Mario Avilés

@marioaviles19

Engineering Manager at ARS Servicios Professionales enterprise, previous work as an support engineer for hardware and software at HP and Sonda Chile enterprises

Providencia, Santiago, Chile

Joined November 2014

2.1K Following

1.7K Followers

1K Posts

Mario Avilés @marioaviles19

15 days ago

Tecnología: HPE VM Essentials - Morpheus - HPE redes inteligentes Aruba - Juniper

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marioaviles19 retweeted

Brais Moure

@MoureDev

6 months ago

¿Sabes que Anthropic (creadores de Claude Code) tiene su propia academia de cursos gratis? → 13 cursos desde cero → Claude Code, APIs, MCPs, Skills... → Con certificado de finalización https://t.co/URRSvJYO1F

MoureDev's tweet photo. ¿Sabes que Anthropic (creadores de Claude Code) tiene su propia academia de cursos gratis?

→ 13 cursos desde cero
→ Claude Code, APIs, MCPs, Skills...
→ Con certificado de finalización

https://t.co/URRSvJYO1F https://t.co/p85POtjWew

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Rohit Kumar Tiwari @_rohit_tiwari_

6 months ago

BOOKMARK Worthy: This guy literally built a full set of notebooks that actually teach deep learning, from basics to transformers. https://t.co/AW01sBiON1 Notebook 1.1 - Background mathematics Notebook 2.1 - Supervised learning Notebook 3.1 - Shallow networks I Notebook 3.2 - Shallow networks II Notebook 3.3 - Shallow network regions Notebook 3.4 - Activation functions Notebook 4.1 - Composing networks Notebook 4.2 - Clipping functions Notebook 4.3 - Deep networks Notebook 5.1 - Least squares loss Notebook 5.2 - Binary cross-entropy loss Notebook 5.3 - Multiclass cross-entropy loss Notebook 6.1 - Line search Notebook 6.2 - Gradient descent Notebook 6.3 - Stochastic gradient descent Notebook 6.4 - Momentum Notebook 6.5 - Adam Notebook 7.1 - Backpropagation in toy model Notebook 7.2 - Backpropagation Notebook 7.3 - Initialization Notebook 8.1 - MNIST-1D performance Notebook 8.2 - Bias-variance trade-off Notebook 8.3 - Double descent Notebook 8.4 - High-dimensional spaces Notebook 9.1 - L2 regularization Notebook 9.2 - Implicit regularization Notebook 9.3 - Ensembling Notebook 9.4 - Bayesian approach Notebook 9.5 - Augmentation Notebook 10.1 - 1D convolution Notebook 10.2 - Convolution for MNIST-1D Notebook 10.3 - 2D convolution Notebook 10.4 - Downsampling & upsampling Notebook 10.5 - Convolution for MNIST Notebook 11.1 - Shattered gradients Notebook 11.2 - Residual networks Notebook 11.3 - Batch normalization Notebook 12.1 - Self-attention Notebook 12.2 - Multi-head self-attention Notebook 12.3 - Tokenization Notebook 12.4 - Decoding strategies Notebook 13.1 - Encoding graphs Notebook 13.2 - Graph classification Notebook 13.3 - Neighborhood sampling Notebook 13.4 - Graph attention Notebook 15.1 - GAN toy example Notebook 15.2 - Wasserstein distance Notebook 16.1 - 1D normalizing flows Notebook 16.2 - Autoregressive flows Notebook 16.3 - Contraction mappings Notebook 17.1 - Latent variable models Notebook 17.2 - Reparameterization trick Notebook 17.3 - Importance sampling Notebook 18.1 - Diffusion encoder Notebook 18.2 - 1D diffusion model Notebook 18.3 - Reparameterized model Notebook 18.4 - Families of diffusion models Notebook 19.1 - Markov decision processes Notebook 19.2 - Dynamic programming Notebook 19.3 - Monte-Carlo methods Notebook 19.4 - Temporal difference methods Notebook 19.5 - Control variates Notebook 20.1 - Random data Notebook 20.2 - Full-batch gradient descent Notebook 20.3 - Lottery tickets Notebook 20.4 - Adversarial attacks Notebook 21.1 - Bias mitigation Notebook 21.2 - Explainability

_rohit_tiwari_'s tweet photo. BOOKMARK Worthy: This guy literally built a full set of notebooks that actually teach deep learning, from basics to transformers. https://t.co/AW01sBiON1

Notebook 1.1 - Background mathematics
Notebook 2.1 - Supervised learning
Notebook 3.1 - Shallow networks I
Notebook 3.2 - Shallow networks II
Notebook 3.3 - Shallow network regions
Notebook 3.4 - Activation functions
Notebook 4.1 - Composing networks
Notebook 4.2 - Clipping functions
Notebook 4.3 - Deep networks
Notebook 5.1 - Least squares loss
Notebook 5.2 - Binary cross-entropy loss
Notebook 5.3 - Multiclass cross-entropy loss
Notebook 6.1 - Line search
Notebook 6.2 - Gradient descent
Notebook 6.3 - Stochastic gradient descent
Notebook 6.4 - Momentum
Notebook 6.5 - Adam
Notebook 7.1 - Backpropagation in toy model
Notebook 7.2 - Backpropagation
Notebook 7.3 - Initialization
Notebook 8.1 - MNIST-1D performance
Notebook 8.2 - Bias-variance trade-off
Notebook 8.3 - Double descent
Notebook 8.4 - High-dimensional spaces
Notebook 9.1 - L2 regularization
Notebook 9.2 - Implicit regularization
Notebook 9.3 - Ensembling
Notebook 9.4 - Bayesian approach
Notebook 9.5 - Augmentation
Notebook 10.1 - 1D convolution
Notebook 10.2 - Convolution for MNIST-1D
Notebook 10.3 - 2D convolution
Notebook 10.4 - Downsampling & upsampling
Notebook 10.5 - Convolution for MNIST
Notebook 11.1 - Shattered gradients
Notebook 11.2 - Residual networks
Notebook 11.3 - Batch normalization
Notebook 12.1 - Self-attention
Notebook 12.2 - Multi-head self-attention
Notebook 12.3 - Tokenization
Notebook 12.4 - Decoding strategies
Notebook 13.1 - Encoding graphs
Notebook 13.2 - Graph classification
Notebook 13.3 - Neighborhood sampling
Notebook 13.4 - Graph attention
Notebook 15.1 - GAN toy example
Notebook 15.2 - Wasserstein distance
Notebook 16.1 - 1D normalizing flows
Notebook 16.2 - Autoregressive flows
Notebook 16.3 - Contraction mappings
Notebook 17.1 - Latent variable models
Notebook 17.2 - Reparameterization trick
Notebook 17.3 - Importance sampling
Notebook 18.1 - Diffusion encoder
Notebook 18.2 - 1D diffusion model
Notebook 18.3 - Reparameterized model
Notebook 18.4 - Families of diffusion models
Notebook 19.1 - Markov decision processes
Notebook 19.2 - Dynamic programming
Notebook 19.3 - Monte-Carlo methods
Notebook 19.4 - Temporal difference methods
Notebook 19.5 - Control variates
Notebook 20.1 - Random data
Notebook 20.2 - Full-batch gradient descent
Notebook 20.3 - Lottery tickets
Notebook 20.4 - Adversarial attacks
Notebook 21.1 - Bias mitigation
Notebook 21.2 - Explainability

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Mario Avilés @marioaviles19

9 months ago

@Ejaz_bashir1 Thanks, @InovasSpA

Who to follow

Todd Dwyer

@ToddDwyer

I tweet about celebrity #RealEstate & Industry news! DRE# 01357463 #palmspringsrealestate #palmspringsca #palmsprings #redfin #coachellavalley

☆Your Success Coach☆I Believe You Should Achieve Your Dreams & I'll Help You Do It☆ ☆Leadership☆Coach☆Mentor☆Speaker☆My Glass Is Always FULL of Mountain Dew☆

marioaviles19 retweeted

Ejaz Bashir

@Ejaz_bashir1

9 months ago

80+ AI tools to finish months of work in few hours 1. Research - ChatGPT - Copilot - Gemini - Abacus - Perplexity 2. Image - Fotor - Dalle 3 - Stability AI - Midjourney - Microsoft Designer 3. CopyWriting - Rytr - Copy AI - Writesonic - Adcreative AI 4. Writing - Jasper - HIX AI - Jenny AI - Textblaze - Quillbot 5. Website - 10Web - Durable - Framer - Style AI 6. Video - Klap - Opus - Eightify - InVideo - HeyGen - Runway - ImgCreator AI - Morphstudio .xyz 7. Meeting - Tldv - Otter - Noty AI - Fireflies 8. SEO - VidIQ - Seona AI - BlogSEO - Keywrds ai 9. Chatbot - Droxy - Chatbase - Mutual info - Chatsimple 10. Presentation - Decktopus - Slides AI - Gamma AI - Designs AI - Beautiful AI 11. Automation - Make - Zapier - Xembly - Bardeen 12. Prompts - FlowGPT - Alicent AI - PromptBox - Promptbase - Snack Prompt 13. UI/UX - Figma - Uizard - UiMagic - Photoshop 14. Design - Canva - Flair AI - Designify - Clipdrop - Autodraw - Magician design 15. Logo Generator - Looka - Designs AI - Brandmark - Stockimg AI - Namecheap 16. Audio - Lovo ai - Eleven labs - Songburst AI - Adobe Podcast 17. Productivity - Merlin - Tinywow - Notion AI - Adobe Sensei - Personal AI 18. Social media management - Tapilo - Typefully - Hypefury - TweetHunter Follow @Ejaz_bashir1 for more AI tools hacks and updates.

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Mario Avilés @marioaviles19

9 months ago

@srishticodes Thanks, @InovasSpA

Mario Avilés @marioaviles19

9 months ago

@Ben_escrito Thanks, @InovasSpA

marioaviles19 retweeted

Ben Pierron

@Ben_escrito

9 months ago

La gente cree que aprender IA es difícil. No lo es. Encontré 14 guías gratuitas para que tú no tengas que buscarlas: 1. OpenAI Academy - https://t.co/qysC2VsmEc La academia oficial de ChatGPT y OpenAI. 2. Coursera - https://t.co/eLiXEq5M2I Aprende Machine Learning con Andrew Ng. 3. Learn Prompting - https://t.co/DLPmXXjbbe Guías paso a paso sobre prompt engineering. 4. Curso de Veo - https://t.co/Btf1m5I3Qd La guía oficial para dominar Veo, el modelo de video de Google (el mejor del mercado). 5. Curso de Midjourney - https://t.co/s0Sjjtd4tx Guía rápida, sencilla (y gratuita) para Midjourney v7. 6. Perplexity Labs - https://t.co/wMuP9wDYzW La función más interesante de Perplexity, explicada. 7. Escribe una tesis con IA - https://t.co/Uky1KxDhKr Video hecho por Ruben Hassid. 8. OpusClip para editar video - https://t.co/UPToVaUKFW La mejor IA para editar videos (hasta ahora). 9. Deeplearning AI - https://t.co/VtJZg7Q2JM Mejores introducciones a la IA (perfecto para principiantes). 10. Claude de la A a la Z - https://t.co/lKYJFHCT2M La guía oficial de Claude, hecha por su propio equipo. 11. Imágenes con ChatGPT - https://t.co/cNUJf2yOtJ? Es mío. Pero cubro el 80% en solo 10 diapositivas. Nada mal. 12. Prompting con Gemini - https://t.co/JDTNEd3xav La mejor guía de prompts, directamente de Google. 13. Guía de agentes de IA - https://t.co/Kbkz59Bzxg OpenAI hizo esta guía (de alguna forma poco conocida). 14. Profundización en LLMs - https://t.co/1LfjGvFShx Si tienes 3 horas → una mina de oro absoluta.