Deep Learning Interview Questions and Answers- Part 2

Answer:

At its core, an RBM is a two-layer neural network comprising visible units and hidden units, with no connections between units within the same layer. The visible units represent the input data, and the hidden units act as latent variables that capture patterns and features in the data. Each unit in an RBM is binary, meaning it can only take on values of 0 or 1.

Answer:

Gradient descent is an iterative optimization algorithm commonly used to find the minimum of a function. There are several variants of gradient descent, each with its own advantages and drawbacks.

Answer:

Here are the top three variants:

• Batch Gradient Descent (BGD):

In BGD, the entire training dataset is used to compute the gradient of the cost function with respect to the model parameters in each iteration. It offers a more accurate estimate of the gradient since it considers all training samples. However, BGD can be computationally expensive, especially for large datasets, as it requires processing the entire dataset in each iteration.

• Stochastic Gradient Descent (SGD):

In SGD, only one random training sample is used to compute the gradient of the cost function in each iteration. It updates the model parameters more frequently, which can lead to faster convergence, especially for large datasets. However, the frequent updates can cause a lot of noise in the parameter updates and make the convergence path erratic.

• Mini-Batch Gradient Descent:

Mini-Batch Gradient Descent is a compromise between BGD and SGD. It divides the training dataset into smaller batches, and the gradient is computed based on these batches in each iteration. Mini-batch gradient descent combines the advantages of both BGD and SGD, providing a more stable and faster convergence compared to BGD while reducing the noise compared to SGD.

Answer:

Deep Autoencoders are a type of artificial neural network used in unsupervised learning tasks, particularly in the domain of Deep Learning and representation learning. They are a variation of the traditional autoencoder architecture, which is a type of neural network designed to learn efficient representations of the input data by encoding it into a lower-dimensional space and then decoding it back to its original dimension.

Answer:

Generative Adversarial Networks (GANs) are a class of machine learning models that belong to the broader category of generative models. GANs have become one of the most popular and powerful methods for generating realistic synthetic data. The fundamental idea behind GANs is to train two neural networks, called the generator and the discriminator, in a competitive setting. The generator’s primary task is to create synthetic data that resembles real data, while the discriminator’s role is to distinguish between real and generated data.

Answer:

Bagging and boosting are two ensemble learning techniques used in machine learning, including Deep Learning. Both methods aim to improve the performance and robustness of models by combining the predictions of multiple weaker models into a single, stronger model.

• Bagging is a technique that involves training multiple instances of the same model on different subsets of the training data. The subsets are created by randomly sampling the training data with replacement, which means that some examples may appear multiple times in a subset, while others may not appear at all. Each model in the ensemble is trained independently and produces its predictions. The final prediction is then obtained by aggregating the predictions of all individual models.
• Boosting is another ensemble learning technique that iteratively builds a strong model by combining multiple weak models. Unlike bagging, boosting focuses on sequentially training models, where each model tries to correct the mistakes of its predecessor. The algorithm assigns higher weights to misclassified examples, so subsequent models focus more on these difficult-to-classify instances.

Answer:

The binary step function or the Heaviside step function, is a simple activation function commonly used in Deep Learning and neural networks. It takes an input and returns a binary output based on whether the input is greater than or equal to zero.

Answer:

ReLU stands for Rectified Linear Unit, and it is an activation function commonly used in artificial neural networks and Deep Learning models. The ReLU function introduces non-linearity to the network, which allows it to learn complex patterns and representations in the data.

Answer:

Swish is an innovative self-gated activation function discovered by researchers at Google. As stated in their paper, Swish demonstrates superior performance compared to ReLU while maintaining a similar level of computational efficiency.

Answer:

Model Capacity refers to the ability of a neural network to capture and represent complex patterns or relationships in the data it is trained on. Essentially, it measures how flexible or expressive the model is in fitting the training data. A model with high capacity can learn intricate patterns and details, while a model with low capacity may struggle to capture complex relationships.

Answer:

Data normalization, also known as data standardization or feature scaling, is a crucial preprocessing step in Deep Learning and various other machine learning algorithms. It involves transforming the numerical input data to have a standard scale, often resulting in improved model performance and convergence.

Answer:

Hyperparameters are parameters that are set before the training process begins and cannot be learned directly from the data. They are used to control various aspects of the training process and model architecture, influencing how the neural network learns and generalizes from the training data. Choosing appropriate hyperparameters is crucial for achieving good performance and preventing issues like overfitting.

Answer:

Below are some common hyperparameters in Deep Learning:

• Batch Size
• Optimizer Choice
• Number of Epochs
• Regularization Parameters
• Learning Rate Schedule
• Initialization Parameters
• Architecture-related Hyperparameters

Answer:

The key difference between a shallow network and a deep network lies in the number of layers they have and their capacity to learn complex representations.

1. Shallow Network:
   • A shallow network typically consists of only a few layers, usually one or two hidden layers.
   • These networks have limited capacity to learn complex patterns and representations from data.
   • Due to their simplicity, they are easier and faster to train, especially on smaller datasets.
   • Shallow networks may struggle to capture intricate features in data, making them less suitable for solving complex problems.
   • As a result, shallow networks are better suited for simpler tasks with relatively less complex input-output mappings.
2. Deep Network:
   • A deep network, on the other hand, has a large number of layers, typically more than two, and can even contain hundreds or thousands of layers in some cases.
   • The depth of the network allows it to learn hierarchical representations of data, where each layer captures more abstract and high-level features based on the representations learned by previous layers.
   • Deep networks are capable of learning intricate patterns and can handle complex tasks effectively.
   • Training deep networks can be computationally intensive and require a large amount of data. However, advancements like transfer learning and better optimization techniques have made training deep networks more feasible.
   • Deep Learning models can automatically learn hierarchical representations from raw data, reducing the need for manual feature engineering.

Answer:

Batch Gradient Descent is an optimization algorithm commonly used in machine learning and Deep Learning to update the parameters of a model during the training process. The main goal of Batch Gradient Descent is to minimize the loss function of the model by adjusting the model’s parameters (weights and biases) in the direction that reduces the error between the predicted output and the actual target.

Answer:

Here’s how Batch Gradient Descent works:

1. Data Preparation: In machine learning, you have a dataset comprising input features (X) and corresponding target labels (Y). The algorithm aims to learn a function that maps inputs to outputs.
2. Initialization: The model’s parameters (weights and biases) are initialized randomly or using some pre-defined values.
3. Loss Function: A loss function is chosen to measure the error between the predicted output and the actual target. Common loss functions include mean squared error (MSE) for regression problems and cross-entropy for classification problems.
4. Batch Processing: Batch Gradient Descent computes the gradient of the loss function with respect to each parameter over the entire training dataset. Instead of updating the parameters after each individual data point or a small subset, it processes the entire dataset at once.
5. Gradient Computation: For each parameter, the algorithm calculates the average gradient of the loss function over all data points in the batch. This involves taking the partial derivative of the loss function with respect to each parameter.
6. Updating Parameters: After computing the gradients for all parameters, the algorithm updates the model’s parameters by moving them in the opposite direction of the gradient. The size of the step taken in the opposite direction is determined by the learning rate, which controls the step size in the parameter space.
7. Repeat: Steps 4 to 6 are repeated for a certain number of epochs or until convergence is achieved.

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Answer:

Advantages:

• It generally converges to a more stable solution compared to stochastic gradient descent (SGD).
• The use of vectorized operations can lead to efficient computation, especially on hardware with parallel processing capabilities.
• It can make more accurate updates since it considers the whole dataset.

 Disadvantages:

• It can be computationally expensive, especially for large datasets, as it processes the entire dataset in each iteration.
• It may get stuck in local minima since it considers the average gradient over the entire dataset at once.
• The learning process may slow down if the dataset does not fit entirely in memory, requiring additional memory management techniques.

Answer:

The primary purpose of activation functions is as follows:

1. Introduce non-linearity: Activation functions introduce non-linearities in the neural network model, allowing it to learn and approximate non-linear relationships between inputs and outputs. Without non-linear activation functions, the entire neural network would behave like a linear model, severely limiting its expressive power and learning capabilities.
2. Enable the network to learn complex patterns: By introducing non-linearity, activation functions enable the neural network to learn and represent intricate patterns in the data. This capability is vital for handling complex tasks, such as image recognition, natural language processing, and many other real-world applications.
3. Ensure better convergence during training: Activation functions help in reducing the training time and improving the convergence of the neural network during the learning process. Non-linearities allow gradients to flow more efficiently through the network, avoiding the vanishing gradient problem, which can hinder the training of deep networks.
4. Introduce sparsity: Some activation functions like (ReLU), lead to sparsity in the network by setting some neurons to zero. Sparse networks are computationally more efficient, require less memory, and are less prone to overfitting.
5. Standardize the output range: Activation functions can help normalize the output range of neurons, which is especially useful in cases where the network’s output needs to be constrained within a specific range.

Answer:

Yes, if a problem can be represented by a linear equation, it is possible to construct deep networks using a linear function as the activation function for each layer. However, if the problem is a composition of linear functions, it remains a linear function itself. In such cases, implementing a deep network with additional nodes will not significantly enhance the predictive capacity of the machine learning model.

Answer:

Exploding and vanishing gradients are issues that can occur during the training of deep neural networks, particularly in models with many layers. These problems affect the stability and effectiveness of the training process.