Janam's application project
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The document describes a pet recommender chatbot application that uses Python code and three AI models. The Python code asks the user questions about their preferences and sends the answers to the AI models. One AI model determines if the user wants a dog or cat. The other two AI models then provide breed recommendations based on the user's preferences. The developer trained the AI models using a Google spreadsheet containing possible values for each breed. Going forward, the developer wants to expand the application by adding more pets, breeds, and attributes like color.
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Objective APIs are very popular and necessary when you want.pdf
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Linear regression middleschool
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.
Knn intro advanced_middleschool
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.
M1 regression metrics_middleschool
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.
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 that are used for machine learning tasks involving image classification.
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.
Ai in real life face detection
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Cnn
This document discusses convolutional neural networks (CNNs) and how they process images without flattening the image into a single vector like multilayer perceptrons (MLPs) do. It provides examples of CNN weight matrices and explains that CNNs apply weights to nodes in local regions of the image rather than a single node. The document also notes that many types of CNNs exist that differ in their architecture, layers, connections, and other attributes.
Res net high level intro
This document provides a high-level introduction to Residual Neural Networks (ResNets). ResNets allow for very deep neural networks with many layers by introducing shortcut connections that skip layers, enabling the flow of gradients across many layers. This architecture has enabled the creation of neural networks with over 100 layers that were able to achieve state-of-the-art results on image recognition tasks. ResNets demonstrate that very deep neural networks can be effectively trained if they contain shortcuts for the gradients to flow across many layers.
Neural networks and flattened images
The document discusses how images are used as inputs for neural networks. It explains that images need to be flattened into individual numbers before being input into the first layer. This flattening process takes a multi-dimensional array and converts it into a one-dimensional vector. An example is provided of a 2D array being flattened into a single list of numbers that can then be input to the neural network.
What is a_neural_network
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.
How neural networks learn part iii
The document discusses key concepts in neural network training including weights, stochastic gradient descent, learning rate, mini-batch size, and epochs. Weights in a neural network are adjusted during training to minimize loss, using stochastic gradient descent. The learning rate determines how much weights are adjusted with each pass of data. Mini-batch size refers to the number of samples used to calculate weight updates in each iteration. Epochs refer to the number of times the full training data is passed through the network. The optimal values for these hyperparameters depend on factors like the dataset and model architecture.
Introduction to deep learning image classification
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.
Accuracy middleschool
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
- Common applications include image recognition, sentiment analysis, medical diagnosis
Introduction to the cloud
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.
Ai vs robotics_level 1
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.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Janam's application project
- 1. http://aiclub.worldhttp://aiclub.world Pet Recommender ChatBot By: Janam Chahal http://aiclub.world
- 2. http://aiclub.world Application My app is a pet recommender and it recommends a pet and the breed based on your preferences.
- 3. http://aiclub.world Architecture My python code does all of the asking questions and storing the answers. The AI studies the answers and will give you a result for which pet matches your preferences. I have 3 AIs included in this project, one for deciding between cats and dogs, and one for providing breed recommendations for each. Python Code The Python takes in all of the values, sends them to the AI, and will output the results. AI The AI will take in all of the users preferences and match it with a breed/pet. Then, it will send it back for the python to output.
- 4. http://aiclub.world Describe Your App? I have 193 lines of python code and my IDE is repl. Here is a little section of my code ------>
- 5. http://aiclub.world How did you train your AI? I made a google spreadsheet with the possible values for each breed. I had 3 AIs, one to decide between dog or cat, and then two others that predict breeds. Dog or Cat Dog Breeds Cat Breeds
- 6. http://aiclub.world What would you like to do next? I would like to add more pets, more breeds, and more choices for looks such as color.