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Answer:- Deep Learning is a subset of Machine Learning that focuses on artificial neural networks with multiple layers. It allows models to automatically learn representations from large amounts of data and is widely used in areas like image recognition, natural language processing, and autonomous driving.
Answer:- Deep Learning differs from traditional Machine Learning in its ability to automatically extract features from raw data using multiple layers of neural networks. Traditional ML models often require manual feature engineering, whereas deep learning models learn hierarchical representations directly from data.
Answer:- An Artificial Neural Network (ANN) is a computational model inspired by the human brain. It consists of interconnected layers of nodes (neurons) that process information through weighted connections and activation functions.
Answer:- The main types of neural networks include Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Autoencoders, and Generative Adversarial Networks (GANs).
Answer:- A neural network typically consists of an input layer (receives data), hidden layers (processes data using weights and activation functions), and an output layer (produces predictions or classifications).
Answer:- An activation function introduces non-linearity into a neural network, allowing it to learn complex patterns. Common activation functions include ReLU, Sigmoid, Tanh, and Softmax.
Answer:- ReLU (Rectified Linear Unit) is an activation function that outputs the input value if it’s positive and zero otherwise. It is commonly used in hidden layers of deep networks due to its simplicity and effectiveness in handling vanishing gradients.
Answer:- Backpropagation is an optimization algorithm used to update neural network weights by calculating the gradient of the loss function and adjusting the weights using gradient descent.
Answer:- A loss function measures the difference between the predicted output and the actual output. Common loss functions include Mean Squared Error (MSE) for regression and Cross-Entropy Loss for classification.
Answer:- Supervised learning involves training a model using labeled data, whereas unsupervised learning finds patterns and structures in unlabeled data. Examples include classification (supervised) and clustering (unsupervised).
Answer:- An optimizer adjusts the weights of a neural network to minimize the loss function. Common optimizers include Stochastic Gradient Descent (SGD), Adam, RMSprop, and Adagrad.
Answer:- SGD updates weights based on individual data samples, whereas Adam combines momentum and adaptive learning rates for faster convergence and better performance on complex datasets.
Answer:- Overfitting occurs when a model learns patterns specific to the training data but fails to generalize well to new data. It happens due to excessive complexity or too many parameters.
Answer:- Overfitting can be prevented using techniques like regularization (L1/L2), dropout, data augmentation, and early stopping.
Answer:- Dropout is a regularization technique where randomly selected neurons are ignored during training to prevent overfitting and improve model generalization.
Answer:- A CNN (Convolutional Neural Network) is used for image processing, while an RNN (Recurrent Neural Network) is designed for sequential data like time series and natural language processing.
Answer:- A convolutional layer applies filters to an input image to extract important features like edges, shapes, and textures.
Answer:- Pooling reduces the spatial dimensions of feature maps to decrease computation and improve feature robustness. Common types include max pooling and average pooling.
Answer:- Swish is defined as f(x) = x * sigmoid(x). It provides smooth gradients and improves performance over ReLU in some cases.
Answer:-Transfer learning is the process of reusing a pre-trained model on a new dataset, reducing the training time and improving performance, especially for small datasets.
Answer:- Autoencoders are neural networks used for unsupervised learning tasks such as dimensionality reduction and anomaly detection by encoding and decoding data.
Answer:- Batch normalization normalizes inputs of each layer to improve stability, speed up training, and reduce internal covariate shifts.
Answer:- LSTMs (Long Short-Term Memory networks) are a type of RNN that solve the vanishing gradient problem by using memory cells to retain long-term dependencies in sequential data.
Answer:- An embedding layer converts categorical text data (words) into dense vector representations, making them suitable for deep learning models.
Answer:-Word embeddings, such as Word2Vec and GloVe, represent words as continuous vector spaces where semantically similar words are closer in distance.
Answer:- Softmax is an activation function used in the output layer of classification models to convert logits into probability distributions.
Answer:- One-hot encoding represents categorical variables as binary vectors, with one value set to 1 and all others set to 0.
Answer:- Gradient clipping limits the gradient magnitude to prevent exploding gradients during backpropagation.
Answer:- Reinforcement learning is a type of ML where an agent learns by interacting with the environment and receiving rewards or penalties.
Answer:- Attention mechanisms allow deep learning models to focus on relevant parts of input sequences, improving performance in NLP and vision tasks.
Answer:- A perceptron is the simplest type of artificial neuron that performs binary classification using weights and an activation function.
Answer:- An epoch represents one complete pass of the training dataset through the neural network.
Answer:- A minibatch is a small subset of the dataset used for training in each iteration to reduce computation and improve efficiency.
Answer:- Xavier initialization sets weights to maintain variance, improving training stability.
Answer:- Early stopping halts training when validation loss stops improving to prevent overfitting.
Answer:- Weight initialization is the process of setting the initial values of the network’s weights before training begins. Proper initialization helps prevent vanishing or exploding gradients. Methods like Xavier and He initialization are commonly used.
Answer:- A Boltzmann Machine is a type of stochastic neural network that learns representations by minimizing an energy function. It is mainly used for unsupervised learning and feature learning.
Answer:- The vanishing gradient problem occurs when small gradients prevent deep neural networks from learning effectively. It can be solved using activation functions like ReLU, batch normalization, residual connections (ResNets), and better weight initialization methods.
Answer:- An RBM is a special type of Boltzmann Machine with two layers: a visible layer and a hidden layer. It is used for dimensionality reduction, collaborative filtering, and feature learning.
Answer:- A Siamese Network consists of two identical neural networks with shared weights. It is used in tasks like face verification and signature verification, where similarity between inputs needs to be measured.
Answer:- A GAN consists of two neural networks—a generator that creates fake data and a discriminator that distinguishes real from fake data. The two networks compete against each other to improve the generated data quality.
Answer:- ResNet is a deep neural network architecture that uses skip (residual) connections to allow gradients to flow more easily, addressing the vanishing gradient problem. It enables training very deep networks.
Answer:- An Encoder-Decoder architecture consists of two neural networks:
Answer:- Leaky ReLU is a variant of ReLU that allows a small gradient for negative inputs instead of setting them to zero. This prevents “dying neurons” and helps improve learning.
Answer:- A Capsule Network (CapsNet) is an advanced neural network architecture that improves spatial hierarchies in image recognition. It overcomes limitations of CNNs by preserving the spatial relationships between features.
Answer:- Swish is an activation function defined as f(x) = x * sigmoid(x). It provides smooth and non-monotonic properties, helping models achieve better performance than ReLU in some cases.
Answer:- KL Divergence measures the difference between two probability distributions. It is commonly used in Variational Autoencoders (VAEs) and reinforcement learning for optimizing probabilistic models.
Answer:- A Self-Organizing Map (SOM) is an unsupervised learning technique used for clustering and visualization. It maps high-dimensional data into lower-dimensional grids, preserving topological relationships.
Answer:- The Attention Mechanism allows neural networks to focus on important parts of the input data while ignoring irrelevant information. It is widely used in transformers, machine translation, and image processing.
Answer:- The Transformer model is a neural network architecture designed for handling sequential data. It replaces RNNs with self-attention mechanisms, enabling parallel processing and improving efficiency in NLP tasks like BERT and GPT.
Answer:- A neural network consists of the following components:
Answer:- Dropout is a regularization technique that randomly drops (deactivates) a fraction of neurons during training. This prevents overfitting by making the network less reliant on specific neurons and forcing it to learn more robust features.
Answer:- Activation functions introduce non-linearity into the network, allowing it to learn complex relationships. Common activation functions include ReLU, Sigmoid, Tanh, and Swish.
Answer:- Batch normalization normalizes inputs across each mini-batch by adjusting and scaling activations. It helps in:
Answer:- A Perceptron is a single-layer neural network used for binary classification. It has only one layer of weights. An MLP is a deep neural network with multiple layers, allowing it to solve more complex problems.
Answer:- The vanishing gradient problem occurs when gradients become too small during backpropagation, preventing deep networks from learning effectively. Solutions include using activation functions like ReLU, batch normalization, and residual connections
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Answer:- Autoencoders are unsupervised neural networks used for dimensionality reduction, feature extraction, and denoising. They consist of an encoder (compressing input) and a decoder (reconstructing the original input).
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Answer:- An RNN is a neural network that processes sequential data by maintaining a hidden state across time steps. It is used in tasks like time series forecasting, speech recognition, and natural language processing.
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Answer:- LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Units) are advanced RNN architectures designed to handle long-term dependencies by using gating mechanisms to regulate information flow.
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Answer:- Transfer learning involves using a pre-trained model on a new, related task to improve performance with limited data. Common pre-trained models include ResNet, VGG, and BERT.
Answer:- An embedding layer converts categorical or textual data into dense vector representations, making them suitable for deep learning models.
Answer:- A GAN consists of a generator (creates fake data) and a discriminator (distinguishes real from fake data). It is used for generating realistic images, data augmentation, and style transfer.
Answer:- The attention mechanism enables models to focus on important parts of input sequences while processing them, improving performance in tasks like machine translation and image captioning.
Answer:- Xavier initialization sets the initial weights of a neural network based on the number of input and output neurons, preventing vanishing or exploding gradients.
Answer:- Outliers are data points that significantly differ from others.
Answer:- An optimizer updates model weights based on loss gradients. Common optimizers include SGD, Adam, RMSprop, and Adagrad.
Answer:- KL Divergence measures how one probability distribution differs from another. It is used in variational autoencoders (VAEs) and reinforcement learning.
Answer:- Reinforcement learning trains an agent to interact with an environment by receiving rewards, whereas supervised learning requires labeled data.
Answer:- Data augmentation artificially increases the training dataset by applying transformations like rotations, flips, and color adjustments, improving model generalization.
Answer:- A Transformer model uses self-attention mechanisms instead of RNNs to process sequential data efficiently. It powers models like BERT and GPT.
Answer:- Capsule Networks preserve spatial relationships between features, addressing limitations of CNNs in recognizing rotated or scaled objects.
Answer:- A SOM is an unsupervised learning technique that clusters data into a 2D grid, preserving topological relationships.
Answer:- Wasserstein Distance measures the difference between two probability distributions and is used in Wasserstein GANs (WGANs).
Answer:- Label smoothing prevents overconfidence by slightly modifying the target labels, improving model calibration.
Answer:- A VAE learns a probabilistic distribution of the input data and generates new, similar data by sampling from the learned distribution.
Answer:- BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained NLP model that understands context by analyzing words bidirectionally.
Answer: – Transformers do not have inherent sequential order like RNNs. Positional encoding helps by adding a unique representation for each word’s position in a sequence. This allows the model to understand the order of words. Typically, sinusoidal functions are used for encoding.
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Answer:- Residual connections (skip connections) allow gradients to flow directly through the network, mitigating the vanishing gradient problem. They help in training very deep networks efficiently by learning identity mappings.
Answer:-Shared weights (convolutional filters) help CNNs detect patterns (edges, textures, objects) across different spatial locations, reducing the number of parameters and improving generalization.
Answer:-Teacher forcing is a training strategy in sequence models where the true previous output is fed as input instead of the model’s predicted output. This speeds up training but may cause issues during inference if the model becomes too reliant on correct inputs.
Answer:- Attention scores measure how much focus a query word should give to each key word in a sequence. It is computed using: Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax} \left( \frac{QK^T}{\sqrt{d_k}} \right) VAttention(Q,K,V)=softmax(dkQKT)V
where QQQ, KKK, and VVV are query, key, and value matrices.
Answer:- Multi-head attention splits the input into multiple heads, each with different learned transformations. This allows the model to capture multiple perspectives of relationships in the data, improving feature learning.
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Local attention is faster but may miss long-range dependencies.
Answer:- Generative Adversarial Networks (GANs) in healthcare are used for:
Data Augmentation (generating synthetic medical data for training)
Answer:- Knowledge distillation is a technique where a large teacher model trains a smaller student model by transferring learned representations. The student model mimics the teacher’s outputs, allowing lighter models to achieve comparable performance.
Answer:- Zero-shot learning allows models to classify objects it has never seen before by leveraging semantic relationships and feature similarities, typically using pre-trained embeddings.
Answer:- Sequence-to-sequence (Seq2Seq) learning is used for tasks like machine translation and text summarization. It involves an encoder-decoder architecture where:
The decoder generates the output sequence step by step.
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Self-supervised learning trains models using automatically generated labels from raw data, without human annotations. Examples include:
Answer:- Contrastive learning trains models by bringing similar samples closer in latent space while pushing dissimilar ones apart. It is commonly used in self-supervised learning (e.g., SimCLR, MoCo).
Answer:- During training, dropout randomly deactivates neurons to prevent overfitting. During inference, all neurons remain active, and activations are scaled down to maintain consistency with training.
Answer:- Diffusion models generate high-quality images by gradually denoising a sample. The process involves:
They are used in DALL·E 2, Stable Diffusion.
Answer: – Meta-learning, or “learning to learn,” enables models to adapt quickly to new tasks with minimal data. It is used in few-shot learning, optimization strategies, and transfer learning.
Answer:- A deformable convolution dynamically adjusts the receptive field shape, allowing CNNs to focus on relevant regions of images rather than fixed grid locations. This improves performance on object detection and segmentation.
Answer:- The No Free Lunch theorem states that no single algorithm is best for all machine learning problems. The effectiveness of an algorithm depends on the specific dataset and task.
Answer:- Deep Reinforcement Learning (DRL) combines neural networks with reinforcement learning to train agents in complex environments. It consists of:
DRL is used in robotics, gaming (AlphaGo, OpenAI Gym), and self-driving cars.
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Backpropagation has several limitations:
Answer:- The Hessian matrix is a second-order derivative (Jacobian of the gradient) that captures the curvature of the loss function. It is used in:
Answer:- Catastrophic forgetting occurs when a model forgets previously learned tasks while learning new ones. This is a major issue in continual learning.
Solutions include:
Answer:- Adversarial attacks involve perturbing input data slightly so that a neural network makes incorrect predictions.
Answer:- Spectral normalization constrains the largest singular value of weight matrices, stabilizing training by preventing discriminator gradients from becoming too large. It is commonly used in Spectral Normalized GANs (SN-GANs).
Answer:- Covariance shift occurs when the distribution of the input features changes between training and inference.
Solutions include:
Answer:- Weight pruning removes unimportant weights in a neural network to reduce model size and computational cost.
Methods include:
Answer:- Progressive Growing trains a GAN by starting with low-resolution images and gradually increasing the resolution. This improves training stability and helps generate high-quality images (e.g., StyleGAN).
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Answer:- Neural Tangent Kernels describe how deep networks behave in the infinite-width limit. They help analyze gradient flow, convergence, and generalization properties.
Answer:- Gradient checkpointing reduces memory usage by recomputing certain activations instead of storing them during backpropagation. This is useful for training deep models with limited GPU memory.
Answer:- Group normalization normalizes activations across groups of channels instead of across batch samples. It is effective in small batch sizes (e.g., object detection models).
Answer:- Implicit neural representations use continuous functions to model signals (e.g., images, videos) instead of discrete data points. They are used in NeRF (Neural Radiance Fields) for 3D rendering.
Answer:- Energy-based models assign an energy score to configurations and optimize by minimizing energy for correct outputs. Examples include Hopfield networks and Boltzmann Machines.
Answer:- Capsule Networks replace traditional neurons with capsules, which encode both features and spatial relationships. They improve robustness to small transformations.
Answer:- Normalizing flows learn complex probability distributions by applying a series of invertible transformations to a simple distribution (e.g., Glow, RealNVP).
Answer:- Transformers scale using:
Answer: – Manifold learning assumes data lies on a lower-dimensional surface. Neural networks learn transformations that preserve this structure (e.g., t-SNE, UMAP).
Answer:- Contrastive learning trains models by pulling similar examples together and pushing dissimilar ones apart in latent space. Examples include SimCLR, MoCo.
Answer:- The Fisher Information Matrix measures the amount of information a model parameter holds about the data distribution. It helps in regularization and continual learning.
Answer:- A hypernetwork is a neural network that generates the weights of another neural network dynamically. Instead of using fixed weights, the hypernetwork learns to generate weights based on input conditions, improving adaptability in meta-learning, continual learning, and few-shot learning.
Answer:- Gradient blending is a technique used in multitask learning where gradients from multiple tasks are combined in an optimal way. Instead of averaging gradients, blending weighs the importance of each task’s gradients based on loss dynamics, ensuring balanced learning across tasks.
Answer:- A weight-agnostic neural network (WANN) is a neural network where the architecture, rather than weights, is responsible for learning. It is designed to function well even with randomly assigned weights, focusing on structural efficiency instead of parameter tuning.
Answer:- DEQ is a neural network model where the hidden layers converge to a fixed-point equilibrium instead of explicitly stacking multiple layers. Instead of iterating forward, it solves for equilibrium using root-finding algorithms, making it memory-efficient and scalable.
Answer:- The Sinkhorn Distance is a regularized version of the Wasserstein Distance used in optimal transport problems. It allows more efficient computation by introducing entropy regularization, helping in domain adaptation, generative modeling, and probability distribution alignment.
Answer:- RLHF is a method used in training models (e.g., ChatGPT) where reinforcement learning is guided by human preferences instead of predefined reward functions. It involves:
Answer:- MoE models divide a task among multiple expert networks, activating only a subset of them per input. This reduces computational cost while allowing specialization for different data distributions. MoEs are used in large-scale models like Google’s Switch Transformer.
Answer:- LoRA is a parameter-efficient fine-tuning method for large models. Instead of updating the full weight matrices, it adds low-rank matrices to pre-trained models, reducing computation while maintaining performance. LoRA is widely used in LLM fine-tuning.
Answer:- Sparse networks remove unnecessary weights or neurons, making inference faster while preserving accuracy. Methods like pruning, quantization, and lottery ticket hypothesis help achieve sparsity, enabling efficient deployment on low-power devices.
Answer:- A Transformer-VAE integrates transformer architectures with variational autoencoders to capture long-range dependencies in latent space. It improves generative modeling for text, images, and audio by combining the strengths of transformers and probabilistic models.
Answer:- Graph-based neural networks (GNNs) capture relationships between nodes in a graph structure, enabling models to generalize across structured data. They improve tasks like social network analysis, recommendation systems, and drug discovery.
Answer:- A differentiable renderer allows gradients to propagate through the rendering process, enabling neural networks to learn from image-based losses. It is used in 3D reconstruction, computer graphics, and neural radiance fields (NeRFs).
Answer:- Perceiver IO is a scalable transformer-based model that processes diverse data types efficiently. Unlike standard transformers, it avoids quadratic complexity by using a latent bottleneck, making it suitable for multimodal learning.
Answer:- Gradient noise adds small perturbations to gradients during training, preventing overfitting and helping escape local minima. It improves generalization and stability, particularly in stochastic gradient descent (SGD).
Answer:- Curriculum learning trains models by gradually increasing task difficulty, similar to human learning. Instead of random data, models first learn easy examples, then progressively tackle harder ones. This improves convergence speed and generalization.
Answer:- A Neural ODE is a continuous-depth neural network that models transformations as differential equations, rather than discrete layers. This enables adaptive computation and is useful in time-series prediction and physics-informed learning.
Answer:- Adversarial contrastive learning introduces perturbations to contrastive learning, forcing models to become robust against adversarial examples. It improves representation learning in self-supervised models.
Answer:- Continual Transformers adapt transformers for continual learning, preventing catastrophic forgetting while handling evolving data distributions. They use techniques like memory replay and knowledge distillation.
Answer:- Federated learning trains models across decentralized devices without sharing raw data. Only model updates are exchanged, preserving user privacy. It is widely used in healthcare and mobile AI.
Answer:- Test-time augmentation applies multiple transformations to test data (e.g., rotations, flips) and averages predictions. It enhances robustness in image classification and object detection.
Answer:- Dual encoders encode queries and documents separately, allowing efficient similarity search via dot product operations. They are widely used in semantic search and recommendation systems.
Answer:- Equivariance ensures that transformations in input space correspond to transformations in output space. CNNs, for example, are translation-equivariant, while group-equivariant networks extend this to other transformations.
Answer:- Diffusion models gradually add noise to data and learn to reverse the process, generating high-quality samples. They outperform GANs in tasks like image synthesis and text-to-image generation (e.g., Stable Diffusion).
Answer:- It focuses on learning representations with minimal labeled data. Techniques include contrastive learning, clustering-based learning, and masked autoencoders.
Answer:- Deep kernel learning combines Gaussian processes with deep learning, allowing uncertainty estimation in high-dimensional data.
Answer:- Soft prompts are learnable embeddings that guide a pre-trained model without modifying its weights. This enables efficient task adaptation in large language models (LLMs).
Answer:- Meta-Prompting dynamically adjusts prompts based on model responses, improving contextual adaptation in zero-shot and few-shot learning.
Answer:- 3D CNNs process spatiotemporal data, making them effective for video processing, medical imaging (MRI scans), and 3D object recognition.
Answer:- Self-distillation trains a model using its own predictions as soft labels, improving generalization without an external teacher.
Answer:-Learnable positional embeddings replace fixed sinusoidal embeddings in transformers, allowing models to learn task-specific positional encodings.
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