Deep Learning Interview Questions and Answers

Full Stack Development with Angular is one of the most in-demand skills in top tech companies, including IDM TechPark. Mastering both frontend and backend technologies, databases, and deployment strategies makes an Angular Full Stack Developer a valuable asset in modern software development. To secure an Angular Full Stack Developer role at IDM TechPark, candidates must be proficient in technologies like HTML, CSS, JavaScript, Angular, Node.js, Express.js, MongoDB, and cloud services, as well as ready to tackle both the Angular Full Stack Online Assessment and Technical Interview Round.
To help you succeed, we have compiled a list of the Top 100 Angular Full Stack Developer Interview Questions along with their answers. Mastering these will give you a strong edge in cracking Angular Full Stack Development interviews at IDM TechPark.

1. What is Deep Learning?

Deep Learning is a subset of Machine Learning that uses multi-layered artificial neural networks to process complex data. It excels in tasks like image recognition, natural language processing (NLP), and speech recognition.

2. What are Artificial Neural Networks (ANNs)?

ANNs are computational models inspired by the human brain. They consist of:

Input layer – Receives raw data.
Hidden layers – Perform computations via neurons.
Output layer – Produces the final prediction.

3. What is the difference between Machine Learning and Deep Learning?

FeatureMachine LearningDeep Learning

Feature EngineeringRequiredAutomated

Performance on Large DataLimitedScales well

InterpretabilityMore explainableLess explainable

ExamplesDecision Trees, SVMsCNNs, RNNs, Transformers

4. What is the role of an Activation Function?

Activation functions introduce non-linearity in neural networks. Examples:

ReLU (Rectified Linear Unit) – Most commonly used.
Sigmoid – Used for binary classification.
Softmax – Used for multi-class classification.

5. What is Backpropagation?

Backpropagation is a method for training neural networks. It computes gradients using the chain rule and updates weights via gradient descent.

6. What is the Vanishing Gradient Problem?

In deep networks, gradients shrink, preventing deep layers from learning.
Solution: Use ReLU activation instead of Sigmoid or Tanh.

7. What are the types of Neural Networks?

Feedforward Neural Networks (FNNs) – Standard ANN models.
Convolutional Neural Networks (CNNs) – Best for image processing.
Recurrent Neural Networks (RNNs) – Used for sequential data (NLP, time series).
Transformer Networks – Used in NLP models like BERT, GPT.

8. What is a Convolutional Neural Network (CNN)?

CNNs are designed for image processing. Key components:

Convolutional layers extract features.
Pooling layers reduce dimensionality.
Fully connected layers make predictions.

9. What is Transfer Learning?

Transfer Learning reuses a pre-trained model (e.g., ResNet, VGG, BERT) and fine-tunes it for a new task.

10. What is the difference between LSTM and GRU?

Both are used for sequential data:

LSTM (Long Short-Term Memory) – Has three gates (input, forget, output).
GRU (Gated Recurrent Unit) – Has two gates (reset, update), making it simpler.

11. What is Batch Normalization?

Batch Normalization normalizes activations within a layer, speeding up training and reducing overfitting.

12. What is Dropout?

Dropout randomly disables neurons during training to prevent overfitting.

13. What is a Loss Function?

Loss functions measure prediction errors:

Regression: Mean Squared Error (MSE)
Classification: Cross-Entropy Loss

14. What is an Optimizer in Deep Learning?

Optimizers adjust model weights to minimize loss:

SGD (Stochastic Gradient Descent) – Simple and widely used.
Adam (Adaptive Moment Estimation) – Faster convergence.

15. What is Data Augmentation?

Data Augmentation increases training data by modifying existing samples (e.g., rotating or flipping images).

16. What are Word Embeddings?

Word Embeddings convert words into vectors. Examples:

Word2Vec
GloVe
BERT embeddings

17. What is the difference between Supervised, Unsupervised, and Reinforcement Learning?

Supervised Learning – Labeled data (e.g., image classification).
Unsupervised Learning – No labels (e.g., clustering).
Reinforcement Learning – Agent learns by rewards/punishments.

18. What is the difference between AI, Machine Learning, and Deep Learning?

AI (Artificial Intelligence) – General field for intelligent machines.
Machine Learning – AI subset using algorithms to learn patterns.
Deep Learning – ML subset using neural networks.

19. What are Attention Mechanisms in Deep Learning?

Attention mechanisms allow models to focus on important parts of the input, used in Transformers like BERT and GPT.

20. What is Explainable AI (XAI)?

XAI helps interpret deep learning models using methods like SHAP and LIME.

Coding Questions with Solutions

21. Implement a Simple Neural Network in TensorFlow

import tensorflow as tf from tensorflow import keras # Define a simple model model = keras.Sequential([ keras.layers.Dense(64, activation='relu', input_shape=(10,)), keras.layers.Dense(32, activation='relu'), keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.summary()

22. Implement a CNN using Keras

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense model = Sequential([ Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), MaxPooling2D(pool_size=(2,2)), Flatten(), Dense(128, activation='relu'), Dense(10, activation='softmax') # 10 classes ]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.summary()

23. Implement Image Data Augmentation in TensorFlow

from tensorflow.keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator( rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True ) # Example usage # train_data = datagen.flow_from_directory('train_data/')

24. Implement a Recurrent Neural Network (RNN) with LSTM

import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense model = Sequential([ LSTM(50, activation='tanh', return_sequences=True, input_shape=(10, 1)), LSTM(50, activation='tanh'), Dense(1) ]) model.compile(optimizer='adam', loss='mse') model.summary()

25. Implement Transfer Learning with ResNet50

from tensorflow.keras.applications import ResNet50 from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten # Load pre-trained ResNet50 model base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3)) # Freeze base layers for layer in base_model.layers: layer.trainable = False # Add custom classifier model = Sequential([ base_model, Flatten(), Dense(128, activation='relu'), Dense(10, activation='softmax') # 10 classes ]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.summary()

Conclusion

1. What is the Universal Approximation Theorem?

The Universal Approximation Theorem states that a feedforward neural network with a single hidden layer containing a finite number of neurons can approximate any continuous function given sufficient neurons and appropriate activation functions.

2. What are the differences between CNN, RNN, and Transformer architectures?

FeatureCNN (Convolutional NN)RNN (Recurrent NN)Transformer

Best forImages, spatial dataSequential data (text, time series)NLP, vision

MemoryNo memory of previous dataHas memory (hidden states)Uses attention mechanism

Training ParallelizationHighLowHigh

Example ModelsResNet, VGGLSTM, GRUBERT, GPT

3. Why do we use 1x1 convolutions in CNNs?

Feature compression – Reduces the number of channels.
Dimension reduction – Used in architectures like GoogleNet (Inception).
Non-linearity introduction – Adds more depth to the model.

4. What are Skip Connections in ResNet? Why are they useful?

Skip connections bypass certain layers and add their output directly to a later layer.

Solves vanishing gradient issue.
Improves gradient flow.
Enables training of very deep networks (e.g., ResNet50, ResNet101).

5. What are the different types of Pooling in CNNs?

Max Pooling: Selects the maximum value in a window (reduces spatial size).
Average Pooling: Computes the average of values in a window.
Global Pooling: Converts an entire feature map into a single value per channel.

6. What is Label Smoothing?

Label smoothing prevents overconfidence in classification models by assigning soft labels instead of one-hot encoding.
Example: Instead of [0,1,0], use [0.1, 0.8, 0.1].

7. How do GANs (Generative Adversarial Networks) work?

GANs consist of:

Generator: Creates fake samples.
Discriminator: Tries to distinguish real from fake samples.
Both networks train adversarially to improve generation quality.

8. What is the purpose of the Softmax function in classification?

Softmax converts logits into probabilities that sum to 1.
Formula:

P(yi)=ezi∑jezjP(y_i) = \frac{e^{z_i}}{\sum_{j} e^{z_j}}

9. What is the role of the Adam optimizer?

Adam combines Momentum-based Gradient Descent (RMSProp + Momentum) for fast convergence using:

Adaptive learning rates.
Momentum for smooth updates.

10. What is Knowledge Distillation?

Knowledge Distillation transfers knowledge from a large model (teacher) to a smaller model (student) while retaining performance.

11. How does Positional Encoding work in Transformers?

Since Transformers lack recurrence (RNNs have it), positional encoding is added to the input sequence to retain order information.

12. What are the common issues in Deep Learning training?

Overfitting – Too complex model, memorizing training data.
Underfitting – Model is too simple, failing to learn patterns.
Exploding Gradients – Very high gradients causing instability.
Vanishing Gradients – Tiny gradients prevent deep layers from learning.

13. What is a Siamese Network?

A Siamese Network has two identical subnetworks that process different inputs but share weights.
Used for face recognition, signature verification, and similarity learning.

14. What is the F1-score? Why is it important?

F1-score balances precision and recall in classification tasks:

F1=2×(Precision×Recall)Precision+RecallF1 = \frac{2 \times (Precision \times Recall)}{Precision + Recall}

Useful when dealing with imbalanced datasets.

15. What are the different weight initialization techniques?

Xavier (Glorot) Initialization – Works well for Sigmoid and Tanh activations.
He Initialization – Best for ReLU activation.
Random Initialization – Can lead to exploding or vanishing gradients.

16. How does Attention Mechanism work?

Attention helps models focus on important parts of the input sequence.
Example: In NLP, attention allows a model to weigh different words differently based on context.

17. What is Beam Search in NLP?

Beam search is a heuristic search algorithm used in text generation (e.g., translation, summarization). It keeps track of multiple high-scoring sequences instead of just one.

18. What is Quantization in Deep Learning?

Quantization reduces model size by converting floating-point numbers to lower-bit representations (e.g., INT8 instead of FP32).
Used for deploying models on edge devices.

19. What are Contrastive Loss and Triplet Loss?

Used in metric learning for tasks like face recognition:

Contrastive Loss minimizes distance between similar pairs.
Triplet Loss ensures that an anchor is closer to a positive sample than a negative one.

20. What are Capsule Networks?

Capsule Networks (CapsNet) improve CNNs by preserving spatial hierarchies. They use dynamic routing instead of pooling.

Coding Questions with Solutions

21. Implement a CNN for Image Classification (MNIST)

import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense model = Sequential([ Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), MaxPooling2D((2,2)), Flatten(), Dense(128, activation='relu'), Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.summary()

22. Implement an RNN for Text Classification

import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Embedding, SimpleRNN, Dense model = Sequential([ Embedding(input_dim=5000, output_dim=64), SimpleRNN(128, activation='tanh'), Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.summary()

23. Implement a Transformer Block in TensorFlow

from tensorflow.keras.layers import Layer, Dense, MultiHeadAttention, LayerNormalization class TransformerBlock(Layer): def __init__(self, embed_dim, num_heads, ff_dim): super().__init__() self.att = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.ffn = Sequential([ Dense(ff_dim, activation="relu"), Dense(embed_dim) ]) self.norm1 = LayerNormalization() self.norm2 = LayerNormalization() def call(self, inputs): attn_output = self.att(inputs, inputs) out1 = self.norm1(inputs + attn_output) ffn_output = self.ffn(out1) return self.norm2(out1 + ffn_output)

24. Implement a Siamese Network in Keras

from tensorflow.keras.layers import Input, Dense, Lambda from tensorflow.keras.models import Model import tensorflow.keras.backend as K def euclidean_distance(vectors): x, y = vectors return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True)) input_shape = (128,) input_a = Input(shape=input_shape) input_b = Input(shape=input_shape) distance = Lambda(euclidean_distance)([input_a, input_b]) model = Model(inputs=[input_a, input_b], outputs=distance)

25. Implement Data Augmentation in TensorFlow

from tensorflow.keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator(rotation_range=30, horizontal_flip=True)

1. What are Transformer models, and how do they work?

Transformers use self-attention mechanisms instead of recurrence (RNNs) to process sequences in parallel.
Key components:

Self-attention – Assigns weights to words based on relevance.
Positional Encoding – Adds sequential information.
Feedforward Layers – Perform transformations.
Multi-head Attention – Captures diverse relationships.

Examples: BERT, GPT-4, ViT (Vision Transformer).

2. What is Self-Supervised Learning (SSL)?

Self-Supervised Learning trains models on automatically generated labels (e.g., predicting missing words).

Examples: BERT (masked language model), SimCLR (contrastive learning).

3. How does Contrastive Learning work?

Contrastive learning pulls similar samples closer while pushing dissimilar samples apart.

Used in self-supervised learning (SimCLR, MoCo, BYOL).

4. Explain Attention Heads in Multi-Head Attention.

Each attention head processes different parts of input and concatenates results.
Formula:

Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax} \left(\frac{QK^T}{\sqrt{d_k}}\right)V

5. How does Neural Architecture Search (NAS) work?

NAS automates deep learning model design using Reinforcement Learning or Evolutionary Algorithms.

Example: EfficientNet uses NAS for optimized CNN architectures.

6. Explain Diffusion Models in AI.

Diffusion models generate images by gradually denoising random noise.

Used in DALL-E 2, Stable Diffusion.

7. What is Zero-Shot and Few-Shot Learning?

Zero-Shot Learning: Model generalizes to unseen categories.
Few-Shot Learning: Model learns new categories with few examples.

Example: GPT-4 can perform tasks without direct training.

8. What is the Lottery Ticket Hypothesis?

A deep network contains subnetworks (winning tickets) that train efficiently when initialized correctly.

Used in pruning and model compression.

9. What are Diffusion Transformers (DiTs)?

A combination of transformers and diffusion models for generative AI.

Example: Stable Diffusion 2.0.

10. Explain Meta-Learning (Learning to Learn).

Meta-learning enables models to learn new tasks with minimal data.

Used in MAML (Model-Agnostic Meta Learning).

11. How does BERT differ from GPT?

FeatureBERTGPT

TypeEncoder-basedDecoder-based

TrainingBidirectionalAutoregressive

TaskNLP tasks (Q&A, classification)Text generation

12. What is the difference between Variational Autoencoders (VAEs) and GANs?

VAEs generate diverse, structured outputs but may blur details.
GANs generate realistic images but can be unstable.

13. Explain the concept of Sparse Neural Networks.

Sparse neural networks reduce unnecessary connections to improve efficiency.

Pruning – Removes unimportant weights.
Quantization – Reduces precision (FP32 → INT8).

14. What is Reinforcement Learning with Human Feedback (RLHF)?

RLHF fine-tunes models using human feedback to align AI with human preferences.

Used in ChatGPT, GPT-4.

15. What is a Memory-Augmented Neural Network (MANN)?

MANNs store and retrieve external information like a memory bank.

Used in Neural Turing Machines, Differentiable Neural Computers.

16. What is Perceiver IO?

Perceiver IO is a general deep learning model for multimodal data processing.

Unlike transformers, it scales efficiently to high-dimensional data.

17. What is Swin Transformer?

A Vision Transformer (ViT) that uses hierarchical feature extraction for images.

Used in object detection, segmentation.

18. What is CLIP (Contrastive Language-Image Pretraining)?

CLIP learns joint representations of images and text.

Used in DALL-E, OpenAI models.

19. How do GNNs (Graph Neural Networks) work?

GNNs process graph-structured data using message passing.

Used in social networks, molecular predictions, recommendation systems.

20. What is NeRF (Neural Radiance Fields)?

NeRF generates 3D scenes from 2D images using neural networks.

Used in virtual reality, gaming.

Advanced Coding Questions

21. Implement Vision Transformer (ViT) using TensorFlow.

import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.applications import EfficientNetB0 # Load Vision Transformer Model base_model = EfficientNetB0(weights='imagenet', include_top=False) # Build classifier model = tf.keras.Sequential([ base_model, Flatten(), Dense(128, activation='relu'), Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.summary()

22. Implement a Transformer Encoder Block in TensorFlow.

from tensorflow.keras.layers import MultiHeadAttention, LayerNormalization, Dense class TransformerEncoder(tf.keras.layers.Layer): def __init__(self, embed_dim, num_heads, ff_dim): super().__init__() self.att = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.ffn = tf.keras.Sequential([ Dense(ff_dim, activation="relu"), Dense(embed_dim) ]) self.norm1 = LayerNormalization() self.norm2 = LayerNormalization() def call(self, inputs): attn_output = self.att(inputs, inputs) out1 = self.norm1(inputs + attn_output) ffn_output = self.ffn(out1) return self.norm2(out1 + ffn_output)

23. Implement StyleGAN Latent Space Interpolation.

import numpy as np import tensorflow as tf # Generate random latent vectors z1 = np.random.randn(1, 512) z2 = np.random.randn(1, 512) # Linear interpolation alpha = np.linspace(0, 1, num=10) interpolated_latents = [(1 - a) * z1 + a * z2 for a in alpha]

24. Implement Neural Style Transfer using TensorFlow.

import tensorflow_hub as hub # Load pre-trained style transfer model hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') # Apply style transfer stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]

25. Implement a Simple Graph Neural Network (GNN).

import torch import torch.nn as nn import torch_geometric.nn as pyg_nn class GCN(nn.Module): def __init__(self, in_channels, out_channels): super().__init__() self.conv1 = pyg_nn.GCNConv(in_channels, 16) self.conv2 = pyg_nn.GCNConv(16, out_channels) def forward(self, x, edge_index): x = self.conv1(x, edge_index).relu() x = self.conv2(x, edge_index) return x

1. What is Backpropagation Through Time (BPTT) in RNNs?

Backpropagation Through Time (BPTT) is an extension of backpropagation used to train Recurrent Neural Networks (RNNs).

It unrolls the network over multiple time steps.
Gradients are computed for each step and accumulated.
Suffers from vanishing/exploding gradient problems.

2. What are Layer Normalization and Batch Normalization?

FeatureBatch Normalization (BN)Layer Normalization (LN)

Normalization AxisAcross the batch dimensionAcross features (channels)

Works well forCNNsRNNs, Transformers

DependencyDepends on batch sizeIndependent of batch size

3. What is Weight Sharing in CNNs?

Weight sharing ensures that the same filter (kernel) is applied across an image.

Reduces the number of parameters.
Enables CNNs to detect patterns across locations.

4. Explain Swarm Intelligence in Neural Networks.

Swarm Intelligence refers to collective problem-solving by agents (particles, ants).

Used in Neural Architecture Search (NAS).
Examples: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO).

5. What is the Computational Complexity of Self-Attention in Transformers?

Self-Attention has a quadratic complexity:

O(n2d)O(n^2 d)

where:

nn = sequence length
dd = embedding dimension

Solutions:

Linformer: Reduces to O(n d).
Longformer, Performer: Uses local attention for efficiency.

6. Why do Transformers use Layer Normalization instead of Batch Normalization?

BN depends on batch statistics, but transformers process sequences independently.
LN stabilizes training in non-i.i.d. inputs like NLP.

7. How does Model Pruning work in Deep Learning?

Pruning removes unimportant connections to reduce model size.

Magnitude Pruning: Remove small-weight neurons.
Structured Pruning: Removes entire filters/layers.
Unstructured Pruning: Removes individual weights.

8. What is Teacher Forcing in RNNs?

Teacher forcing uses the ground-truth previous output instead of the model’s output during training.

Helps faster convergence.
Causes exposure bias (model fails on unseen sequences).

9. What is Dynamic Computation Graph in PyTorch?

Unlike TensorFlow (static graph), PyTorch uses dynamic computation graphs, allowing on-the-fly modifications.

Example:

import torch x = torch.tensor(2.0, requires_grad=True) y = x**2 + 3*x + 1 y.backward() # Computes dy/dx print(x.grad)

10. How does Spectral Normalization prevent mode collapse in GANs?

Spectral Normalization controls the Lipschitz constant of the discriminator by normalizing weight matrices.

Formula:

WSN=Wσ(W)W_{\text{SN}} = \frac{W}{\sigma(W)}

where σ(W)\sigma(W) is the largest singular value.

Architectural Questions

11. How does EfficientNet achieve better accuracy with fewer parameters?

EfficientNet uses Compound Scaling:

α×width,β×depth,γ×resolution\alpha \times \text{width}, \beta \times \text{depth}, \gamma \times \text{resolution}

This maintains a balance between network depth, width, and resolution.

12. What are MLP-Mixer and ConvMixer?

MLP-Mixer: Uses only MLPs and token mixing instead of convolutions.
ConvMixer: Uses depthwise convolutions and MLPs, inspired by transformers.

13. Explain the role of Positional Encoding in Transformers.

Transformers lack sequential order, so positional encodings provide ordering information:

Formula:

PE(pos,2i)=sin⁡(pos/100002i/d)PE_{(pos, 2i)} = \sin(pos / 10000^{2i/d}) PE(pos,2i+1)=cos⁡(pos/100002i/d)PE_{(pos, 2i+1)} = \cos(pos / 10000^{2i/d})

14. What is Tokenization in BERT?

BERT uses WordPiece Tokenization, which breaks words into subwords:

"unhappiness" → ["un", "##happiness"].
Helps with out-of-vocabulary (OOV) handling.

15. How does a Reparameterization Trick work in Variational Autoencoders (VAEs)?

Instead of directly sampling zz from N(μ,σ)N(\mu, \sigma), we use:

z=μ+σ⋅ϵ,ϵ∼N(0,1)z = \mu + \sigma \cdot \epsilon, \quad \epsilon \sim N(0,1)

This makes the model differentiable.

16. What is Hyperparameter Tuning using Bayesian Optimization?

Bayesian Optimization models a probability function over hyperparameters and chooses values based on:

Expected Improvement (EI)=(μ−f∗)Φ(z)+σϕ(z)\text{Expected Improvement (EI)} = (\mu - f^*)\Phi(z) + \sigma\phi(z)

Mathematical & Coding Questions

17. Implement AdamW Optimizer in PyTorch.

import torch.optim as optim optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.01)

AdamW separates weight decay from momentum updates, improving generalization.

18. Implement a Custom Activation Function in TensorFlow.

import tensorflow as tf from tensorflow.keras.layers import Activation def swish(x): return x * tf.sigmoid(x) custom_activation = Activation(swish)

19. Implement Knowledge Distillation in PyTorch.

import torch.nn.functional as F def knowledge_distillation_loss(student_logits, teacher_logits, temperature=2.0): return F.kl_div( F.log_softmax(student_logits / temperature, dim=1), F.softmax(teacher_logits / temperature, dim=1), reduction='batchmean' )

20. Implement Label Smoothing in TensorFlow.

loss = tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1)

Prevents overconfidence in predictions.

21. Implement Data Augmentation for Image Processing.

from tensorflow.keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator(rotation_range=20, zoom_range=0.2, horizontal_flip=True)

22. Implement Gradient Clipping in PyTorch.

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

Prevents exploding gradients.

23. Compute Cosine Similarity in NumPy.

import numpy as np def cosine_similarity(a, b): return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) vec1 = np.array([1, 2, 3]) vec2 = np.array([4, 5, 6]) print(cosine_similarity(vec1, vec2))

24. Compute Cross-Entropy Loss Manually.

import numpy as np def cross_entropy_loss(y_true, y_pred): return -np.sum(y_true * np.log(y_pred)) y_true = np.array([0, 1, 0]) y_pred = np.array([0.1, 0.8, 0.1]) print(cross_entropy_loss(y_true, y_pred))

25. Implement an LSTM for Sequence Prediction.

import tensorflow as tf from tensorflow.keras.layers import LSTM, Dense model = tf.keras.Sequential([ LSTM(64, return_sequences=True, input_shape=(10, 100)), LSTM(32), Dense(1, activation='sigmoid') ])

"Deep Concepts to Elevate Your Career"

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Deep Learning Interview Questions and Answers

Top 100 Deep Learning Interview Questions for Freshers

"Deep Concepts to Elevate Your Career"

This guide provides 100+ Deep Learning interview questions along with in-depth concepts to strengthen your expertise.

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Software Courses

Full Stack Developer

Deep Learning Interview Questions and Answers

Top 100 Deep Learning Interview Questions for Freshers

"Deep Concepts to Elevate Your Career"

This guide provides 100+ Deep Learning interview questions along with in-depth concepts to strengthen your expertise.

Contact Us

IDM Techpark Erode 1st floor, 33/15 vasavi complex, nalli hospital road, annamalai layout, near bus stand, erode 638011

+91-95853-05700

idmtechpark@gmail.com

Software Courses

Full Stack Developer

IDM Techpark Erode
1st floor, 33/15 vasavi complex, nalli
hospital road, annamalai layout, near bus
stand, erode 638011