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Questions and Answers
What are machine learning models used for?
What are machine learning models used for?
To compute a function that maps inputs to corresponding outputs.
What are Artificial Neural Networks inspired by?
What are Artificial Neural Networks inspired by?
The central nervous system of mammals.
What is a Perceptron?
What is a Perceptron?
A two-layer network used for simple operations.
Deep learning refers to neural networks with fewer layers.
Deep learning refers to neural networks with fewer layers.
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What is the main purpose of activation functions in neural networks?
What is the main purpose of activation functions in neural networks?
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Which activation function outputs values between -1 and +1?
Which activation function outputs values between -1 and +1?
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What problem does ReLU activation function aim to address?
What problem does ReLU activation function aim to address?
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The vanishing gradient problem causes the early layers of a neural network to learn quickly.
The vanishing gradient problem causes the early layers of a neural network to learn quickly.
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What is overfitting in machine learning?
What is overfitting in machine learning?
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Which technique can be used to prevent overfitting?
Which technique can be used to prevent overfitting?
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What does L1 Regularization express?
What does L1 Regularization express?
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What is the purpose of hyperparameter tuning?
What is the purpose of hyperparameter tuning?
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What is Backpropagation?
What is Backpropagation?
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Study Notes
Machine Learning Models
- Mathematical functions that map inputs to outputs, combining operations like addition and multiplication with non-linear activation functions.
- Layers of neurons help learn complex relationships among data.
Artificial Neural Networks (ANNs)
- Inspired by the central nervous systems of mammals, consisting of interconnected neurons organized in layers.
- Neurons in one layer pass messages to the next layer, learning expected outputs from training datasets.
Perceptron
- A simple two-layer network designed for basic operations, outputting binary results (1 or 0).
Back Propagation Algorithm
- A method for efficiently training multi-layer networks by adjusting weights based on error feedback.
Deep Learning
- Characterized by numerous layers and a high number of neurons, capable of learning sophisticated models.
- This approach draws inspiration from the human visual system, allowing for progressive levels of abstraction in learning.
Multilayer Perceptron (MLP)
- An extension of the perceptron with multiple layers, where input and output layers are exposed, while hidden layers remain internal.
- Each layer typically uses linear functions.
Activation Functions
- Essential for enabling smooth learning transitions; they provide continuity and allow for derivative computation.
- Activation functions like Sigmoid, Tanh, and ReLU facilitate the neural network's ability to learn from data without abrupt changes.
Sigmoid Activation Function
- Outputs values between 0 and 1, used in binary classification tasks.
- Maps any real-valued number to a value between 0 and 1, providing probabilities for decision-making.
Tanh Activation Function
- Outputs values ranging from -1 to +1, suitable for data requiring normalization and aiding in model performance.
- Useful in scenarios like predicting stock price changes.
ReLU Activation Function
- Defined as f(x) = max(0, x), ReLU is linear for positive values and zero for negatives, promoting efficiency in deep learning.
- Typically used in image classification, object detection, and regression tasks due to its ability to handle sparsity effectively.
Issues in Deep Neural Networks
- Vanishing Gradient Problem: Gradients become very small, hindering learning in early layers, affecting optimization.
- Dying ReLU Problem: Neurons with ReLU can become inactive, leading to zero outputs across various inputs.
Overfitting
- Occurs when a model becomes too complex, losing its ability to generalize across new data.
- Common causes include excessive parameters, insufficient training data, and overly complex models.
Identifying Overfitting
- Monitor training and validation losses; an increase in validation loss after initial training indicates potential overfitting.
Preventing Overfitting
- Simplifying the model by reducing complexity, selecting fewer features, and decreasing model parameters.
- Applying cross-validation, regularization techniques, and pruning methods for decision trees.
- Monitoring performance and potentially stopping training early, alongside increasing training datasets.
Regularization Techniques
- L1 Regularization (Losso): Enforces sparsity by summing the absolute values of weights.
- L2 Regularization (Ridge): Penalty based on the sum of the squares of the weights, helping to reduce overfitting.
- Elastic Regularization: Combines L1 and L2 penalties to manage model complexity effectively.
Batch Normalization
- Accelerates training by adjusting weights to stabilize layer inputs in their distribution across batches and epochs, enhancing training efficiency.
Hyperparameter Tuning and Auto ML
- Hyperparameters: Adjustable parameters like the number of hidden neurons, batch size, epochs, and learning rate that influence model architecture.
- Auto ML: Techniques aimed at automating hyperparameter tuning and optimizing network architecture for improved performance.
Back Propagation
- A learning process for multilayer perceptrons, leveraging error propagation to adjust weights iteratively, improving model predictions based on input data.
- Involves initial random weight assignments, forward propagation of activations, comparison with true outputs, and backward propagation to minimize prediction error.
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Description
This quiz covers the basics of Neural Networks, focusing on how artificial neural networks (ANNs) map inputs to outputs using mathematical functions. Discover the architecture of these networks, including layers and non-linear activation functions, and their role in complex learning tasks.
