Machine learning - session 7
- 1. Machine Learning Lunch & Learn - Session 7 Luis Borbon 20/07/2017
- 2. Table of contents 1. Recap 2. AI and Industry 3. Artificial Neural Networks 4. Real Application 5. Video
- 3. Recap
- 4. KNN (K- Nearest Neighbors) It can be used for both classification and regression problems. However, it is more widely used in classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors. The case being assigned to the class is most common amongst its K nearest neighbors measured by a distance function.
- 5. K-Means It is a type of unsupervised algorithm which solves the clustering problem. Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). Data points inside a cluster are homogeneous and heterogeneous to peer groups.
- 7. AI maturity levels by sector
- 14. Artificial Neural Networks ANN
- 15. Gradient descent Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
- 16. Gradient descent
- 17. Gradient descent Gradient descent is a popular method in the field of machine learning because part of the process of machine learning is to find the highest accuracy, or to minimize the error rate, given a set of training data. Gradient descent is used to find the minimum error by minimizing a "cost" function.
- 18. Gradient descent
- 19. Perceptron Algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not). It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
- 20. Neuron An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network.
- 21. Neuron The artificial neuron receives one or more inputs (representing dendrites) and sums them to produce an output (or activation) (representing a neuron's axon). Usually the sums of each node are weighted, and the sum is passed through a non-linear function known as an activation function.
- 22. Activation Function Activation function of a node defines the output of that node given an input or set of inputs.
- 25. Back Propagation
- 26. Real Application
- 27. FunCaptcha FunCaptcha stops automated abuse and retains high human conversion. We stop billions of attacks for global brands that avoid the flaws common in every other CAPTCHA solution on the market.
- 28. How Computers Understand Pictures
- 29. Video Check out this video https://youtu.be/40riCqvRoMs
Editor's Notes
- #5: https://elitedatascience.com/machine-learning-algorithms