Top 30 Deep Learning Interview Questions and Answers

MI stands for Myocardial Infarction, commonly known as a heart attack.

• MI occurs when blood flow to the heart is blocked, causing damage to the heart muscle.

• Symptoms include chest pain, shortness of breath, and nausea.

• Treatment may include medications, lifestyle changes, and medical procedures such as angioplasty or bypass surgery.

DL architectures like CNNs require more computing power due to their complex structure and operations.

• The architecture of Ai Ml is simpler compared to other DL architectures like CNNs.

• It uses a single layer of neurons which reduces the number of computations required.

• It also uses a linear activation function which is computationally less expensive.

• Ai Ml is suitable for simpler tasks like linear regression and classification.

• In contrast, CNNs are used for complex tasks like im...read more

The lr function is used in load testing to parameterize values in scripts.

• The lr function is specific to LoadRunner, a performance testing tool.

• It allows testers to replace hard-coded values with dynamic data during script execution.

• The lr function can be used to simulate different user behaviors and data inputs.

• For example, lr_eval_string can be used to extract values from server responses and use them in subsequent requests.

Logit is the log-odds of the probability, while probabilities are the actual probabilities of an event occurring.

• Logit is the natural logarithm of the odds ratio, used in logistic regression.

• Probabilities are the actual likelihood of an event occurring, ranging from 0 to 1.

• In deep learning, logit values are transformed into probabilities using a softmax function.

• Logit values can be negative or positive, while probabilities are always between 0 and 1.

Deep learning is used over statistical models for complex, non-linear relationships in data.

• Deep learning can automatically learn hierarchical representations of data, capturing intricate patterns and relationships.

• Statistical models may struggle with high-dimensional data or non-linear relationships, where deep learning excels.

• Deep learning can handle unstructured data like images, audio, and text more effectively than traditional statistical models.

• Examples include image re...read more

Understanding deep equations and algorithms in DL and ML is crucial for a data scientist.

• Deep learning involves complex neural network architectures like CNNs and RNNs.

• Machine learning algorithms include decision trees, SVM, k-means clustering, etc.

• Understanding the math behind algorithms helps in optimizing model performance.

• Equations like gradient descent, backpropagation, and loss functions are key concepts.

• Practical experience with implementing algorithms in Python or R i...read more

Object detection using CNN involves training a neural network to identify and locate objects within an image.

• CNNs use convolutional layers to extract features from images

• These features are then passed through fully connected layers to classify and locate objects

• Common architectures for object detection include YOLO, SSD, and Faster R-CNN

Mobilenet is a lightweight DL architecture designed for mobile devices.

• Mobilenet uses depthwise separable convolutions to reduce computation and model size.

• It has fewer parameters and lower computational requirements compared to other DL architectures like VGG and ResNet.

• Mobilenet is optimized for mobile devices and can run in real-time on smartphones and other embedded devices.

• Other DL architectures like VGG and ResNet are designed for high accuracy on large datasets and req...read more

To build a deep learning regression model, we need to choose appropriate architecture, loss function, optimizer and train the model with data.

• Choose appropriate architecture such as feedforward neural network, convolutional neural network, recurrent neural network, etc.

• Select appropriate loss function such as mean squared error, mean absolute error, etc.

• Choose appropriate optimizer such as stochastic gradient descent, Adam, etc.

• Preprocess the data and split it into training a...read more

No, the deepness of a model depends on the complexity of the problem and the number of abstract features it needs to learn.

• The number of layers and neurons can affect the performance and accuracy of the model, but it's not the only factor.

• A simple problem may require only a few layers and neurons, while a complex problem may need more.

• The deepness of a model can also be influenced by the type of layers used, such as convolutional or recurrent layers.

• For example, a deep neural...read more

ML is a subset of AI that involves training models on data, while DL is a subset of ML that uses neural networks to learn from data.

• ML involves using algorithms to learn patterns from data

• DL is a type of ML that uses neural networks to learn from data

• DL requires large amounts of data and computing power

• DL is used for complex tasks such as image and speech recognition

• ML is used for a wide range of applications such as fraud detection and recommendation systems

I have extensive knowledge in operating and maintaining various machines, including industrial equipment and computer systems.

• Experience with troubleshooting and repairing mechanical equipment

• Proficiency in programming and operating computer-controlled machinery

• Familiarity with maintenance procedures for industrial machines

• Knowledge of safety protocols for operating heavy machinery

LSTM addresses vanishing gradient problem in RNN and captures long-term dependencies better than GRU.

• LSTM has three gates (input, output, forget) which help in preserving long-term dependencies.

• GRU has two gates (reset, update) which are simpler but may not capture long-term dependencies as effectively as LSTM.

• LSTM is better at avoiding vanishing gradient problem compared to RNN due to its gating mechanism.

• LSTM is more complex and computationally expensive than GRU.

Optimizers in Deep Learning Models are algorithms used to minimize the loss function by adjusting the weights of the neural network.

• Optimizers help in updating the weights of the neural network during training to minimize the loss function.

• Popular optimizers include Adam, SGD, RMSprop, and Adagrad.

• Each optimizer has its own way of updating the weights based on gradients and learning rate.

• Choosing the right optimizer can significantly impact the training process and model perf...read more

Max pooling is a down-sampling technique in deep learning where the maximum value from a set of values is selected.

• Max pooling reduces the spatial dimensions of the input data by selecting the maximum value from a set of values in a specific window.

• It helps in reducing the computational complexity and controlling overfitting in the model.

• Example: In a 2x2 max pooling operation, the maximum value from each 2x2 window of the input data is selected to create a down-sampled outpu...read more

Deep learning is a subset of machine learning that uses neural networks to model and solve complex problems.

• Deep learning involves neural networks with multiple layers to learn complex patterns and representations.

• Machine learning uses algorithms to learn patterns and make predictions based on data.

• Deep learning requires large amounts of data and computational power compared to traditional machine learning techniques.

• Examples of deep learning include image and speech recognit...read more

Deep learning (DL) can be used instead of machine learning (ML) for more complex tasks and larger datasets.

• DL is suitable for tasks requiring high levels of abstraction and complex patterns.

• DL can handle unstructured data like images, audio, and text more effectively than ML.

• DL requires more data and computational power compared to ML.

• DL models often have more layers and parameters than ML models.

• Example: Using DL for image recognition tasks like object detection or facial re...read more

ML focuses on algorithms that can learn from and make predictions on data, while DL is a subset of ML that uses neural networks to model and process complex patterns.

• ML uses algorithms to learn from and make predictions on data, while DL uses neural networks to model and process complex patterns

• ML requires feature extraction and selection, while DL automatically learns features from the data

• DL is a subset of ML that focuses on deep neural networks with multiple layers for pro...read more

Filters in CNN are small matrices used to extract features from input data by performing convolution operations.

• Filters are applied to small regions of the input data to detect specific patterns or features.

• Each filter slides over the input data and performs element-wise multiplication followed by summation to produce a feature map.

• Filters are learned during the training process to capture important features for the task at hand.

• Common filter sizes are 3x3 or 5x5, and multipl...read more

ML stands for machine learning, while DL stands for deep learning. DL is a subset of ML that uses neural networks to model and solve complex problems.

• ML (Machine Learning) is a broader concept that involves algorithms and models that can learn from and make predictions or decisions based on data.

• DL (Deep Learning) is a subset of ML that uses neural networks with multiple layers to model and solve complex problems.

• DL requires a large amount of data and computational power comp...read more

Machine learning is a subset of AI that allows systems to learn from data and make predictions, while deep learning is a subset of machine learning that uses neural networks to model and process data.

• Machine learning is a broader concept that involves algorithms that can learn from and make predictions on data.

• Deep learning is a subset of machine learning that uses neural networks with multiple layers to model and process data.

• Deep learning requires a large amount of data and...read more

DL uses neural networks with multiple layers to learn complex patterns, while ML uses algorithms to learn from data.

• DL uses deep neural networks with multiple layers, while ML uses simpler algorithms like decision trees or SVMs.

• DL requires large amounts of data to train effectively, while ML can work with smaller datasets.

• DL is more computationally intensive and requires more processing power compared to ML.

• DL is better suited for tasks like image and speech recognition, whil...read more

Handling class imbalance in CNN involves techniques like data augmentation, re-sampling, and using weighted loss functions.

• Use data augmentation techniques like rotation, flipping, and scaling to generate more samples of minority class

• Apply re-sampling methods like over-sampling (SMOTE) or under-sampling to balance the class distribution

• Utilize weighted loss functions to give more importance to minority class during training

• Consider using ensemble methods or transfer learning...read more

The algorithm used in the project is LSTM.

• LSTM stands for Long Short-Term Memory and is a type of recurrent neural network.

• It is commonly used for sequential data analysis such as time series forecasting, speech recognition, and natural language processing.

• LSTM networks have the ability to remember long-term dependencies and avoid the vanishing gradient problem.

• They consist of memory cells, input gates, output gates, and forget gates.

• Example applications of LSTM include predi...read more

Machine learning is a subset of artificial intelligence that focuses on developing algorithms to make predictions based on data, while deep learning is a subset of machine learning that uses neural networks to learn from large amounts of data.

• Machine learning involves developing algorithms that can learn from and make predictions or decisions based on data.

• Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn from large amounts ...read more

Hyperparameters are settings that are external to the model and are used to control the learning process of deep learning algorithms.

• Hyperparameters for neural networks include learning rate, batch size, number of layers, number of neurons per layer, activation functions, and dropout rate.

• For example, in a convolutional neural network, hyperparameters may include filter size, stride, and padding.

• Hyperparameters can be tuned using techniques like grid search, random search, an...read more

Encoder-decoder models are popular in sequence-to-sequence tasks, with advantages like flexibility and disadvantages like potential information loss.

• Advantages: flexibility in handling variable length inputs/outputs, ability to learn complex patterns, widely used in machine translation tasks (e.g. Google Translate)

• Disadvantages: potential information loss during encoding/decoding process, difficulty in capturing long-range dependencies, computationally expensive

Transformers are a type of deep learning model that uses self-attention mechanisms to process sequential data.

• Transformers are based on the attention mechanism, allowing the model to focus on different parts of the input sequence.

• They have been widely used in natural language processing tasks such as machine translation, text generation, and sentiment analysis.

• Examples of transformer models include BERT, GPT, and TransformerXL.