What is a neural network in the context of machine learning? A single-layer perceptron used for linear classification A decision tree used for classification tasks A clustering algorithm for unsupervised learning A model made of layers of connected nodes that learn patterns in data

A model made of layers of connected nodes that learn patterns in data

What are the three basic types of layers in a neural network? Convolutional Layer, Pooling Layer, Fully Connected Layer Recurrent Layer, LSTM Layer, GRU Layer Dense Layer, Dropout Layer, Batch Normalization Layer Input Layer, Hidden Layer, Output Layer

Input Layer, Hidden Layer, Output Layer

Which specialized types of layers are commonly used in neural networks? Input Layer, Output Layer, Hidden Layer Recurrent Layer, LSTM Layer, GRU Layer Convolutional Layer, Pooling Layer, Attention Layer Linear Layer, Non-linear Layer, Activation Layer

Convolutional Layer, Pooling Layer, Attention Layer

What is supervised learning in machine learning? A model reduces dimensionality of data A model learns from examples with input data and correct answers A model clusters data into groups A model learns without any labeled data

A model learns from examples with input data and correct answers

What does a classification problem in machine learning involve? Reducing data dimensionality Generating new data samples Predicting a target variable's class or label Clustering data into groups

Predicting a target variable's class or label

What is a model in the context of machine learning? A method for reducing data dimensionality A technique for clustering data A mathematical object that assigns labels to data A dataset used for training

A mathematical object that assigns labels to data

What is classification and how does it relate to machine learning? Deciding between categories using trained models Generating new data samples Reducing data dimensionality Clustering data into groups

Deciding between categories using trained models

How does a linear classifier work for multiclass classification? It clusters data into groups It reduces data dimensionality It assigns the class with the highest score It uses a decision tree to classify

It assigns the class with the highest score

Comprehensive Guide to Machine Learning and Neural Networks Concepts

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Cards in this deck(100)

What is the field of study that gives computers the ability to learn without being explicitly programmed?

What is a neural network in the context of machine learning?

What are the three basic types of layers in a neural network?

Which specialized types of layers are commonly used in neural networks?

What is supervised learning in machine learning?

What does a classification problem in machine learning involve?

What is a model in the context of machine learning?

What is classification and how does it relate to machine learning?

What is the formula for a linear classifier in binary classification?

How does a linear classifier work for multiclass classification?

What are weights in the context of neural networks?

How do weights relate to loss in a neural network?

What is loss in the context of machine learning?

What is a hyperparameter in machine learning?

What is the difference between classification and regression?

What is Gradient Descent in machine learning?

What are the three different kinds of loss functions?

What is Cross Entropy Loss and its formula?

What is Hinge Loss and its behavior?

What is Squared Hinge Loss and its behavior?

What is the root of supervised learning?

What are the pros and cons of linear classifiers?

What are the limitations of a linear classifier?

How do neurons function mathematically in a neural network?

What are the neuron activation functions covered in lectures?

What challenges do computer vision neural networks face?

What are Convolutional Neural Networks (CNNs) and their purpose?

What are the different objects of computer vision tasks?

What is the intuition behind a convolutional layer?

What happens in a convolutional layer during forward propagation?

What is a feature map in the context of convolutional layers?

What is zero padding and why is it used?

What is a local receptive field in a convolutional layer?

What does stride length mean in convolution operations?

What is a Step Function in neural networks?

What is ReLU in the context of neural networks?

What is the sigmoid activation function?

What are the differences between Step Function, ReLU, and Sigmoid?

What is a dilated convolution in neural networks?

What are the differences between dilated and normal convolution?

What happens with each convolutional layer in a neural network?

What is a pooling layer and its benefits in neural networks?

What are the differences between Max Pooling and Average Pooling?

What is a Fully Connected Layer in a neural network?

What happens if the batch size is too big or too small?

What does dropout rate mean and why is it used?

What does it mean when a dropout rate is too high or too low?

Describe the training process for a Convolutional Neural Network (CNN).

What is the backpropagation algorithm in neural networks?

When do we stop training a neural network?

How do you build a neural network?

What are parameters in a neural network?

What is transfer learning in machine learning?

What are Generative Adversarial Networks (GANs)?

How do we compress and reconstruct images using neural networks?

What are autoencoders and why are they useful?

What is a Deconvolutional Layer in neural networks?

What is an Unpooling Layer in neural networks?

Explain the process of encoding and decoding in autoencoders.

What is the correlation between Game Theory and GANs?

What happens if the discriminator gets much better than the generator in a GAN?

What is the vanishing gradient problem in a GAN?

How can we combat the vanishing gradient problem in a GAN?

Why would we want to force convergence in a GAN and how can we do that?

When would non-convergence occur in a GAN and why?

What are Latent Space Vectors in neural networks?

What is Mode Collapse in GANs and why is it a problem?

What are Conditional GANs and their purpose?

What are the differences between Conditional GANs and Traditional GANs?

What model may we use to support text to image generation and why?

How does face aging work using deep learning?

How do we pass text to neural networks?

What are word embeddings and their purpose?

True or False: Word embeddings preserve semantic similarity of words.

How could we check the similarity between two different word embeddings?

What are some popular word embedding models?

When should you use a generator in relation to word and input?

What is Natural Language Processing (NLP) and how does it work?

What are the differences between Latent Space Vectors and Word Embeddings?

What are some problems in Natural Language Processing (NLP)?

What are some affective dimensions a word has for word embeddings?

What is Word2Vec and how does it work?

What is Skip-Gram in the context of Word2Vec?

What is Continuous Bag of Words (CBOW) in Word2Vec?

What is SoftMax and its purpose in neural networks?

How can we get the context of words while capturing different kinds of words?

How do we calculate the loss/probability of the output word given the context?

Explain the necessary layers of a neural network to predict words.

How do we train a word embedding model?

How do we get the weights for word embedding?

How can word embeddings be used to find analogies?

What is the relationship between word embeddings and GANs?

What is GloVe and its purpose in word embeddings?

What is a Large Language Model (LLM) and how does it work?

What does a Large Language Model (LLM) learn during training?

What is Model Distillation in machine learning?

What is Model Distillation used for?

What measurements do we use to evaluate intelligence in LLMs?

What is perplexity in the context of language models?

How can we calculate perplexity in language models?

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Comprehensive Guide to Machine Learning and Neural Networks Concepts

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