The vanishing gradient problem occurs in deep neural networks when gradients become too small for weights to be updated effectively, causing the model to stop learning.
Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal.
A random forest is an ensemble learning method that uses multiple decision trees to make predictions and reduces overfitting compared to a single decision tree.
Autonomous vehicles, healthcare (diagnostics, drug discovery), recommendation systems, fraud detection, natural language processing, and robotics.
Feature engineering involves selecting, modifying, or creating new features from raw data to improve the performance of machine learning models.
L1 regularization adds the absolute value of the coefficients to the loss function, leading to sparse models. L2 regularization adds the square of the coefficients, penalizing large weights and preventing overfitting.
Random forest is a specific type of ensemble learning technique that uses multiple decision trees (bagging) but adds an extra layer of randomness when splitting nodes, making it more robust than a single decision tree.
A decision tree is a flowchart-like tree structure used for classification and regression, where each node represents a decision based on a feature, and each branch represents an outcome.
A CNN is a deep learning algorithm typically used for image and video recognition. It uses convolutional layers to detect patterns in the data.
SVM is a supervised machine learning algorithm used for classification tasks. It finds the hyperplane that best separates the classes in the data.
k-NN is a simple, non-parametric algorithm used for classification and regression, where the prediction is based on the majority class (or average) of the k nearest neighbors.
RNN is a type of neural network designed for sequential data, where the output from the previous step is fed as input to the next step. It is commonly used in time series analysis and natural language processing.
AI is the broader concept of machines performing tasks that typically require human intelligence. ML is a subset of AI that focuses on algorithms that learn from data. Deep Learning is a subset of ML that uses neural networks with many layers.
In supervised learning, the algorithm is trained on labeled data. In unsupervised learning, the data has no labels, and the algorithm tries to find patterns on its own.
A neural network is a series of algorithms designed to recognize underlying relationships in a set of data through a process that mimics how the human brain operates.
Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Underfitting occurs when a model is too simple to capture the underlying pattern of the data.
Cross-validation is a technique for assessing the performance of a machine learning model by training it on different subsets of the data and validating it on the remaining parts to reduce overfitting.
Gradient descent is an optimization algorithm used to minimize the cost function in machine learning by iteratively adjusting model parameters in the direction of the negative gradient of the function.
Hyperparameters are parameters set before training a model. Hyperparameter tuning involves using techniques like grid search, random search, or Bayesian optimization to find the optimal values for these parameters.
The bias-variance trade-off is a fundamental concept in machine learning where increasing the complexity of a model (low bias) can lead to high variance, and simpler models (low variance) can have high bias.
A confusion matrix is a table used to evaluate the performance of a classification algorithm by comparing the predicted and actual values.
Sigmoid, Tanh, ReLU, Softmax, and Leaky ReLU.
Classification involves predicting a category or class, while regression involves predicting a continuous value.
The learning rate determines the size of the steps the model takes when optimizing the loss function. If it's too large, the model may converge too quickly to a suboptimal solution. If it's too small, the model may take too long to converge.
The curse of dimensionality refers to the difficulty of analyzing and organizing data as the number of dimensions (features) increases.
PCA is a dimensionality reduction technique that transforms the data into a new coordinate system by identifying the directions (principal components) that maximize the variance.
Bagging (Bootstrap Aggregating) involves training multiple models independently and averaging their predictions. Boosting involves training models sequentially, where each model corrects the errors of the previous one.
Batch gradient descent computes the gradient of the cost function using the entire dataset, while stochastic gradient descent updates the model weights using one data point at a time.