🔥Data Scientist Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training?utm_campaign=DWBSM47aLY8&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITK - Professional Certificate Course in Data Science (India Only) - https://www.simplilearn.com/iitk-professional-certificate-course-data-science?utm_campaign=DWBSM47aLY8&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Caltech Post Graduate Program in Data Science - https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=DWBSM47aLY8&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Data Scientist Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training?utm_campaign=DWBSM47aLY8&utm_medium=DescriptionFirstFold&utm_source=Youtube
In this video, we're going to dive into the details of three popular ways to store data: databases, data lakes, and data warehouses. These terms are often thrown around in the tech world, especially during job interviews, so it's important to know what each one means and how they differ. We'll start off by explaining what each of these data storage options is and highlight their main features. Next, we'll look at the advantages and disadvantages of using each type of storage solution, helping you understand when and why you might choose one over the others. We will also compare how each system handles data. This includes discussing how they manage various forms of data, from well-organized structured data to more flexible semi-structured data and even unstructured data, which can be more complex to organize. After that, we'll go over some typical uses for databases, data lakes, and data warehouses, sharing examples to show how each is applied in real-life situations. By the end of this video, you'll have a solid grasp of the key differences between a database, a data lake, and a data warehouse. This knowledge will not only boost your understanding but also prepare you to discuss these topics confidently in job interviews.

00:00:00 - Introduction to Data Storage
00:01:58 - What is a Database?
00:03:37 - What is a Data Warehouse?
00:05:29 - What is a Data Lake?
00:07:14 - A Quick Recap
00:09:32 - Factors to Consider to Choose the Right Platform

You can also go through the slides here: https://www.slideshare.net/sli....deshow/database-vs-d

✅ Is BigQuery a data lake or data warehouse?

BigQuery is a data warehouse. It is a fully managed, serverless, highly scalable, and cost-effective multi-cloud data warehouse offered by Google Cloud. BigQuery is designed for analyzing large datasets quickly and efficiently using SQL queries.

✅ Is data lake SQL or NoSQL?

A data lake itself is neither SQL nor NoSQL; rather, it is a storage repository designed to hold large amounts of raw data in its native format, including structured, semi-structured, and unstructured data. The type of data stored in a data lake can vary, and both SQL and NoSQL databases can be part of a data lake ecosystem.

✅ Does data lake use ETL?

Yes, data lakes use ETL (Extract, Transform, Load) processes, but they can also use ELT (Extract, Load, Transform) processes.

✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

#Database #DataLake #DataWarehouse #CloudComputing #2024 #simplilearn

➡️ About AWS Cloud Practitioner Course
AWS Cloud Practitioner course is for individuals seeking an overall understanding of Amazon Web Services Cloud, independent of specific technical roles. To build AWS Cloud knowledge, you will learn about AWS Cloud concepts, services, security, architecture and support. This course also helps prepare for the AWS Certified Cloud Practitioner exam.providers: Microsoft Azure, AWS, and GCP.

✅ Key Features
- Official AWS live class training content
- Self-paced learning videos authored by AWS
- Live online training from AWS Authorized Instructors
- 1-day Instructor-Led Training Course for accelerated learning

✅ Skills Covered
- AWS Cloud Adoption Framework
- AWS Global Infrastructure
- AWS data migration solutions
- Storage and databases
- AWS IAM security levels
- AWS Compute Services
- Amazon Cloud
- Front and Edge locations
- Networking concepts
- Payasyougo pricing model
- AWS pricing models and support

Learn More:- 🔥AWS Cloud Practitioner Essentials: https://www.simplilearn.com/aws-cloud-practitioner-essentials?utm_campaign=25June2024DbvsDWvsDL&utm_medium=Description&utm_source=youtube

🔥Data Analyst Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/data-analyst-masters-certification-training-course?utm_campaign=HtHEDuhX8J8&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITK - Professional Certificate Course in Data Analytics and Generative AI (India Only) - https://www.simplilearn.com/iitk-professional-certificate-course-data-analytics?utm_campaign=HtHEDuhX8J8&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Purdue - Post Graduate Program in Data Analytics - https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=HtHEDuhX8J8&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Caltech - Data Analytics Bootcamp (US Only) - https://www.simplilearn.com/data-analytics-bootcamp?utm_campaign=HtHEDuhX8J8&utm_medium=DescriptionFirstFold&utm_source=Youtube

We will first begin with an Introduction to Big Data, setting the foundation for understanding large data sets. Next, we’ll dive into Introduction to Business Intelligence, followed by a focused Introduction to Power BI. Moving ahead, we’ll cover Top BI Terms Every Data Analyst Should Know, giving you a strong grasp of essential concepts. Then, we’ll explore Power BI Basics and learn How to Create a Power BI Dashboard, before advancing to Data Transformation Tutorial using Power Query. After that, we will practice How to Build a Daily Activity Tracker in Excel and understand How to Use Microsoft Power Query for seamless data handling. The course will further introduce SQL with an emphasis on SQL Common Table Expressions (CTE) and practical SQL Projects for Data Analysis. Lastly, we’ll explore the integration of Data Analytics Using AI tools like ChatGPT with Excel and finish by preparing for the field with Power BI Interview Questions.
here are the topics covered in this Power BI full course:

00:00:00 Introduction to Power BI Full course
00:04:02 Introduction to Big Data
00:09:14 Introduction to Power BI
00:15:09 Power BI Introduction
00:15:58 Top BI Terms every data analyst know
00:30:41 Introduction to Business Intelligence
00:44:38 How Create PowerBI Dashboard
00:52:56 Data Analyst Roadmap
06:36:08 Power BI Basics
06:51:14 How to build a Daily Activity Tracker in Excel
07:12:38 How to use microsoft power query
07:28:59 Data Transformation tutorial
07:44:36 Chatgpt and Excel with Data Analytics
07:47:53 Introduction to SQL
07:59:48 SQL CTE
07:59:58 DA using AI
09:25:40 SQL Project for data analysis
09:53:19 Power BI Interview Questions

✅ what is power bi
Microsoft Power BI is an interactive data visualization software product developed by Microsoft with a primary focus on business intelligence. It is part of the Microsoft Power Platform.

✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

⏩ Check out the Data Analytics training videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUUKnB4

#powerbi #powerbiforbeginners #powerbitips #powerbicourse #datanalytics #simplilearn #2024

➡️ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.

✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 8X higher live interaction in live online classes by industry experts
- Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Resume preparation and LinkedIn profile building
- 1:1 mock interview
- Career accelerator webinars

✅ Skills Covered
- Data Analytics
- Statistical Analysis using Excel
- Data Analysis Python and R
- Data Visualization Tableau and Power BI
- Linear and logistic regression modules
- Clustering using kmeans
- Supervised Learning

👉 Learn More At: https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=HtHEDuhX8J8&utm_medium=Description&utm_source=youtube

🔥 Introduction to Generative AI: https://www.edureka.co/introduction-generative-ai
🔥Generative AI Course: Masters Program: https://www.edureka.co/masters....-program/generative-

In this video, on *Generative AI Examples* , we will explore the fascinating world of *Generative AI* and how it's transforming industries. From creating stunning visual art to generating human-like text, Generative AI is opening new doors for creativity and innovation. We'll break down how AI models like *GPT* and *DALL-E* are used to generate everything from realistic images to engaging text content. By the end of this video, you’ll have a clear understanding of how Generative AI is shaping the future and what it means for businesses and creatives alike.

✅ 00:00 - Generative AI Examples
✅ 01:49 - Evolution of Generative AI
✅ 02:49 - What is Generative AI?
✅ 03:25 - Use-Cases of Generative AI
✅ 05:13 - Impact of Generative AI
✅ 05:57 - Examples of Generative AI

✅Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

📝Feel free to share your comments below.📝

𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
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🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🌕 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

🔵 Advanced Certificate Program in Cloud Computing with E&ICT Academy, IIT Guwahati: https://bit.ly/43vmME8

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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

- - - - - - - - - - - - - -

About Introduction to Generative AI Fundamentals Course

This course on Introduction to Generative AI is designed to equip learners with a solid understanding of generative models, from fundamental concepts to advanced applications. Starting with the basics, learners will grasp the principles of Generative AI, differentiate between generative and discriminative models, and explore popular tools in the field. Ethical considerations are addressed alongside practical skills like prompt engineering and deep learning techniques such as autoencoders and GANs. Advanced topics include working with large language models (LLMs), fine-tuning them for specific tasks, and applying reinforcement learning.

Who should take up this Generative AI learning path Course?

This course on Introduction to Generative AI is perfect for individuals aiming to pursue a career in AI, including data scientists, researchers, and developers who wish to explore generative models. It is also beneficial for professionals in various sectors like software development, marketing, and retail who want to utilize AI for problem-solving and innovation. Regardless of your level of experience, whether you are a novice or a seasoned practitioner, this course provides valuable knowledge and practical skills to enhance your expertise in Generative AI.

What are the prerequisites for this Introduction to Generative AI fundamentals Course?

There are no prerequisites required for this course, Introduction to Generative AI. However, it is beneficial to have:
Basic Python Programming Knowledge
Problem-Solving Skills
Fundamentals of Machine Learning

For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US & Others: +18885487823 (toll-free)

#generativeaiexamples #generativeai #genai #aiexamples #artificialintelligence #aitools #aiapplications

Dr. Paul Lessard and his collaborators have written a paper on "Categorical Deep Learning and Algebraic Theory of Architectures". They aim to make neural networks more interpretable, composable and amenable to formal reasoning. The key is mathematical abstraction, as exemplified by category theory - using monads to develop a more principled, algebraic approach to structuring neural networks.

We also discussed the limitations of current neural network architectures in terms of their ability to generalise and reason in a human-like way. In particular, the inability of neural networks to do unbounded computation equivalent to a Turing machine. Paul expressed optimism that this is not a fundamental limitation, but an artefact of current architectures and training procedures.

The power of abstraction - allowing us to focus on the essential structure while ignoring extraneous details. This can make certain problems more tractable to reason about. Paul sees category theory as providing a powerful "Lego set" for productively thinking about many practical problems.

Towards the end, Paul gave an accessible introduction to some core concepts in category theory like categories, morphisms, functors, monads etc. We explained how these abstract constructs can capture essential patterns that arise across different domains of mathematics.

Paul is optimistic about the potential of category theory and related mathematical abstractions to put AI and neural networks on a more robust conceptual foundation to enable interpretability and reasoning. However, significant theoretical and engineering challenges remain in realising this vision.

Please support us on Patreon. We are entirely funded from Patreon donations right now.
https://patreon.com/mlst
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail

Links:
Categorical Deep Learning: An Algebraic Theory of Architectures
Bruno Gavranović, Paul Lessard, Andrew Dudzik,
Tamara von Glehn, João G. M. Araújo, Petar Veličković
Paper: https://categoricaldeeplearning.com/

Symbolica:
https://twitter.com/symbolica
https://www.symbolica.ai/

Dr. Paul Lessard (Principal Scientist - Symbolica)
https://www.linkedin.com/in/paul-roy-lessard/

Neural Networks and the Chomsky Hierarchy (Grégoire Delétang et al)
https://arxiv.org/abs/2207.02098

Interviewer: Dr. Tim Scarfe

Pod: https://podcasters.spotify.com..../pod/show/machinelea

Transcript:
https://docs.google.com/docume....nt/d/1NiHJKTkeqYdpcg

More info about NNs not being recursive/TMs:
https://www.youtube.com/watch?v=4KIQH1VEwBI

Geometric Deep Learning blueprint:
https://www.youtube.com/watch?v=bIZB1hIJ4u8

TOC:
00:00:00 - Intro
00:05:07 - What is the category paper all about
00:07:19 - Composition
00:10:42 - Abstract Algebra
00:23:01 - DSLs for machine learning
00:24:10 - Inscrutability
00:29:04 - Limitations with current NNs
00:30:41 - Generative code / NNs don't recurse
00:34:34 - NNs are not Turing machines (special edition)
00:53:09 - Abstraction
00:55:11 - Category theory objects
00:58:06 - Cat theory vs number theory
00:59:43 - Data and Code are one and the same
01:08:05 - Syntax and semantics
01:14:32 - Category DL elevator pitch
01:17:05 - Abstraction again
01:20:25 - Lego set for the universe
01:23:04 - Reasoning
01:28:05 - Category theory 101
01:37:42 - Monads
01:45:59 - Where to learn more cat theory

In this video we will build our first neural network in tensorflow and python for handwritten digits classification. We will first build a very simple neural network with only input and output layer. After that we will add a hidden layer and check how the performance of our model changes.

🔖 Hashtags 🔖
#handwrittendigitrecognition #tensorflowtutorial #handwritingrecognition #mnisttensorflowtutorial

Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.

Github link for code in this tutorial: https://github.com/codebasics/....deep-learning-keras-

Next video: https://www.youtube.com/watch?v=icZItWxw7AI&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=8

Previous video: https://www.youtube.com/watch?v=z-ZR_8BZ1wQ&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=6

Deep learning playlist: https://www.youtube.com/playli....st?list=PLeo1K3hjS3u

Prerequisites for this series:   
1: Python tutorials (first 16 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u  
2: Pandas tutorials(first 8 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u
3: Machine learning playlist (first 16 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u

🌎 My Website For Video Courses: https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description

Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.

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When you don't always have the same amount of data, like when translating different sentences from one language to another, or making stock market predictions from different companies, Recurrent Neural Networks come to the rescue. In this StatQuest, we'll show you how Recurrent Neural Networks work, one step at a time, and then we'll show you their critical flaw that will lead us to understanding Long Short-Term Memory Networks.

English
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.

Spanish
Este video ha sido doblado al español con voz artificial con https://aloud.area120.google.com para aumentar la accesibilidad. Puede cambiar el idioma de la pista de audio en el menú Configuración.

Portuguese
Este vídeo foi dublado para o português usando uma voz artificial via https://aloud.area120.google.com para melhorar sua acessibilidade. Você pode alterar o idioma do áudio no menu Configurações.

For a complete index of all the StatQuest videos, check out...
https://app.learney.me/maps/StatQuest
...or...
https://statquest.org/video-index/

If you'd like to support StatQuest, please consider...
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...or just donating to StatQuest!
https://www.paypal.me/statquest

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

0:00 Awesome song and introduction
4:13 Basic anatomy of a recurrent neural network
5:59 Running data through a recurrent neural network
10:31 Shared weights and biases
11:23 The vanishing/exploding gradient problem.

#StatQuest #NeuralNetworks #Deeplearning #DubbedWithAloud

Learn how watsonx helps you utilize AI → https://ibm.biz/BdMpXV

The recent interest in AI as meant a lot of people have been encountering new vocabulary. Martin Keen is to help you sort it out. This video runs through key terms like machine learning, deep learning, foundation models, and large language models and how they're related to each other.

Unpacking the multilayer perceptrons in a transformer, and how they may store facts
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support
An equally valuable form of support is to share the videos.

AI Alignment forum post from the Deepmind researchers referenced at the video's start:
https://www.alignmentforum.org..../posts/iGuwZTHWb6DFY

Anthropic posts about superposition referenced near the end:
https://transformer-circuits.p....ub/2022/toy_model/in
https://transformer-circuits.p....ub/2023/monosemantic

Some added resources for those interested in learning more about mechanistic interpretability, offered by Neel Nanda

Mechanistic interpretability paper reading list
https://www.alignmentforum.org..../posts/NfFST5Mio7BCA

Getting started in mechanistic interpretability
https://www.neelnanda.io/mecha....nistic-interpretabil

An interactive demo of sparse autoencoders (made by Neuronpedia)
https://www.neuronpedia.org/gemma-scope#main

Coding tutorials for mechanistic interpretability (made by ARENA)
https://arena3-chapter1-transf....ormer-interp.streaml

Sections:
0:00 - Where facts in LLMs live
2:15 - Quick refresher on transformers
4:39 - Assumptions for our toy example
6:07 - Inside a multilayer perceptron
15:38 - Counting parameters
17:04 - Superposition
21:37 - Up next

------------------

These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/

All code for specific videos is visible here:
https://github.com/3b1b/videos/

The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcam....p.com/album/the-musi
https://open.spotify.com/album..../1dVyjwS8FBqXhRunaG5

------------------

3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.

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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.

Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios

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The Latest from PBS Digital Studios: https://www.youtube.com/playli....st?list=PL1mtdjDVOoO

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Machine Learning es una de las ramas de la Inteligencia Artificial que está revolucionando el mundo, ChatGPT, GPT3, Dalle2, miniDalle, Stable Diffusion, MidJourney, alguna de estas IAs te sonarán pero seguro que aparte de saber que son IA, no sabes cómo aprenden. Échate unas risas y aprende conmigo qué es el ML y las 3 ramas principales del ML: Aprendizaje Supervisado, Aprendizaje No Supervisado y Aprendizaje por Refuerzo.

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#machinelearning #ias #inteligenciaartificial

El Deep Learning ha cambiado el mundo en sólo una década y hoy os contaré cómo esta rama de la informática podría seguir evolucionando. Y lo haremos desde el comienzo, con las redes neuronales más sencillas hasta Google Gemini, la futura promesa de Google DeepMind, pasando eso sí por los enormes modelos fundacionales como ChatGPT. ¡Bienvenidos a la nueva temporada de DotCSV!

📹 EDICIÓN: Carlos Santana y Diego Gonzalez (Diocho)

--- ¡MÁS DOTCSV! ----

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-- ¡MÁS CIENCIA! ---

🔬 Este canal forma parte de la red de divulgación de SCENIO. Si quieres conocer otros fantásticos proyectos de divulgación entra aquí:

http://scenio.es/colaboradores

In this video you will learn about three very common methods for data dimensionality reduction: PCA, t-SNE and UMAP. These are especially useful when you want to visualise the latent space of an autoencoder.

If you want to learn more about these techniques, here are some key papers:
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction https://arxiv.org/abs/1802.03426
- Stochastic Neighbor Embedding https://papers.nips.cc/paper_f....iles/paper/2002/hash
- Visualizing Data using t-SNE https://www.jmlr.org/papers/vo....lume9/vandermaaten08

And if you want to learn about even more recent techniques such as TriMAP and PACMAP, here are the papers:
- TriMap: Large-scale Dimensionality Reduction Using Triplets https://arxiv.org/abs/1910.00204
- PaCMAP https://arxiv.org/abs/2012.04456

Chapters:
00:36 PCA
05:15 t-SNE
13:30 UMAP
18:02 Conclusion

This video features animations created with Manim, inspired by Grant Sanderson's work at @3blue1brown. Here is the code that I used to make this video: https://github.com/ytdeepia/La....tent-Space-Visualisa

If you enjoyed the content, please like, comment, and subscribe to support the channel!


#DeepLearning #PCA #ArtificialIntelligence #tsne #DataScience #LatentSpace #Manim #Tutorial #machinelearning #education #somepi

✅ 𝗔𝗚𝗢𝗥𝗔 𝗘𝗨 𝗧𝗘𝗡𝗛𝗢 𝗨𝗠 𝗖𝗨𝗥𝗦𝗢 😍
▸ Olha que massa que ficou: https://curso.dev/

✅ALURA COM 10% DE DESCONTO: https://www.alura.com.br/promocao/news-deschamps

✅ÍNDICE:

01 - Diferença entre todos os termos: https://www.youtube.com/watch?v=ccZ2pyr3YDw&list=PLMdYygf53DP7YZiFUtGTWJJlvynRyrna-&index=2
02 - Introdução ao Python: https://www.youtube.com/watch?v=Gojqw9BQ5qY&list=PLMdYygf53DP7YZiFUtGTWJJlvynRyrna-&index=2
03 - Introdução a Data Science: https://www.youtube.com/watch?v=F608hzn_ygo&list=PLMdYygf53DP7YZiFUtGTWJJlvynRyrna-&index=3
04 - Introdução a Machine Learning: https://www.youtube.com/watch?v=JyGGMyR3x5I&list=PLMdYygf53DP7YZiFUtGTWJJlvynRyrna-&index=4
05 - Introdução a Data Visualization: https://www.youtube.com/watch?v=qLiEDvs57nk&list=PLMdYygf53DP7YZiFUtGTWJJlvynRyrna-&index=5

Este é o primeiro vídeo de uma playlist SENSACIONAL sobre Inteligência Artificial e que conta com o apoio da Alura e o seu co-fundador Guilherme Silveira.

Este vídeo serve para dar uma visão macro de todos os termos geralmente relacionados ao tópico "Inteligência Artificial" como por exemplo Machine Learning (Aprendizado de Máquina), Data Science (Cientista de Dados), Deep Learning e até coisas como Data Visualization. Chegou a hora de clarearmos na nossa cabeça esses termos e inclusive colocar a mão na massa!!!

Alura, muito obrigado pelo apoio ao canal, tanto por trazer um conteúdo que vai mudar a vida de muita gente quanto por garantir o emprego a longo prazo de todo mundo que seguir essa playlist!

Se você também quiser apoiar a Alura, confira os cursos deles com 10% de desconto no link abaixo:

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✅ 𝗚𝗢𝗦𝗧𝗔 𝗗𝗘 𝗡𝗢𝗧𝗜𝗖𝗜𝗔𝗦 𝗦𝗢𝗕𝗥𝗘 𝗧𝗘𝗖𝗡𝗢𝗟𝗢𝗚𝗜𝗔?

▸ Então você vai pirar nisso: https://filipedeschamps.com.br/newsletter


✅ 𝗢𝗟𝗛𝗔 𝗤𝗨𝗘 𝗠𝗔𝗦𝗦𝗔!

▸ Se essas conversas aqui estão fazendo você perceber coisas diferentes no seu código, ou na sua profissão de desenvolvedor, considera se tornar um Membro da Turma. É muito massa porque dá pra ter uma conversa muito mais próxima e discutir coisas bem diferentes e super importantes do nosso dia a dia: https://www.youtube.com/FilipeDeschamps/join


✅ 𝗢𝗦 𝗠𝗘𝗟𝗛𝗢𝗥𝗘𝗦 𝗩𝗜𝗗𝗘𝗢𝗦 𝗗𝗢 𝗖𝗔𝗡𝗔𝗟

▸ Preguiça: Descobri Como Consertar o Meu Maior Problema
https://youtu.be/rHANBi7E2cI

▸ 3 Técnicas Que Eu Uso Para Aprender a Programar Qualquer Coisa
https://youtu.be/ZtMzB5CoekE

▸ SOLID fica FÁCIL com Essas Ilustrações
https://youtu.be/6SfrO3D4dHM

▸ Eu fiz um dos melhores cursos de Programação do Mundo!
https://youtu.be/elIl48sZ3rA

▸ Desafio: 10 projetos rápidos para treinar Programação e conseguir um Emprego
https://youtu.be/fYR9L2ZmodM

Machine Learning is one of those things that is chock full of hype and confusion terminology. In this StatQuest, we cut through all of that to get at the most basic ideas that make a foundation for the whole thing. These ideas are simple and easy to understand. After watching this StatQuest, you'll be ready to learn all kinds of new and exciting things about Machine Learning.

If you're interested in learning more about SoSA, here's the link: http://thesosa.org/

Here's the link to the video about the bias/variance tradeoff:
https://youtu.be/EuBBz3bI-aA

Here's the link to the video about cross-validation, aka the way to determine which samples go into your training set and which samples go into your testing set: https://youtu.be/fSytzGwwBVw

For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/

If you'd like to support StatQuest, please consider...

Buying The StatQuest Illustrated Guide to Machine Learning!!!
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC

Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channe....l/UCtYLUTtgS3k1Fg4y5

...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/s....tatquest-with-josh-s

...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

...or just donating to StatQuest!
https://www.paypal.me/statquest

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

0:00 Awesome song and introduction
0:35 A silly example of classification
2:24 A silly example of regression
3:37 The Bias/Variance Tradeoff
8:15 Fancy machine learning
8:56 Evaluating the performances of a decision tree
11:12 Summary of concepts and main ideas


#statquest #ML

Artificial Intelligence, Machine Learning, and Deep Learning have become the most talked-about technologies in today’s commercial world as companies are using these innovations to build intelligent machines and applications. And although these terms are dominating business dialogues all over the world, many people have difficulty differentiating between them.

In this video, Dr. Sheraz Naseer, a cyber security and deep learning expert, will be explaining:
- What are AI, ML, and Deep Learning?
- What's the difference between AI, ML & DL?
- How can you make a career in this field?

Playlist - Data Science Series: https://www.youtube.com/playli....st?list=PLxf3-FrL8Gz

👉 For free online courses, Visit Irfan Malik's channel: http://youtube.com/@muhammadirfanmalik

Much more:

Facebook: https://www.facebook.com/iamirfansaeedmalik/
Facebook Group: https://www.facebook.com/groups/2763770613931485/
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#irfanmalik #xevensolutions #xevenskills #muhammadirfan #artificialintelligence #machinelearning #deeplearning #datascience #nlp #freeonlinecourses

AI Learning Roadmap (PDF) 👉 https://tinyurl.com/3hdjnbaa

Master Python for AI Projects 👉 https://python-course-earlybird.framer.website/

Receive top data science/ AI insights in your inbox 👉 https://thu-vu.ck.page/49c5ee08f6

Article version of this video (if you prefer reading) 👉 https://medium.com/towards-dat....a-science/how-to-lea


🔑 TIMESTAMPS
================================
0:00 - Intro
1:08 - Why should you learn AI
2:20 - Low code / No code approach
3:26 - Programming (Python)
5:09 - Git
6:16 - APIs
7:03 - Neural networks
8:56 - Neural network architectures
10:08 - Text embeddings & vector store
10:38 - Real-world projects
11:52 - Mental models & specializations
13:56 - Extra resources


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================================
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🙋🏻‍♀️ LET'S CONNECT!
================================
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As a member of the Amazon and Coursera Affiliate Programs, I earn a commission from qualifying purchases on the links above. By using the links you help support this channel at no cost for you.

#gpt #ai #datascience #ThuVu #dataanalytics