Cláudio Toledo
@claudio-toledoHi there, it's Cláudio! Glad you are checking out my profile. I am a very passionate for people and technology, not necessarily in this order.
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Ryota Kawamura
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John WR
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Jared Wright
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Tom Jobbins
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AI Anytime
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18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
Lab Materials for MIT 6.S191: Introduction to Deep Learning
DO IT YOURSELF Case Study 1.2.1: PCA - Identifying Faces Instructor: Stefanie Jegelka Activity Type: Optional Case Study Description: Classifying and identifying human faces. Why this Case Study? Build your own implementation of an image classification algorithm that helps classify new photos of humans! This can help you understand how it is possible for Facebook to suggest, very accurately, who to tag in a given photo with people's photos. Self-Help Documentation: In this document, we walk through some helpful tips to get you started with building your own application for classifying faces in photo images using Principle Component Analysis (PCA). In this tutorial, we provide examples and some pseudo-code for the following programming environment: Matlab. Download Self-Help Documentation Download Pictures DataSet Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience.
Do It Yourself Case Study 1.1.1: Genetic Codes Instructor: Tamara Broderick Activity Type: Optional Case Study Description: Using K-means to help figure out that DNA is composed of 3-letter words. Self-Help Documentation: From this document, you will learn how data visualization can help in genomic sequence analysis and start with a fragment of genetic text of a bacterial genome and analyze its structure. Download Self-Help Documentation Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience. Have questions? Feel free to discuss the case study with other participants in the Discussion Forum under Module 1 - Case Studies Section.
Case Study 1.3.2: Spectral Clustering - Grouping News Stories Instructor: Stefanie Jegelka Activity Type: Optional Case Study Description: Auto-clustering News stories. Why this Case Study? Build your own clustering for news stories on the web similar to how you see Google News organize news stories by auto-generated topics/groupings! Self-Help Documentation: In this document, we walk through some helpful tips to get you started with building your own application for automating the clustering of news stories using Spectral Clustering. In this tutorial, we provide examples and some pseudo-code for the following programming environment: Python. Download Self-Help Documentation Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience. Have questions? Feel free to discuss the case study with other participants in the Discussion Forum under Module 2 - Case Studies Section.
Hands on tutorials demonstrating the concepts of Prediction engineering, Feature engineering and automation in data science.
Some Python Implementations of the Kalman Filter
Case Study 1.1.2: Finding Themes in Project Descriptions Instructor: Tamara Broderick Activity Type: Optional Case Study Description: Using Latent Dirichlet Allocation to discover topics in a corpus of text. Finding Themes in Project Descriptions - LDA Analysis. Self-Help Documentation: In this document, we walk through some tips to help you with doing your own analysis on MIT EECS faculty data using stochastic variational inference on LDA. We provide some examples for the following programming environment: Python. Download Self-Help Documentation Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience. Have questions? Feel free to discuss this case study with other participants in the Discussion Forum under Module 1 - Case Studies Section.
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code
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