# data[].attributes.attachment_link.title
Papers with Code - The latest in Machine Learning

# data[].attributes.attachment_link.description
Papers With Code highlights trending Machine Learning research and the code to implement it.

# data[].attributes.body
Datasets para treinamento

# data[].attributes.attachment_link.description
This pathway is a series of courses and resources focused on protecting computerized systems and prepares you for a career in cybersecurity.

# data[].attributes.attachment_link.readable_text
Dive into the world of computer vision as you take the journey of hands-on learning with PyTorch. Gain practical expertise through project-based modules that give you the skills to tackle real-world challenges and build an amazing portfolio. With PyTorch as your guiding light, you'll unleash the power of cutting-edge vision algorithms, honing your skills in object detection, image classification, and more. Elevate your professional trajectory, impressing employers and clients alike with your ability to deliver tangible results. Don't just learn computer vision - experience it firsthand and set yourself apart in the competitive field of technology.

# data[].attributes.attachment_link.description
Dive into the world of computer vision as you take the journey of hands-on learning with PyTorch. Gain practical expertise through project-based modules that give you the skills to tackle real-world challenges and build an amazing portfolio. With PyTorch as your guiding light, you'll unleash the power of cutting-edge vision algorithms, honing your skills in object detection, image classification, and more.

# data[].attributes.attachment_link.title
Aplicativo Python para monitorar o preço do Bitcoin em tempo real_Projetos Python intermediários

# data[].attributes.attachment_link.description
Neste tutorial, você aprenderá como construir um aplicativo Bitcoin Price Tracker usando Python. Espero que isso seja realmente interessante e divertido para vo

# data[].attributes.attachment_link.title
O que faz um Engenheiro de Aprendizagem de Máquina?

# data[].attributes.attachment_link.description
Se você quer trabalhar com Machine Learning, confira um pouco mais sobre as atribuições dessa profissão, como a necessidade da expertise em dados.

# data[].attributes.attachment_link.readable_text
Criado em 1950, o ITA é o instituto de ensino superior do Comando da Aeronáutica (COMAER), localizado no Departamento de Ciência e Tecnologia Aeroespacial (DCTA). Mantém cursos de graduação em engenharia (Aeronáutica, Civil-Aeronáutica, Eletrônica, Mecânica-Aeronáutica, Engenharia de Computação, Aeroespacial), especialização, extensão e pós-graduação stricto sensu (Mestrado, Mestrado Profissionalizante e Doutorado).

# data[].attributes.attachment_link.title
Instituto Tecnológico de Aeronáutica Online Courses | Coursera

# data[].attributes.attachment_link.description
Criado em 1950, o ITA é o instituto de ensino superior do Comando da Aeronáutica (COMAER), localizado no Departamento de Ciência e Tecnologia Aeroespacial (DCTA). Mantém cursos de graduação em engenharia (Aeronáutica, Civil-Aeronáutica, ...

# data[].attributes.attachment_link.title
Python and Statistics for Financial Analysis

# data[].attributes.attachment_link.description
Offered by The Hong Kong University of Science and Technology. Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the ... Enroll for free.

# data[].attributes.attachment_link.readable_text
Here's a 9-hour crash course curriculum for learning to build LLM applications. All resources are free. 𝗣𝗔𝗥𝗧 𝟭 - 𝗔 𝗚𝗘𝗡𝗧𝗟𝗘 𝗜𝗡𝗧𝗥𝗢 𝗧𝗢 𝗟𝗟𝗠𝗦 (1h 30m) Intro & overview of language models, next-word prediction, embeddings, cosine similarity, semantic search Resources: 📺 30m: Simple intro (MBerm) https://lnkd.in/eCfYMdUK 📖 40m: llms nohype (MRied) https://lnkd.in/eY6Pd9bJ 📺 10m: Next wrd prd (LBouc) https://lnkd.in/ergd9FT2 📺 10m: Sem search (LSerr) https://lnkd.in/eRnnhmuj --- 𝗣𝗔𝗥𝗧 𝟮 - 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗘𝗥 𝗔𝗥𝗖𝗛𝗜𝗧𝗘𝗖𝗧𝗨𝗥𝗘 (1h 30m) Encoder-decoder architecture, masking, attention, transformers, GPTs Resources: 📺 30m: Visual llm (3Blu1Br) https://lnkd.in/egJPUhuX 📺 20m: Enc-Dec (JStarm) https://lnkd.in/eJNfAKZ8 📺 10m: Attention (LSerr) https://lnkd.in/eEFrS5uV 📺 15m: Transformers (LSerr) https://lnkd.in/eVcnZxad 📺 15m: GPTs (DataBrks) https://lnkd.in/es9bKS-z --- 𝗣𝗔𝗥𝗧 𝟯 - 𝗣𝗥𝗢𝗠𝗣𝗧 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥𝗜𝗡𝗚 (2h 0m) Zero/one/few-shot, chain-of-thought, self-consistency, generated knowledge, prompt chaining, ReAct Resources: 📺 60m: PE Overview (ESarav) https://lnkd.in/eFF2Yc7C 📖 90m: Prompt Eng (DAIR) https://lnkd.in/eaqPT63j 📖 30m: PE Guide (Brex) https://lnkd.in/etPjrkvw ⚒️ 0m: LangChain Hub https://lnkd.in/e_pt2cVx --- 𝗣𝗔𝗥𝗧 𝟰 - 𝗖𝗛𝗔𝗜𝗡𝗜𝗡𝗚, 𝗥𝗔𝗚, 𝗩𝗘𝗖𝗧𝗢𝗥 𝗗𝗕𝗦, 𝗔𝗚𝗘𝗡𝗧𝗦 (2h 30m) Langchain, RAG, vector databases, LlamaIndex, Open AI functions Resources: 📺 30m: LngCh ckbk1 (GKam) https://lnkd.in/eRTUH3rZ 📺 30m: LngCh ckbk2 (GKam) https://lnkd.in/ehXZNsRg ⚒️ 0m: LngCh tutorials (GKam) https://lnkd.in/eSyFQwhm 📖 30m: Adv RAG (HugFace) https://lnkd.in/eSqe-UCD 📺 5m: Vector dbs (Fireshp) https://lnkd.in/et3Zz8-k 📺 10m: LC+Pinecone (GKam) https://lnkd.in/e5E6Fv46 📺 20m: LlamaIdx (engprmpt) https://lnkd.in/eYRFG-wg 📺 30m: OpenAI fn/agnt (auto) https://lnkd.in/esZa7c3s --- 𝗣𝗔𝗥𝗧 𝟱 - 𝗙𝗜𝗡𝗘𝗧𝗨𝗡𝗜𝗡𝗚 (1h 30m) Feature-based finetuning, LoRA, RLHF Resources: 📖 15m: Finetuning (SRasc) https://lnkd.in/eTdFHQS5 📖 15m: FT v PrmpEng (NBant) https://lnkd.in/ekeHKWPe 📺 30m: RLHF intro (HugFac) https://lnkd.in/etsH53ri 📖 15m: RLHF guid (Lblerr) https://lnkd.in/e57dsuEp 📖 15m: LoRA (HugFac) https://lnkd.in/ervJK9C7 --- Cheers 🍻#ai #machinelearning #aiengineering #genai

# data[].attributes.attachment_link.title
Jack Blandin on LinkedIn: #ai #machinelearning #aiengineering #genai | 57 comments

# data[].attributes.attachment_link.description
Here's a 9-hour crash course curriculum for learning to build LLM applications. All resources are free. 𝗣𝗔𝗥𝗧 𝟭 - 𝗔 𝗚𝗘𝗡𝗧𝗟𝗘 𝗜𝗡𝗧𝗥𝗢 𝗧𝗢... | 57 comments on LinkedIn

# data[].attributes.attachment_link.readable_text
This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (Two thirds of CS50 students have never taken CS before.) This course teaches you how to solve problems, both with and without code, with an emphasis on correctness, design, and style. Topics include computational thinking, abstraction, algorithms, data structures, and computer science more generally. Problem sets inspired by the arts, humanities, social sciences, and sciences. More than teach you how to program in one language, this course teaches you how to program fundamentally and how to teach yourself new languages ultimately. The course starts with a traditional but omnipresent language called C that underlies today's newer languages, via which you'll learn not only about functions, variables, conditionals, loops, and more, but also about how computers themselves work underneath the hood, memory and all. The course then transitions to Python, a higher-level language that you'll understand all the more because of C. Toward term's end, the course introduces SQL, via which you can store data in databases, along with HTML, CSS, and JavaScript, via which you can create web and mobile apps alike. Course culminates in a final project.

Watch an introduction

How to Take this Course

Even if you are not a student at Harvard, you are welcome to "take" this course for free via this OpenCourseWare by working your way through the course's eleven weeks of material. For each week, follow this workflow:

flowchart TD A[Watch Lecture] --> |recommended|B[Watch Section] A --> D B --> |recommended|C[Watch Shorts] B --> D[Submit Problem Set] C --> D

And then submit the course's final project.

If you'd like to submit the course's problem sets and final project for feedback, be sure to create an edX account, if you haven't already. Ask questions along the way via any of the course's communities!

How to Teach this Course

If you are a teacher, you are welcome to adopt or adapt these materials for your own course, per the license.

# data[].attributes.attachment_link.description
Harvard University's introduction to the intellectual enterprises of computer science and the art of programming.

# data[].attributes.attachment_link.title
Aplicativo Python para monitorar o preço do Bitcoin em tempo real_Projetos Python intermediários

# data[].attributes.attachment_link.description
Neste tutorial, você aprenderá como construir um aplicativo Bitcoin Price Tracker usando Python. Espero que isso seja realmente interessante e divertido para vo

# data[].attributes.attachment_link.title
Classify default payments using SVM with Python

# data[].attributes.attachment_link.description
Explore Support Vector Machines (SVMs) with Python, a popular algorithm in classification tasks, with an application of machine learning in predictive modelling. Using a robust dataset featuring critical client attributes, we will predict whether or not a client will default their payment the following month. Through hands-on exercises, learn how to classify data with SVMs, optimize your model with hyperparameter tuning, and reduce data dimensionality.

# data[].attributes.attachment_link.title
Hugging Face - The AI community building the future.

# data[].attributes.attachment_link.description
We're on a journey to advance and democratize artificial intelligence through open source and open science.

# data[].attributes.body
Curso de Python

# data[].attributes.body
Curso de R

# data[].attributes.attachment_link.description
O Bootcamp de Data Analytics é um programa de capacitação para mulheres na área de programação Dados e IA, criado pela WoMakersCode. Saiba mais!

# data[].attributes.attachment_link.readable_text
Workshop Pré-MBA em Inteligência Artificial para Negócios, você recebe direito a uma garantia incondicional. Se, ao final do workshop (dia 17 de junho de 2024), você não estiver completamente satisfeito, basta entrar em contato conosco para ser 100% reembolsado.

# data[].attributes.attachment_link.readable_text
© 2023 Salesforce, Inc.

Salesforce Tower, 415 Mission Street, 3rd Floor, San Francisco, CA 94105, United States

General Inquiries: 1-800-NO-SOFTWARE
Unsubscribe or Manage Preferences
Salesforce values your privacy. Check out our Privacy Statement to learn more.

# data[].attributes.attachment_link.description
© 2023 Salesforce, Inc. Salesforce Tower, 415 Mission Street, 3rd Floor, San Francisco, CA 94105, United States General Inquiries: 1-800-NO-SOFTWARE Unsubscribe or Manage Preferences Salesforce values your privacy. Check out our Privacy Statement to learn more.

# data[].attributes.attachment_link.readable_text
In 1970, more than 300,000 people died when a strong cyclone hit the coast of Bangladesh. In 1985, another storm caused 15,000 deaths. Just six years later, another killed 140,000.

Fast-forward to 2020. Bangladesh was hit by cyclone Amphan, one of the strongest storms on record in the Bay of Bengal. The death toll was 26 - barely visible on the chart below, compared to these very deadly disasters.

That's 26 too many deaths, and the cyclone also caused huge amounts of damage: millions of people were displaced, and there were large economic losses. But tens - possibly hundreds - of thousands of lives were saved through early warnings, evacuations, and increased resilience. People in Bangladesh are much better protected from disasters than they were a few decades ago.

This development is part of a longer-term and widespread success in reducing humanity's vulnerability to storms, floods, earthquakes, and other hazards.

Bangladesh is not an isolated example. We can observe long-term improvements in the world's resilience.

Here, I will look at data published by the International Disaster Database, EM-DAT, which stretches back to 1900. In the chart below, I've shown the number of deaths from disasters, given as the decadal average. This is helpful as there is a lot of volatility in disasters from year to year. You can also explore this data annually.

The number of people killed in disasters has fallen a lot over the last century. That's despite there being four times as many people. That means the decline in death has been even more dramatic.

Single events that caused over a million deaths were not rare in the first half of the 20th century. These events were mostly floods or droughts and were often linked to agricultural shocks that caused hunger and starvation. There are many similar examples in humanity's history before the year 1900. A strong El-Niño weather event in the 1870s led to severe famines across China, India, and Brazil, killing tens of millions.

To be clear, deaths haven't declined so steeply because disasters are becoming less frequent or intense. This data also doesn't mean climate change isn't happening or isn't worsening weather events. The main reason that fewer people are dying is that we've gotten better at protecting ourselves and each other.

As we'll see later, the toll of disasters depends on our physical environment, economic resources, political systems, technological advances, and cooperation.

Massive productivity improvements have made agricultural systems far more resilient to shocks. Changes in political systems have reduced the risk of famine. As my colleagues Joe Hasell and Max Roser have shown, famines are rare in well-functioning democracies.

Weather forecasts have improved dramatically over the past 50 years, and populations can better prepare for storms, floods, droughts, and wildfires. Many more countries have early warning systems.

And overall, people worldwide are richer than they were a century ago. It's often the poorest who are most vulnerable to disasters. Having an earthquake-proof home, access to electricity and clean water, air conditioning or heating, enough money to absorb shocks in energy or food prices, and resources to recover after a disaster means you can protect yourself. Billions of people have gained access to these basic resources over the last century.

Many researchers and organizations have noted this reduction in vulnerability to various types of disasters. The World Meteorological Organization found that deaths from climate and weather-related disasters decreased almost 3-fold from 1970 to 2019. Researchers Giuseppe Formetta and Luc Feyen studied a different disaster database - Munich RE's NatCatSERVICE - and found a large reduction in global vulnerability to disasters from the 1980s to 2010s.

Three factors determine the risk of damage when a hazard hits.

First, the characteristics of the hazard itself. Is it a flood, drought, hurricane, or heatwave? What's its magnitude, speed, or power? And how long does it last? Is it a 30-minute downpour or a 5-day deluge of heavy rainfall?

Second, the number of people or the amount of infrastructure exposed to the hazard. Does the earthquake strike a densely populated city or a rural area? How many people live on a coastline inundated by storm surges or sea level rise? How much stuff - buildings, bridges, roads, and other infrastructure - is in harm's way?

Third and finally, the vulnerability of those who are exposed. A heatwave in Dubai will be less harmful than one in New Delhi because most people in Dubai have air conditioning. A strong earthquake in a country with quake-resistant infrastructure will be less damaging than the one that struck Haiti in 2010. Vulnerability is often strongly linked to income: poorer countries and communities tend to have fewer resources to protect themselves and respond afterward.

Disaster risk, then, sits at the center of all three. An increase in any of these dimensions increases the risk, while a reduction lowers it.

This is why you might hear the phrase, "There's no such thing as a natural disaster". Hazards only become disasters when they impact societies and people. A hurricane, for example, is not a disaster until it hurts or kills people or destroys homes in its path.

How does this framework explain the dramatic decline in deaths from disasters over the last century?

The hazard component has not declined. It has probably increased - on several dimensions - because of climate change.

Exposure has not declined either. There are more than four times as many people on the planet than a century ago. People have migrated to cities, often along coastlines, where events such as storm surges, cyclones, and flooding are more likely. That means more people in harm's way. What has reduced, though, is acute exposure to some events. Better weather prediction and early warning systems mean people can evacuate before a hazard hits.

What has declined is vulnerability: a long list of improvements have made communities less vulnerable. Our agricultural systems are more productive and recover from damaging events. Political systems allow for national and international support before or after a hazard strikes. People live in better buildings. Some have heating or air conditioning to protect them from extreme temperatures.

How should we think about these three factors going forward?

First, we will never be able to stop hazards completely. There will never be an end to earthquakes, cyclones, or extreme rainfall. What we can determine is how much worse they get in a changing climate. Hazards at 3°C of warming will be worse than at 2°C, which will be worse than at 1.5°C. We need to reduce our carbon emissions and avoid the worst impacts of climate change. The better we do here, the less exposed and vulnerable we will be.

But that alone won't be enough. Regardless of how successful we are in reducing our emissions, climate change is already here, and the world will get warmer even on our most ambitious pathways. Societies will need to be more resilient to these changes, and they should be protected from events unrelated to climate change - the types of events that our ancestors were exposed to 50, 100, or 1,000 years ago.

To reduce exposure, we must understand when and where hazards are most likely to hit. We must understand which regions are most vulnerable to sea-level rise or wildfires. People are still moving into areas at serious risk in the future, exposing many of them to disruption and damage.

Increasing the coverage of early warning systems will help. When a hazard is imminent, people can evacuate and their exposure can be temporarily reduced. According to the World Meteorological Organization, around one-third of the world - mostly in the poorest countries - does not have these systems. I've recently written about this.

Finally, there is a lot of room for reducing vulnerabilities. Poverty makes people most vulnerable; that's why I argue that lifting people out of poverty is an essential solution to reducing climate risks. Investing in infrastructure, making agriculture more productive, and building strong political governance are all vital. Poorer countries tend to have higher death rates from disasters, as the chart below shows. (Note that this is on a logarithmic scale.)

This relationship is even stronger for the total number of people affected by disasters, which includes those left homeless, injured, or requiring assistance. People in lower-income countries are much more vulnerable to disasters such as drought, which affects many more people. Poorer infrastructure also means they're slower to respond and recover from disasters, leaving more people affected.

Richer countries can help and have committed to doing so with adaptation funds within the Paris Agreement on climate change.

One way to build more resilient communities is to learn from other countries, like Bangladesh, that have saved many lives with the right preparation and response. The problem is that we tend to see the large and fatal events where things have gone wrong and miss the small events where people were protected. Disasters hit the news; averted ones don't.

Thankfully, some researchers are trying to change this. The project " Disasters Avoided", led by a team of disaster risk experts, tries to highlight case studies of events where disasters were prevented. As Ilan Kelman, one of the authors, writes: "We frequently see headlines about disasters. But where are the headlines covering the good news of lives saved and damage averted when disasters do not happen?"

Over the last century, we have outpaced the impacts of climate change on natural disasters. Deaths have fallen despite climate change because we've built more resilient societies.

Think of it like being in a race. Climate change has been jogging while we've been running. We've mostly stayed ahead, but there is no guarantee that things will stay that way. Slow down, and we'll be overtaken. Stay at the same pace, and we'll probably still be overtaken as the impacts of climate change accelerate.

If we fail to invest in protection measures and development trends slow down, then the progress we've made over the last century could easily reverse, and disaster deaths could start to rise again.

What's key, though, is that the direction of that trend - a continued fall or a reversal - is up to us.

None of this will happen on its own. Bangladesh's success was driven by local communities and investment in early warning systems. Chile and Japan's resilience to earthquakes came from architects, engineers, and governments upholding strict building standards. The dramatic decline in famine came from technological revolutions in agriculture and populations pushing for political rights and accountability.

International cooperation and support will be needed to ensure that the poorest and most vulnerable are not left behind.

# data[].attributes.attachment_link.title
The world has become more resilient to disasters, but investment is needed to save more lives

# data[].attributes.attachment_link.description
Deaths from disasters have fallen, but we need to build even more resilience to ensure this progress doesn't reverse.

# data[].attributes.attachment_link.readable_text
Quer se capacitar nas habilidades mais requisitadas do mercado de trabalho? Aqui está uma seleção de cursos curtos, sem limite de vagas, que te ajudarão no seu desenvolvimento pessoal e profissional. Inscreva-se e comece agora!

Cursos em destaque

Você pode fazer quantos cursos quiser, só precisa se registrar.

# data[].attributes.attachment_link.title
Cursos de formação online | Santander Open Academy

# data[].attributes.attachment_link.description
Descubra os cursos de formação oferecidos pela Santander Open Academy: tecnologia, marketing, liderança e muito mais. Venha e descubra!

# data[].attributes.attachment_link.description
Cursos de programação, ciência de dados, inteligência artificial, mentorias técnicas e de carreira na tecnologia para mulheres. Conheça!

# data[].attributes.attachment_link.readable_text
An MIT Press book

Ian Goodfellow and Yoshua Bengio and Aaron Courville

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.

The deep learning textbook can now be ordered on Amazon.

For up to date announcements, join our mailing list.

Citing the book
To cite this book, please use this bibtex entry:

@book{Goodfellow-et-al-2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}

To write your own document using our LaTeX style, math notation, or to copy our notation page, download our template files.

Errata in published editions

Deep Learning

Bibliography

FAQ

Can I get a PDF of this book?
No, our contract with MIT Press forbids distribution of too easily copied electronic formats of the book.

Why are you using HTML format for the web version of the book?
This format is a sort of weak DRM required by our contract with MIT Press. It's intended to discourage unauthorized copying/editing of the book.

What is the best way to print the HTML format?
Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well.

Can I translate the book into Chinese?

Posts and Telecom Press has purchased the rights.

If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to the website, do not hesitate to contact the authors directly by e-mail at: feedback@deeplearningbook.org

Since the book is complete and in print, we do not make large changes, only small corrections.

Known issues: In outdated versions of the Edge browser, the "does not equal" sign sometimes appears as the "equals" sign. This may be resolved by updating to the latest version.

# data[].attributes.attachment_link.description
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The deep learning textbook can now be ordered on Amazon.

# data[].attributes.attachment_link.readable_text
JavaScript is the world's most popular programming language.

JavaScript is the programming language of the Web.

JavaScript is easy to learn.

This tutorial will teach you JavaScript from basic to advanced.

Start learning JavaScript now "

Examples in Each Chapter

With our "Try it Yourself" editor, you can edit the source code and view the result.

Use the Menu

We recommend reading this tutorial, in the sequence listed in the menu.

If you have a large screen, the menu will always be present on the left.

If you have a small screen, open the menu by clicking the top menu sign ☰.

Learn by Examples

Examples are better than 1000 words. Examples are often easier to understand than text explanations.

This tutorial supplements all explanations with clarifying "Try it Yourself" examples.

If you try all the examples, you will learn a lot about JavaScript, in a very short time!

JavaScript Examples "

Why Study JavaScript?

JavaScript is one of the 3 languages all web developers must learn:

1. HTML to define the content of web pages

2. CSS to specify the layout of web pages

3. JavaScript to program the behavior of web pages

This tutorial covers every version of JavaScript:

The Original JavaScript ES1 ES2 ES3 (1997-1999)

The First Main Revision ES5 (2009)

The Second Revision ES6 (2015)

All Yearly Additions (2016, 2017, 2018, 2019, 2020)

Learning Speed

In this tutorial, the learning speed is your choice.

Everything is up to you.

If you are struggling, take a break, or re-read the material.

Always make sure you understand all the "Try-it-Yourself" examples.

The only way to become a clever programmer is to: Practice. Practice. Practice. Code. Code. Code !

Commonly Asked Questions

How do I get JavaScript?

Where can I download JavaScript?

Is JavaScript Free?

You don't have to get or download JavaScript.

JavaScript is already running in your browser on your computer, on your tablet, and on your smart-phone.

JavaScript is free to use for everyone.

My Learning

Track your progress with the free "My Learning" program here at W3Schools.

Log in to your account, and start earning points!

This is an optional feature. You can study W3Schools without using My Learning.

JavaScript References

W3Schools maintains a complete JavaScript reference, including all HTML and browser objects.

The reference contains examples for all properties, methods and events, and is continuously updated according to the latest web standards.

Complete JavaScript Reference "

JavaScript Quiz Test

Test your JavaScript skills at W3Schools!
Start JavaScript Quiz!
Kickstart your career

Get certified by completing the course

# data[].attributes.attachment_link.description
W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.

# data[].attributes.attachment_link.title
Cursos de machine learning e IA | Treinamento do Google Cloud

# data[].attributes.attachment_link.description
Tenha aulas de machine learning e IA com especialistas do Google. Dê os primeiros passos na nuvem ou aprimore suas habilidades em ML com as experiências práticas dos laboratórios interativos. Implante a tecnologia de IA mais recente e adote uma estratégia baseada em dados.

# data[].attributes.attachment_link.provider_name
AGEAD - Agência de Educação Digital e a Distância

# data[].attributes.attachment_link.description
Nome do curso: Curso Superior de Tecnologia de Ciência dos Dados Coordenador do Curso: Jonathan Andrade Projeto Pedagógico de Curso: RESOLUÇÃO Nº 538-COGRAD/UFMS, DE 25 DE FEVEREIRO DE 2022 E-mail da Coordenação: tcd.agead@ufms.br Carga Horária Total do Curso: 2400 Horas Duração: 6 Semestres Vagas: 500 vagas Forma de ingresso: Vestibular Digital e SISU Inverno VER MATRIZ

# data[].attributes.attachment_link.description
Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.

# data[].attributes.attachment_link.readable_text
This Learning Path introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning. The hands-on section of this Learning Path focuses on using best practices for unsupervised learning.

Who should take this Learning Path?
This Learning Path targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.

What skills should you have?

To make the most out of this Learning Path, you should have familiarity with programming on a Python development environment, as well as a fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

# data[].attributes.attachment_link.description
This Learning Path introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning.

# data[].attributes.attachment_link.title
HarvardX: CS50’s Introduction to Artificial Intelligence with Python

# data[].attributes.attachment_link.description
Learn to use machine learning in Python in this introductory course on artificial intelligence.

# data[].attributes.attachment_link.readable_text
Step into the world of AI with this beginner-friendly project on machine learning using Python and scikit-learn. Discover the magic of data analysis through the intuitive Random Forest algorithm. Unleash your potential as you learn to transform data into insights with captivating visualizations using matplotlib and scikitplot. Perfect for those new to AI, this project is your gateway to mastering essential skills in the exciting realm of data science and machine learning.

1. Practical Machine Learning Implementation: Dive into the practical aspects of machine learning using Python and scikit-learn, and understand how these tools can be used to solve real business problems.

2. Random Forest Classification Mastery: Master the Random Forest algorithm, a versatile and powerful classification method that can be applied to a wide range of data analysis tasks.

3. Data Visualization Techniques: Learn to create insightful 2D and 3D visualizations of your data using matplotlib and scikitplot, enhancing your ability to communicate complex information effectively.

4. Real-world Application: Gain hands-on experience by working with actual data from online trading platforms, allowing you to understand the nuances and challenges of working with real-world data sets.

Technical Requirements: A basic understanding of Python programming and some familiarity with data analysis concepts will be beneficial. All necessary machine learning and data visualization libraries, such as scikit-learn, matplotlib, and scikitplot, are pre-installed in the IBM Skills Network Labs environment.

Hardware and Software: A computer with a stable internet connection and a modern browser like Chrome, Edge, Firefox, Internet Explorer, or Safari. Our platform is designed to be compatible with current browser versions to ensure a smooth learning experience.

A Keen Mind: Bring your curiosity and willingness to explore the fascinating world of machine learning and data analytics.

# data[].attributes.attachment_link.title
Your First AI Model using Python and Scikit-learn

# data[].attributes.attachment_link.description
Step into the world of AI with this beginner-friendly project on machine learning using Python and scikit-learn. Discover the magic of data analysis through the intuitive Random Forest algorithm. Unleash your potential as you learn to transform data into insights with captivating visualizations using matplotlib and scikitplot.

# data[].attributes.attachment_link.title
Building Your First Data Analytics Portfolio: A Step-by-Step Guide (2024)

# data[].attributes.attachment_link.title
🔥 Top 10 Data Science Projects For 2024 | 10 Data Science Projects For Beginners | Simplilearn

# data[].attributes.attachment_link.readable_text
🚀 Master the language of AI with our brand new course: "Prompt Engineering for Everyone" Learn more →

Offered By: IBMSkillsNetwork

Use Llama 3 (on IBM watsonx.ai), LangChain, and RAG to enable LLMs to retrieve information from your own private document. Learn to split, embed, and summarize vast amounts of texts with advanced LLMs, crafting a smart agent that not only retrieves and condenses information, but also remembers your interactions. If you're looking to revolutionize data handling, this tutorial offers hands-on experience in AI-driven document management, setting a new standard in efficiency.

Continue reading

Guided Project

Artificial Intelligence

115 Enrolled (17 Reviews)

Use Llama 3 (on IBM watsonx.ai), LangChain, and RAG to enable LLMs to retrieve information from your own private document. Learn to split, embed, and summarize vast amounts of texts with advanced LLMs, crafting a smart agent that not only retrieves and condenses information, but also remembers your interactions. If you're looking to revolutionize data handling, this tutorial offers hands-on experience in AI-driven document management, setting a new standard in efficiency.

In an age where information overload is a common challenge, the ability to efficiently process, understand, and summarize large volumes of text, especially your own private documents that you do not want external parties to access, is invaluable. Automating the process of reading, understanding, and summarizing text documents is a game-changer across various fields, from research and development to customer service and beyond. This project uses the cutting-edge capabilities of large language models (LLM), specifically from IBM watsonx.ai, along with the innovative retrieval and summarization processes provided by LangChain and Retrieval-Augmented Generation (RAG) technologies. By the end of this tutorial, you will not only grasp the theoretical knowledge of these technologies but also apply them practically by creating a chatbot. This chatbot will serve as a personal assistant that is capable of retrieving and summarizing documents based on your queries.

After completing this project, you are able to:

Master document processing techniques: Learn to split and embed documents in formats that LLMs can efficiently process, making large volumes of text easily manageable.

Utilize advanced LLMs: Gain hands-on experience with IBM watsonx.ai, choosing the best LLMs for your document processing needs to achieve high-quality outcomes.

Customize information retrieval: Implement various retrieval chains from LangChain, adapting your document retrieval process for different purposes, and enhancing the precision of information extraction using promote templates.

Build a smart agent: Develop an agent that integrates LLMs, LangChain, and RAG technologies for an interactive experience and is capable of efficiently retrieving and summarizing documents based on user queries.

To start on this journey, you need a basic understanding of Python programming and some familiarity with AI and machine learning concepts.

Estimated Effort

45 Minutes

Skills You Will Learn

Artificial Intelligence, Generative AI, Lang Chain, LLM, NLP, RAG

# data[].attributes.attachment_link.title
Summarize private documents using RAG, LangChain, and LLMs

# data[].attributes.attachment_link.description
Use Llama 3 (on IBM watsonx.ai), LangChain, and RAG to enable LLMs to retrieve information from your own private document. Learn to split, embed, and summarize vast amounts of texts with advanced LLMs, crafting a smart agent that not only retrieves and condenses information, but also remembers your interactions.

# data[].attributes.attachment_link.title
Data Science Project - Covid-19 Data Analysis Project using Python | Python Training | Edureka