy: Optimized for production, and faster processing.
39. Name some best tools useful in NLP.
Answer:
- NLTK
- spaCy
- Gensim
- Hugging Face Transformers
40. Are chatbots derived from NLP?
Answer:
Yes, chatbots use NLP techniques like intent recognition and entity extraction to process user queries.
41. What are the main components of LSTM?
Answer:
LSTM consists of three gates:
- Forget Gate: Decide what information to discard.
- Input Gate: Updates cell state.
- Output Gate: Produces the next hidden state.
42. Give some benefits of transfer learning.
Answer:
- Reduces training time.
- Requires less data.
- Improves model accuracy.
43. Explain the importance of the cost/loss function.
Answer:
The cost/loss function quantifies how well a model’s predictions match the actual values, guiding optimization algorithms.
44. Define the following terms – Epoch, Batch, and Iteration.
Answer:
- Epoch: One complete pass through the entire training dataset.
- Batch: A subset of the training data processed at one time.
- Iteration: A single update step in training, processing one batch.
45. Explain dropouts.
Answer:
Dropout is a regularization technique in neural networks where random neurons are ignored during training to prevent overfitting. It improves model generalization by reducing dependency on specific neurons.
46. Explain the vanishing gradient problem.
Answer:
The vanishing gradient problem occurs in deep neural networks when gradients become very small during backpropagation, preventing weight updates and slowing learning. Solutions include using ReLU activation and batch normalization.
47. Explain the function of batch gradient descent.
Answer:
Batch gradient descent computes the gradient using the entire dataset before updating weights. It ensures a stable convergence but is computationally expensive for large datasets.
48. What is an ensemble learning method?
Answer:
Ensemble learning combines multiple models to improve accuracy and robustness. Methods include:
- Bagging (e.g., Random Forest): Reduces variance.
- Boosting (e.g., AdaBoost, XGBoost): Reduces bias and variance.
- Stacking: Combines diverse models for stronger performance.
49. What are some drawbacks of machine learning?
Answer:
- Data Dependency: Requires large datasets for accuracy.
- Computational Cost: Training deep models are expensive.
- Interpretability: Some models (e.g., deep learning) lack transparency.
- Bias & Fairness Issues: Models can inherit bias from training data.
50. Explain Sentiment Analysis in NLP.
Answer:
Sentiment analysis is an NLP technique that determines the emotional tone of text. It is used in:
- Social media monitoring (e.g., analyzing tweets).
- Customer feedback analysis (e.g., reviews, surveys).
- Stock market prediction based on news sentiment.
51. What are the BFS and DFS algorithms?
Answer:
- Breadth-First Search (BFS): Explores neighbors before moving deeper.
- Depth-First Search (DFS): Explores as deep as possible before backtracking.
Used in graph traversal, AI pathfinding, and decision trees.
52. Explain the difference between supervised and unsupervised learning.
Answer:
- Supervised Learning: Uses labeled data; example: spam detection.
- Unsupervised Learning: Uses unlabeled data; for example: clustering customers by behavior.
53. What is the text extraction process?
Answer:
Text extraction involves identifying relevant information from unstructured text data using NLP techniques such as Named Entity Recognition (NER) and pattern matching.
54. What are some disadvantages of linear models?
Answer:
- Assumes linear relationships: Cannot model complex patterns.
- Sensitive to outliers: Can be easily distorted.
- Limited flexibility: Does not perform well on non-linear data.
55. Mention methods for reducing dimensionality.
Answer:
- Principal Component Analysis (PCA): Reduces correlated features.
- t-SNE: Visualizes high-dimensional data in two or three dimensions.
- Autoencoders: Neural network-based dimensionality reduction.
56. Explain the cost function.
Answer:
The cost function measures how well a model’s predictions match actual values. Example:
- Mean Squared Error (MSE): Used for regression problems.
- Cross-Entropy Loss: Used for classification tasks.
57. Mention hyperparameters of ANN.
Answer:
- Learning rate: Controls weight update magnitude.
- Number of layers: Determines model depth.
- Batch size: Defines training data subset per iteration.
- Dropout rate: Prevents overfitting.
58. Explain intermediate tensors. Do sessions have a lifetime?
Answer:
Intermediate tensors store temporary computations in deep learning frameworks like TensorFlow. Sessions have a lifetime until explicitly closed or the program terminates.
59. Explain exploding variables.
Answer:
Exploding variables occur when gradients become excessively large during training, causing unstable learning. Mitigation techniques include gradient clipping and careful initialization.
60. Is it possible to build a deep learning model only using linear regression?
Answer:
No, linear regression cannot model non-linear relationships. Deep learning requires non-linear activation functions (e.g., ReLU, Sigmoid) to capture complex patterns.
61. What is the function of hyperparameters?
Answer:
Hyperparameters control model training settings and performance. Examples:
- Learning rate: Adjusts weight updates.
- Epochs: Number of passes through the dataset.
- Dropout rate: Prevents overfitting.
62. What is Artificial superintelligence (ASI)?
Answer:
ASI is a hypothetical AI that surpasses human intelligence in all aspects, including reasoning, creativity, and decision-making. It remains theoretical but raises concerns about ethical and safety implications.
63. What is overfitting, and how can it be prevented in an AI model?
Answer:
Overfitting occurs when an AI model learns noise in the training data instead of generalizing it to new data.
Prevention Techniques:
- Regularization (L1, L2): Adds penalties to prevent excessive complexity.
- Dropout: Randomly drops neurons during training.
- Cross-validation: Ensures robustness by splitting data into multiple subsets.
- Early stopping: Stops training when performance degrades on validation data.
64. What is the role of a pipeline for Information Extraction (IE) in NLP?
Answer:
A pipeline in Information Extraction (IE) automates the sequence of NLP tasks such as:
- Tokenization → Named Entity Recognition (NER) → Relation Extraction → Event Detection.
65. What is the difference between the full listing hypothesis and the minimum redundancy hypothesis?
Answer:
- Full Listing Hypothesis: Assumes all features are necessary for accurate predictions.
- Minimum Redundancy Hypothesis: Focuses on selecting only non-redundant, highly relevant features to improve efficiency.
66. Mention the steps of the gradient descent algorithm.
Answer:
- Initialize parameters randomly.
- Compute the cost function.
- Calculate gradients using backpropagation.
- Update parameters using gradient values.
- Repeat until convergence.
67. Write a function to create one-hot encoding for categorical variables in a Pandas DataFrame.
Answer:
import pandas as pd
def one_hot_encode(df, column):
return pd.get_dummies(df, columns=[column])
68. Implement a function to calculate cosine similarity between two vectors.
Answer:
import numpy as np
def cosine_similarity(vec1, vec2):
dot_product = np.dot(vec1, vec2)
magnitude = np.linalg.norm(vec1) * np.linalg.norm(vec2)
return dot_product / magnitude
69. How to handle an imbalanced dataset?
Answer:
- Oversampling minority class (SMOTE).
- Undersampling majority class.
- Using weighted loss functions.
70. How do you solve the vanishing gradient problem in RNN?
Answer:
- Use LSTM or GRU instead of vanilla RNNs.
- Apply batch normalization.
- Use ReLU activation instead of sigmoid/tanh.
71. Implement a function to normalize a given list of numerical values between 0 and 1.
Answer:
def normalize_data(data):
min_val = min(data)
max_val = max(data)
return [(x – min_val) / (max_val – min_val) for x in data]
72. Write a Python function to sort a list of numbers using the merge sort algorithm.
Answer:
def merge_sort(arr):
if len(arr) <= 1:
return arr
mid = len(arr) // 2
left = merge_sort(arr[:mid])
right = merge_sort(arr[mid:])
return merge(left, right)
def merge(left, right):
result = []
i = j = 0
while i < len(left) and j < len(right):
if left[i] < right[j]:
result.append(left[i])
i += 1
else:
result.append(right[j])
j += 1
result.extend(left[i:])
result.extend(right[j:])
return result
73. Explain the purpose of Sigmoid and Softmax functions.
Answer:
- Sigmoid: Outputs values between 0 and 1, used for binary classification.
- Softmax: Converts logits into probabilities, used for multi-class classification.
74. Implement a Python function to calculate the sigmoid activation function value for any given input.
Answer:
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
75. Write a Python function to calculate R-squared (coefficient of determination) given true and predicted values.
Answer:
import numpy as np
def r_squared(y_true, y_pred):
ss_res = np.sum((y_true – y_pred) ** 2)
ss_tot = np.sum((y_true – np.mean(y_true)) ** 2)
return 1 – (ss_res / ss_tot)
76. Explain pragmatic analysis in NLP.
Answer:
Pragmatic analysis interprets the meaning of the text based on context and real-world knowledge, crucial for sentiment analysis and chatbot responses.
77. What is the difference between collaborative and content-based filtering?
Answer:
- Collaborative Filtering: Recommends items based on user preferences.
- Content-Based Filtering: Recommends items similar to previously liked ones.
78. How is parsing achieved in NLP?
Answer:
Parsing analyzes sentence structure using:
- Syntactic Parsing: Determines grammatical structure.
- Dependency Parsing: Identifies relationships between words.
79. Implement a Python function to calculate the precision and recall of a binary classifier.
Answer:
def precision_recall(y_true, y_pred):
tp = sum((y_pred == 1) & (y_true == 1))
fp = sum((y_pred == 1) & (y_true == 0))
fn = sum((y_pred == 0) & (y_true == 1))
precision = tp / (tp + fp)
recall = tp / (tp + fn)
return precision, recall
80. How can you standardize data?
Answer:
Standardizing data transforms it to have a mean of 0 and a standard deviation of 1, ensuring all features contribute equally to the model.
Formula:
Implementation in Python:
from sklearn.preprocessing import StandardScaler
import numpy as np
data = np.array([[50, 200], [30, 100], [20, 80]])
scaler = StandardScaler()
standardized_data = scaler.fit_transform(data)
print(standardized_data)
81. How to implement the Naïve Bayes algorithm in Python?
Answer:
Naïve Bayes is a probabilistic classifier based on Bayes’ Theorem. It is widely used for spam detection and text classification.
Implementation in Python:
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
# Load dataset
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
# Train Naïve Bayes model
nb = GaussianNB()
nb.fit(X_train, y_train)
# Predict
predictions = nb.predict(X_test)
print(predictions)
82. Write a code to visualize data using univariate plots.
Answer:
Univariate plots show the distribution of a single variable.
Implementation in Python:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# Generate random data
data = np.random.randn(1000)
# Create histogram
sns.histplot(data, bins=30, kde=True)
plt.show()
83. How does information gain and entropy work in decision trees?
Answer:
- Entropy: Measures impurity in a dataset.
- Information Gain: Measures how much uncertainty is reduced after a split.
84. Write a code for random forest regression in Python.
Answer:
Random Forest is an ensemble learning method that builds multiple decision trees.
Implementation in Python:
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
# Generate sample dataset
X, y = make_regression(n_samples=100, n_features=4, noise=0.2, random_state=42)
# Train Random Forest model
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(X, y)
# Predict
predictions = rf.predict(X[:5])
print(predictions)
85. Explain the use of kernel tricks.
Answer:
Kernel tricks allow Support Vector Machines (SVMs) to transform non-linearly separable data into higher dimensions where it becomes linearly separable.
Common Kernel Functions:
86. Write a code for the K-Nearest Neighbors (KNN) algorithm in Python.
Answer:
KNN is a simple classification algorithm that assigns labels based on the majority class of k-nearest neighbors.
Implementation in Python:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
# Load dataset
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
# Train KNN model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
# Predict
predictions = knn.predict(X_test)
print(predictions)
87. What are the key differences between image classification, object detection, and image segmentation?
Answer:
Image Classification: Assigns a single label to an entire image.
Object Detection: Identifies multiple objects in an image along with their locations using bounding boxes.
Image Segmentation: Divides an image into regions, with semantic segmentation classifying pixels and instance segmentation distinguishing between object instances.
88. How does a Convolutional Neural Network (CNN) work?
Answer:
CNNs use convolutional layers to extract spatial features from images.
Pooling layers reduce dimensions while retaining essential features.
Fully connected layers classify extracted features.
89. What are some real-world applications of computer vision?
Answer:
Healthcare: Medical imaging analysis.
Autonomous Vehicles: Object detection and lane tracking.
Security: Facial recognition.
Retail: Inventory management and self-checkout systems.
Manufacturing: Quality inspection.
90. What are the differences between supervised, unsupervised, and self-supervised learning in computer vision?
Answer:
Supervised Learning: Uses labeled datasets for training (e.g., image classification).
Unsupervised Learning: Detects patterns without labeled data (e.g., clustering).
Self-Supervised Learning: Uses part of the data to create labels automatically (e.g., contrastive learning).
91. What are common architectures used in computer vision?
Answer:
VGG: Simple but deep architecture.
ResNet: Uses residual connections to prevent vanishing gradients.
EfficientNet: Optimized for performance and efficiency.
92. How does a Fully Convolutional Network (FCN) differ from a traditional CNN?
Answer:
FCNs use only convolutional layers for segmentation, while traditional CNNs use fully connected layers for classification.
93. What is the role of transfer learning in computer vision?
Answer:
Allows using pre-trained models on new tasks to reduce training time and improve performance.
94. How does Named Entity Recognition (NER) work in Natural Language Processing, and what are its primary challenges?
Answer:
Named Entity Recognition (NER) is a key NLP task that identifies entities such as names, organizations, locations, dates, and more within a text. It uses machine learning models, rule-based approaches, or deep learning methods like transformers. Challenges include handling ambiguous entity names, dealing with variations in spelling, recognizing unseen entities, and managing domain-specific terminology.
95. What role does tokenization play in NLP, and how do different tokenization techniques affect model performance?
Answer: Tokenization is the process of breaking text into smaller units (tokens), such as words or subwords, which serve as input for NLP models. Different techniques include whitespace-based, word-based, and subword-based tokenization (e.g., Byte-Pair Encoding or WordPiece). The choice of tokenization affects model accuracy, computational efficiency, and the ability to handle out-of-vocabulary words.
96. How does sentiment analysis work in NLP, and what are the common challenges faced in real-world applications
Answer: Sentiment analysis classifies text as positive, negative, or neutral using lexicon-based, machine-learning, or deep-learning approaches. Challenges include detecting sarcasm, handling domain-specific language, dealing with mixed sentiments in long texts, and adapting models to different cultures or languages with varying sentiment expressions.
97. What is word embedding in NLP, and how do models like Word2Vec, GloVe, and BERT differ in representing words?
Answer: Word embeddings convert words into dense vector representations to capture semantic meaning. Word2Vec and GloVe use static word embeddings, meaning each word has a fixed vector. In contrast, BERT uses contextual embeddings, where word representations change based on their context in a sentence, improving performance in many NLP tasks like sentiment analysis and question answering.
98. How do transformers improve NLP tasks compared to traditional models like RNNs and LSTMs?
Answer: Transformers, introduced in the “Attention Is All You Need” paper, use self-attention mechanisms to process entire sequences in parallel, unlike RNNs and LSTMs, which rely on sequential processing. This allows transformers to capture long-range dependencies more effectively, making them superior for tasks like machine translation, text summarization, and language modeling. However, their high computational cost is a challenge in deployment.
99. What is the fundamental architecture of a Convolutional Neural Network (CNN), and how does it differ from traditional fully connected networks
Answer: A CNN consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers apply filters to extract spatial features while pooling layers reduce dimensionality and prevent overfitting. Fully connected layers at the end help make predictions. Unlike traditional neural networks, CNNs leverage local connections and weight sharing, reducing computational complexity and improving performance in image-related tasks.
100. How do convolutional layers extract features from images, and what is the significance of filters and kernels?
Answer: Convolutional layers apply small learnable filters (kernels) to an input image to detect patterns such as edges, textures, and shapes. Each filter slides across the image, performing element-wise multiplication and summing the values to generate a feature map. The depth of feature maps increases as deeper layers capture more abstract features, enabling CNNs to recognize complex patterns and structures.
101. What is the role of pooling layers in CNNs, and how do max pooling and average pooling differ?
Answer: Pooling layers reduce the spatial dimensions of feature maps while preserving important information. Max pooling selects the highest value from a region, enhancing dominant features, whereas average pooling computes the mean of values, retaining more global information. Max pooling is more commonly used since it helps capture essential features while reducing overfitting and computational costs.
102. How do CNNs handle overfitting, and what techniques are commonly used to improve generalization?
Answer: CNNs handle overfitting using techniques like dropout, data augmentation, and batch normalization. Dropout randomly deactivates neurons during training, preventing reliance on specific features. Data augmentation artificially increases training data by applying transformations (e.g., rotation, flipping). Batch normalization normalizes activations across mini-batches, improving stability and training speed.
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