22 Questions
What is the goal of generative models in unsupervised learning?
To model the underlying probability distribution of the data to generate new samples that look like the training data.
Explain the role of the Generator in GANs.
The Generator is a neural network that takes random noise as input and transforms it into data samples. It learns to generate synthetic data that is indistinguishable from real data.
Describe the training process of the Discriminator in GANs.
The Discriminator is trained on a batch of real data and a batch of fake data. It learns to distinguish between the two by updating its weights based on how well it classifies real and fake samples.
What is the purpose of the adversarial game in GANs?
The adversarial game involves an iterative training process between the generator and discriminator in a feedback loop. The generator aims to generate realistic data, while the discriminator becomes more skilled at distinguishing real from fake data.
What is the main characteristic of Deep Convolutional GAN (DCGAN)?
DCGAN introduced convolutional layers to GANs, making it effective for image generation tasks.
How does Conditional GAN (cGAN) differ from traditional GANs?
cGAN receives additional information to guide the generation process, unlike traditional GANs.
Explain the concept of unsupervised learning.
Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data to find hidden patterns, relationships, or structures without predefined targets.
What is the main goal of unsupervised learning?
The main goal of unsupervised learning is to find hidden patterns, relationships, or structures within data without any predefined target or outcome.
Explain the architecture of a GAN and highlight the roles of the generator and discriminator.
A GAN consists of two neural networks – the generator, which generates new data samples, and the discriminator, which evaluates the generated samples.
Summarize the training process of GANs.
The training process of GANs involves a competitive game between the generator and discriminator where the generator tries to fool the discriminator by generating realistic samples.
What are the challenges associated with training GANs?
Challenges in training GANs include mode collapse, vanishing gradients, and instability during training.
What are some common unsupervised learning techniques?
Common unsupervised learning techniques include clustering and dimensionality reduction.
What is the key concept of Transductive Transfer Learning?
Transferring knowledge from a source task to a target task when the data distribution in the source and target tasks is the same, but the tasks have different objectives.
Explain Unsupervised Transfer Learning.
Transferring knowledge from a source domain with labeled data to a target domain with unlabeled data.
What are the steps involved in Feature Extraction Transfer Learning Approaches?
Remove task-specific layers, freeze pre-trained layers, add new layers, and train.
How does Fine-tuning in Transfer Learning work?
Training the entire pre-trained model on the target task with a smaller learning rate, including changing the weights of pre-trained layers.
What is the purpose of Adding New Task-Specific Layers in Transfer Learning?
To add new layers on top of pre-trained layers that are specific to the target task.
Describe the process of Freezing and Unfreezing Layers in Transfer Learning.
Freezing certain layers prevents them from being updated, preserving generic knowledge, while unfreezing allows them to be trained for specific tasks.
What is the purpose of Dropout and Regularization in Transfer Learning?
To apply dropout and regularization techniques during fine-tuning to improve model performance and prevent overfitting.
What are the popular ImageNet pre-trained models used in Transfer Learning?
VGG, ResNet, GoogLeNet
What is BERT commonly used for in NLP tasks?
Text classification, named entity recognition, or question answering.
What is the purpose of GPT in sequence prediction?
To predict the next word in a sequence of words based on the context of preceding words.
Learn about identifying relationships among variables with Association Rule Learning, such as the Apriori algorithm, and creating new data samples similar to a given dataset with Generative Models like GPT and Generative Adversarial Networks (GANs). Explore unsupervised learning techniques and how these models aim to generate realistic outputs.
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