Deep Learning Module 1

Deep Learning Module 1 Machine Learning (MVJ College of Engineering) Scan to open on Studocu Studocu is not sponsored or endorsed by any college or university Deep Learning Module 1 Machine Learning (MVJ College of Engineering) Scan to open on Studocu Studocu is not sponsored or endorsed by any college or university Downloaded by my stories ([email protected]) lOMoARcPSD|20801770.

DEEP LEARNING MODULE 1 Machine Learning Basics: Learning Algorithms, Capacity, Overfitting and Underfitting, Hyperparameters and Validation Sets, Estimator, Bias and Variance, Maximum Likelihood Estimation, Bayesian Statistics, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Stochastic Gradient Decent, building a Machine Learning Algorithm, Challenges Motivating Deep Learning. Machine learning: Machine learning is a subset of artificial intelligence (AI) that allows machines to learn and improve from experience without being explicitly programmed. Machine Learning Basics:  Definition and Example of Learning Algorithm:  A learning algorithm is introduced with an example: the linear regression algorithm.  Fitting vs. Generalization:  Distinguishes between fitting the training data and finding patterns that generalize to new data.  Hyperparameters:  Discussion on hyperparameters and the need to set them using additional data external to the learning algorithm itself.  Machine Learning as Applied Statistics:  Machine learning is likened to applied statistics with a focus on computational estimation of functions rather than proving confidence intervals.  Frequentist Estimators and Bayesian Inference: Downloaded by my stories ([email protected]) lOMoARcPSD|20801770.

 Introduction to the two central approaches to statistics: frequentist estimators and Bayesian inference.  Categories of Machine Learning Algorithms:  Supervised Learning: Learning with labeled data.  Unsupervised Learning: Learning with unlabeled data.  Optimization Algorithm: Stochastic Gradient Descent:  Many deep learning algorithms are based on an optimization algorithm called stochastic gradient descent.  Components of a Machine Learning Algorithm:  Description of how to combine components such as an optimization algorithm, a cost function, a model, and a dataset to build a machine learning algorithm.  Limitations of Traditional Machine Learning:  Discussion in section 5.11 on factors that limit the ability of traditional machine learning to generalize, motivating the development of deep learning algorithms to overcome these obstacles. Learning Algorithms: Definition of a Learning Algorithm: A machine learning algorithm is defined as an algorithm that learns from data. Mitchell's Definition: Mitchell (1997) defines learning as: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Components of Learning:  Experience (E): The data or experience from which the algorithm learns.  Tasks (T): The specific tasks the algorithm is designed to perform.  Performance Measure (P): The criteria used to measure the algorithm's performance on the tasks. 1 .The Task, T  Definition of Task (T):  In the context of machine learning, the task is not the learning process itself but the ability to perform a specific function (e.g., walking for a robot).  Example of Task:  If the goal is for a robot to walk, then walking is the task. Learning to walk is the means to achieve this task. Downloaded by my stories ([email protected]) lOMoARcPSD|20801770.

1.1Classification:  Definition of Classification Task:  The task involves specifying which of k categories an input belongs to.  Example 1: Object recognition:  An image is represented by a numeric code.  The goal is to recognize objects within the image.  Example 2: Robot task:  A robot should be able to identify different objects and then act on them based on commands.  Example 3: People recognition:  People in images should be automatically recognized and tagged.  1.2.classification with missing inputs: Normally, when you have all the information (temperature, blood pressure, heart rate, and blood test results), the model takes all of these inputs and makes a prediction—either "Disease Present" or "No Disease." Now, imagine that some patients don't get a blood test because it’s expensive. For others, maybe the heart rate is not available. This means your model won't always have a complete set of inputs for every patient. Solution: Instead of training the model for just one set of inputs (all available), the model needs to handle all combinations of missing inputs.  Missing inputs make the problem harder because the model needs to handle many scenarios (different inputs missing for different patients).  The model learns to classify (diagnose) even when some information is unavailable by focusing on what it does know. This approach is efficient because instead of building a separate model for every possible missing input scenario, the model learns a single function that can handle all cases by filling in the missing gaps using probabilities. 1.3. Regression Regression: Predicting a numerical value given input data. Eg:  Predicting Housing Prices  Predicting Stock Prices Downloaded by my stories ([email protected]) lOMoARcPSD|20801770.