How AIs are different from us level 1
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The document discusses linear regression, a type of supervised machine learning algorithm. It can be used to predict a numeric label given input features. The document provides examples of using linear regression to model relationships between features like square footage and home price. It also explains that calculating an average is a simple form of linear regression, with weights of 1/n for each input value.
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The document provides guidance for students to describe an AI application they created. It prompts them to provide information such as the name of the app and student, an overview of the app's architecture and how the AI interacts with it, descriptions and plots of their labeled data distribution and important features, how they trained their AI including metrics and analysis of different feature engineering steps, plans for improving the app, and a demo.
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Mnist images
The document introduces the MNIST dataset which contains images of handwritten digits with a black background at a very low resolution of 28x28 pixels. The MNIST dataset contains 10 categories of numbers from 0 to 9 which are used to train machine learning models to recognize handwritten digits.
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The document provides guidance for students to describe an AI application they created. It prompts them to provide information such as the name of the app and student, an overview of the app's architecture and how the AI interacts with it, descriptions and plots of their labeled data distribution and important features, how they trained their AI including metrics and analysis of different feature engineering steps, plans for improving the app, and a demo.
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This document discusses the K-Nearest Neighbors (KNN) classification algorithm. It provides an example dataset classifying individuals as "adult" or "child" based on their years in school and height. It then demonstrates how KNN would classify a new data point based on different values of K. The document also discusses a regression dataset to predict house prices based on square footage and age. It explains how to evaluate different values of K through hyperparameter tuning and applies KNN to predict the price of a new house. In the end, it notes a drawback of KNN related to scaling of different features.
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This document discusses metrics for evaluating predictive performance in regression models. It explains that accuracy is used for classification models, while regression models are evaluated based on the errors between predicted versus true values. Specifically, mean absolute error, mean squared error, and root mean square error are introduced as common metrics, where errors are calculated as the absolute or squared differences between predictions and true values, and then averaged. Examples are provided to demonstrate calculating these error metrics.
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Mnist images
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The document discusses how neural networks learn through training. It explains that neural networks have many weights that are learned during training using a method called stochastic gradient descent. The learning rate determines how much the weights are adjusted with each pass of data. The mini-batch size refers to the number of samples used to update the weights. The number of epochs is the number of times the entire dataset is used during training to iteratively update the weights.
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Neural networks and deep learning use networks of neurons that can learn from large amounts of data. A basic neuron receives multiple inputs which it multiplies by weights and combines to produce an output. Neural networks contain many interconnected neurons arranged in layers that can learn increasingly complex patterns from data. Deep learning uses neural networks with many hidden layers to perform tasks like image recognition by learning from large datasets.
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The document provides an introduction to image classification and deep learning. It defines AI, machine learning, and deep learning, with deep learning using neural networks to be well-suited for tasks like image, sound, and video recognition. Image classification is described as using deep learning to categorize images into categories like cats versus dogs, and can involve more than two categories. Performance of deep learning image classification models is measured using classification accuracy metrics.
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The document discusses how to measure the accuracy of a machine learning model. It explains that a validation or test dataset should be created from a portion of the training data and held aside for testing. The model makes predictions on the validation data and its predictions are compared to the actual labels to calculate accuracy. Accuracy is determined by taking the number of correct predictions divided by the total number of predictions. A higher accuracy percentage indicates the model is better at making correct predictions. The example shown calculates an accuracy of 75% for a model that made 3 correct predictions out of 4 total in the validation dataset.
Introduction to classification_middleschool
Classification is a type of supervised machine learning that predicts categorical output variables. It involves using labeled examples to train a model to classify new examples. Some key points:
- Classification predicts categories like good/bad, cat/dog rather than continuous values
- It requires labeled training data that maps features to categories
- Features can be any data type like numbers, text, categories
- Models are trained to learn patterns in features to classify new unlabeled examples
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The document provides an introduction to cloud computing, data centers, and neural networks and their relationship to artificial intelligence. It defines the cloud as computers, storage, and apps accessed over the internet, and data centers as large facilities housing hundreds of thousands of computers that power the cloud. Neural networks are also introduced as relevant to AI, with the key point being that artificial intelligence applications often run in the cloud using powerful hardware like GPUs in data centers.
Basics of data
The document discusses the basics of data and AI. It explains that data contains rows and columns, with each row representing an example for the AI to train on. Features describe the examples, while labels categorize them. It provides an example of using text data describing feelings as features to train a model to predict if a feeling is happy or sad. The document also defines an AI service as a program that takes new questions or data as input and uses a pre-trained model to generate predictions or answers that can be used by an application.
Ai basics
The document discusses artificial intelligence and machine learning. It defines AI as how computers mimic human brains, and provides examples like Google Translate, Alexa, and Siri. Machine learning is described as a subset of AI where machines learn from data, and can be supervised by providing examples, unsupervised with no information given, or reinforcement using a reward system. Deep learning is presented as a sophisticated version of machine learning.
Ai lifecycle and navigator
The document discusses the AI workflow and different software options that can be used. The typical AI workflow involves identifying a problem, preparing data, developing and training models, testing models, deploying models, connecting to applications, and monitoring and optimizing models in an iterative process. It then shows diagrams of different software flows using Amazon SageMaker and Scikit-Learn Serverless for AWS, Google Cloud AI Platform for Google Cloud, and using AWS services like S3, SageMaker, and Lambda. It promotes the company's own AI Navigator software for building, deploying, and managing AI applications on AWS at scale.
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This document discusses the differences between artificial intelligence (AI) and robotics. It provides examples of tasks that involve robotics like moving arms and legs to complete physical tasks, while AI involves capabilities like talking to humans, seeing, figuring out patterns, and tasks like auto-correct and digital assistants. The document suggests that combining AI and robotics can create smart robots that can complete both physical and cognitive tasks.
Ai in real_life_dynamic_pricing
AI has enhanced dynamic pricing by allowing prices to change more frequently and be influenced by more factors. While dynamic pricing has existed for a long time, AI has made it possible for prices to update every minute based on news, other purchases, demand, and more. This shows how AI has accelerated an existing practice of adjusting prices rather than creating new uses cases outright. Companies can now leverage vast amounts of customer and market data via AI to continuously set optimal prices.
Object detection
This document discusses object detection and its applications. Object detection involves identifying objects in images and determining what they are and if they are moving. This is important for applications like self-driving cars to detect other cars, pedestrians, and traffic signs. Object detection can also be used to find people in crowds and analyze emotions for applications in sports.
Ai daily life_text_facialrecognition_elementary
Facial recognition AI can identify people in photos and videos, including recognizing emotions. This technology is being developed for uses like finding people in crowds, but it also raises privacy concerns about being tracked in public spaces or identified by shops without consent. More consideration is needed regarding appropriate uses and regulations to address potential privacy issues.
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The document discusses how AI is used in autocorrect and email assistance applications to help complete words and sentences based on patterns and predictions. It can make mistakes by replacing unique words or names with common words. The AI also tries to understand the tone and context of messages to suggest replies by analyzing how other people have written similar sentences. The document encourages the reader to test out autocorrect and email assistance features themselves.
Level 1 ai intro elementary
AI is a field of computer science that aims to build machines that can think and act like humans by learning to make decisions, predict the future, recognize patterns, play games and more. Some examples of AI in real life include using it to detect anomalies, classify types of data, recognize images and sounds, decide strategies for games and more. AI has the potential to be used in many areas of our lives.
How neural networks learn ii
The document discusses how neural networks learn through training. It explains that neural networks have many weights that are learned during training using a method called stochastic gradient descent. The learning rate determines how much the weights are adjusted with each pass of data. The mini-batch size refers to the number of samples used to update the weights. The number of epochs is the number of times the entire dataset is used during training to iteratively update the weights.
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