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! ----

📣 NotCSV - ¡Canal Secundario!
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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:

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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 😍
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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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▸ 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
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▸ SOLID fica FÁCIL com Essas Ilustrações
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▸ Desafio: 10 projetos rápidos para treinar Programação e conseguir um Emprego
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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:

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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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🙋🏻‍♀️ LET'S CONNECT!
================================
🤓 Join my Discord server: https://discord.gg/SK7ZC5XhcS
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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

Best Courses for Analytics:
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Please note that I may earn a commission for purchases made at the above sites! I strongly believe in the material provided; I only recommend what I truly think is great. If you do choose to make purchases through these links; thank you for supporting the channel, it helps me make more free content like this!

Hi! I will be conducting one-on-one discussion with all channel members. Checkout the perks and Join membership if interested: https://www.youtube.com/channe....l/UCG04dVOTmbRYPY1wv Check membership Perks: https://www.youtube.com/channe....l/UCG04dVOTmbRYPY1wv
. In this video, I have explained what is meant by Deep Learning, Artificial Neural Networks and Applications of Deep Learning.

All presentation files for the Machine Learning course as PDF for as low as ₹200 (INR): Drop a mail to [email protected]

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Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision.
I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence.

Hello everyone! I am setting up a donation campaign for my YouTube Channel. If you like my videos and wish to support me financially, you can donate through the following means:

From India 👉 UPI ID : siddhardhselvam2317@oksbi
Outside of India? 👉 Paypal id: [email protected]
(No donation is small. Every penny counts)
Thanks in advance!

Let's build a Community of Machine Learning experts! Kindly Subscribe here👉 https://tinyurl.com/md0gjbis

I am making a "Hands-on Machine Learning Course with Python" in YouTube. I'll be posting 3 videos per week. 2 videos on Machine Learning basics (Monday & Wednesday Evening). 1 video on a Machine Learning project (Friday Evening).

Download the Course Curriculum File from here: https://drive.google.com/file/....d/17i0c6SmncNuwSgr9W

LinkedIn: https://www.linkedin.com/in/si....ddhardhan-s-74165220

Telegram Group: https://t.me/siddhardhan

Facebook group: https://www.facebook.com/group....s/490857825649006/?r Instagram: https://www.instagram.com/siddhardhan23

Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations.

✏️ Course created by Tatev Karen Aslanyan.

✏️ Expanded course with 100 questions: https://academy.lunartech.ai/p....roduct/deep-learning

⭐️ Contents ⭐️
⌨️ 0:00:00 Introduction
⌨️ 0:08:20 Question 1: What is Deep Learning?
⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning?
⌨️ 0:15:25 Question 3: What is a Neural Network?
⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning
⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way
⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network?
⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them
⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network?
⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works
⌨️ 0:53:45 Question 10: What is Gradient Descent?
⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning?
⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning?
⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent?
⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN
⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN
⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason?
⌨️ 1:37:50 Question 17: What do you understand by a computational graph?
⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning?
⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry?
⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems?
⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks?
⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima?
⌨️ 2:03:38 Question 23: How is GD different from SGD?
⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term?
⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent.
⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)?
⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model?
⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages?
⌨️ 2:34:12 Question 29: What is RMSProp and how does it work?
⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods
⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs?
⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam?
⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN?
⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN?
⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN?
⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN?
⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN?
⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients?
⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients?
⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights?
⌨️ 3:29:18 Question 41: What is Dropout and how does it work?
⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN?
⌨️ 3:35:06 Question 43: Is Dropout like Random Forest?
⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing?
⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN?
⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN?
⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN?
⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI?
⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality?
⌨️ 3:56:47 Question 50: What are Generative Models, give examples?

PyTorch is a deep learning framework for used to build artificial intelligence software with Python. Learn how to build a basic neural network from scratch with PyTorch 2.

#ai #python #100SecondsOfCode

💬 Chat with Me on Discord

https://discord.gg/fireship

🔗 Resources

PyTorch Docs https://pytorch.org
Tensorflow in 100 Seconds
Python in 100 Seconds https://youtu.be/x7X9w_GIm1s

🔥 Get More Content - Upgrade to PRO

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🎨 My Editor Settings

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🔖 Topics Covered

- What is PyTorch?
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- Build a basic neural network with PyTorch
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- What is a tensor?
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How does AI learn? Is AI conscious & sentient? Can AI break encryption? How does GPT & image generation work? What's a neural network?
#ai #agi #qstar #singularity #gpt #imagegeneration #stablediffusion #humanoid #neuralnetworks #deeplearning

I used this to create neural nets:
https://alexlenail.me/NN-SVG/index.html

More info on neural networks
https://youtu.be/aircAruvnKk?si=Go9XAXR8TqmhX-5m

How stable diffusion works
https://youtu.be/sFztPP9qPRc?si=aF-doepEiaiDrG6z

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MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
*New 2024 Edition*

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Lecture Outline
0:00​ - Introduction
6:10- Why care about generative models?
8:16​ - Latent variable models
10:50​ - Autoencoders
17:02​ - Variational autoencoders
23:25 - Priors on the latent distribution
32:31​ - Reparameterization trick
34:36​ - Latent perturbation and disentanglement
37:40 - Debiasing with VAEs
39:37​ - Generative adversarial networks
42:09​ - Intuitions behind GANs
44:57 - Training GANs
48:28 - GANs: Recent advances
50:57 - CycleGAN of unpaired translation
55:03 - Diffusion Model sneak peak

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2024 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com

Lecture Outline
0:00​ - Introduction
3:42​ - Sequence modeling
5:30​ - Neurons with recurrence
12:20 - Recurrent neural networks
14:08 - RNN intuition
17:14​ - Unfolding RNNs
19:54 - RNNs from scratch
22:41 - Design criteria for sequential modeling
24:24 - Word prediction example
31:50​ - Backpropagation through time
33:40 - Gradient issues
37:15​ - Long short term memory (LSTM)
40:00​ - RNN applications
44:00- Attention fundamentals
46:46 - Intuition of attention
49:13 - Attention and search relationship
51:22 - Learning attention with neural networks
57:45 - Scaling attention and applications
1:00:08 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Shortform link:
https://shortform.com/artem

In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles.

OUTLINE:
00:00 Introduction
01:28 Historical background
02:50 Curve Fitting problem
06:26 Random vs guided adjustments
09:43 Derivatives
14:34 Gradient Descent
16:23 Higher dimensions
21:36 Chain Rule Intuition
27:01 Computational Graph and Autodiff
36:24 Summary
38:16 Shortform
39:20 Outro

USEFUL RESOURCES:
Andrej Karpathy's playlist: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E

Jürgen Schmidhuber's blog on the history of backprop:
https://people.idsia.ch/~juerg....en/who-invented-back


CREDITS:
Icons by https://www.freepik.com/

El Machine Learning y el Deep Learning son clave para la evolución de la Inteligencia Artificial porque permiten que las máquinas puedan aprender y mejorar por sí mismas, logrando que estas máquinas puedan hacer tareas complejas y resolver problemas de manera autónoma. Pero, ¿qué es Machine Learning y Deep Learning exactamente? Te lo explicaremos en este nuevo video de EDteam.

Y si no lo sabías #LoAprendisteEnEDteam

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1. Curso: Android desde cero https://edy.to/android-yt
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💻 Cursos recomendados:
- Carrera: Machine Learning e Inteligencia Artificial https://ed.team/especialidades/machine-learning
- Curso: Introducción al Machine Learning https://edy.to/ML-yt
- Curso: Introducción a las redes neuronales artificiales https://edy.to/redes-neuronales-yt
- Curso: Redes neuronales con TensorFlow https://edy.to/tensorflow-yt


📺 Videos recomendados
- ¿Qué es la Inteligencia Artificial y cómo está cambiando al mundo? https://www.youtube.com/watch?v=98-SzelNMzU
- ¿Qué es el Big Data? https://www.youtube.com/watch?v=M26iIqmqWkI&t=6s
- ¡Los peores errores de programación de la historia! https://www.youtube.com/watch?v=qi9nFs2caj4

⌚ Timeline:
00:00 - Introducción
00:59 - Que es Machine Learning
04:50 - Tipos de MAchine Learning
08:00 - Las Redes Neuronales
11:40 - El Deep Learning


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This deep learning course is designed to take you from beginner to proficient in deep learning. You will learn the fundamental concepts, architectures, and applications of deep learning in a clear and practical way. So get ready to build, train, and deploy models that can tackle real-world problems across various industries.

Course created by @AyushSinghSh

GitHub: https://github.com/ayush714/co....re-deep-learning-cou

⭐️ Contents ⭐️
0:00:00 Intro
0:03:07 Getting started
0:05:07 Vectors
0:21:51 Operation on vectors
0:38:52 Matrices
0:52:02 Operation on Matrices
0:52:27 Matrix Scalar Multiplication
0:55:47 Addition of Matrices
0:59:27 Properties of Matrix addition
1:03:07 Matrix Multiplication
1:08:02 Properties of Matrix Multiplication
1:18:32 Linear Combination Concept
1:36:20 Span
1:50:57 Linear Transformation
2:05:30 Transpose
2:14:02 Properties of Transpose
2:19:52 Dot Product
2:25:22 Geometric Meaning of Dot Product
2:34:32 Types of Matrices
3:04:22 Determinant
3:11:17 Geometric Meaning of Determinant
3:15:42 Calculating Determinant
3:23:37 Properties of Determinant
3:27:22 Rule of Sarus
3:48:42 Minor
3:56:49 Cofactor of a Matrix
4:00:42 Steps to calculate Cofactor of a Matrix
4:03:17 Adjoint of a Matrix
4:18:47 Trace of a Matrix
4:17:22 Properties of Trace
4:38:17 System of Equations
5:03:07 Example
5:17:42 Determinant
5:57:47 Single Variable Calculus
6:02:48 What is Calculus?
6:11:07 Ideas in Calculus
6:11:33 Differentiation
6:18:38 Integration
6:22:07 Precalculus Functions
6:43:52 Single Variable Calculus (Trigonometry Review)
6:45:02 Trigonometry functions
7:12:02 Unit Circle
7:24:32 Limit Concept
7:51:47 Definition of a limit
7:53:27 Continuity
8:00:17 Evaluating Limits
8:17:12 Sandwich Theorem
8:21:12 Differentiation
8:45:42 Differentiation as rate of Change
8:52:37 Differentiation in terms of Limit
9:04:51 Example
9:09:54 Important Differentiation Rules
9:53:12 Rule Chain Rule
10:17:27 What is Deep Learning
10:18:27 What is Machine Learning
10:36:37 Definition of Deep Learning
10:43:07 Applications
10:47:19 Introduction to Neural Networks
10:51:17 Artificial Neural Networks
11:08:31 The Perceptron
11:19:57 Linear Neural Network
11:21:32 Intuition Behind Activation function and Backpropagation Algorithm



🎉 Thanks to our Champion and Sponsor supporters:
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👾 Oscar Rahnama

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