#GoogleColab

[fptcloud1]

Google Colab là gì? Công cụ lập trình Python miễn phí trên nền tảng đám mây

Google Colab (Colaboratory) là dịch vụ miễn phí cho phép bạn viết và chạy mã Python trực tiếp trên trình duyệt, tích hợp sẵn GPU và hỗ trợ thư viện mạnh mẽ như TensorFlow, PyTorch… Đây là công cụ lý tưởng cho học máy (machine learning), khoa học dữ liệu và lập trình AI mà không cần cấu hình máy cồng kềnh.

Đọc chi tiết: Colab là gì?

[techaivisionx]

Machine Learning for Beginners: Build Your First AI Model!

Welcome to your complete beginner's guide to machine learning! Ever wondered how artificial intelligence really works? This video is your first step into that exciting world. No PhD or complex math required – just your curiosity and computer!

In this interactive tutorial, we break down the basics of machine learning, showing you that it's not magic, but smart logic, data, and pattern recognition. Whether you have some Python skills or are an absolute beginner, you'll learn everything you need to create your first AI model. We'll walk you through each step, from understanding supervised, unsupervised, and reinforcement learning to building a real-world model that predicts house prices!

Discover how to handle data, make it useful, and check how well your model performs. We'll use easy-to-understand tools like Google Colab, scikit-learn, pandas, and matplotlib, turning you from a data explorer into a confident model creator. By the end of this course, you won't just understand machine learning for beginners; you'll be able to apply it. Ready to start your AI adventure and build your first AI model? Let's begin!

[aiandemily]

コード生成AIツール完全ガイド！おすすめ5選と活用のコツを徹底解説

[tpointtechedu]

[interdatavn]

Google Colab là gì? Tính năng & Lợi ích của Google Colab

Google Colab là sự kết hợp hoàn hảo giữa Python, điện toán đám mây và sức mạnh tính toán (GPU/TPU miễn phí). Nền tảng này trở thành trợ thủ đắc lực cho các nhà phát triển và nhà khoa học dữ liệu trong việc xây dựng, huấn luyện mô hình học máy và phân tích dữ liệu hiệu quả. Hãy khám phá Google Colab là gì và tại sao nó lại quan trọng qua bài viết sau.

Xem chi tiết bài viết tại đây: Google Colab là gì? Tính năng & Lợi ích của Google Colab

INTERDATA

Website: Interdata.vn Hotline: 1900-636822 Email: Info@interdata.vn VPĐD: 240 Nguyễn Đình Chính, P.11. Q. Phú Nhuận, TP. Hồ Chí Minh VPGD: Số 211 Đường số 5, KĐT Lakeview City, P. An Phú, TP. Thủ Đức, TP. Hồ Chí Minh

[codewithrees]

Title:

Chess AI Simulation: Two AIs Playing Each Other

Short Description:

This tutorial introduces you to creating a Python-based chess simulation where two AIs play against each other, making random moves. Perfect for exploring basic AI logic and game programming.

[generativeaimasters]

🌟 Week 3: Generative AI Training Progress! 🌟 E week lo practical learning start cheddam with hands-on projects to enhance your Generative AI skills:

📢 Missed Weeks 1 and 2? Week 1 & Week 2 plan chudandi to follow the full program and get the complete learning experience.

🔗 Follow cheyyandi for remaining weeks updates and continue mastering Generative AI! 💡

[bestdatasciencecourseindia]

🔍🚀 Build Your Own Image Recognizer in 10 Minutes! | Step-by-Step Tutorial 📸🧠

🚀 New Blog Alert: Build an Image Recognizer in Under 10 Minutes! 🖼️💻

Are you ready to dive into the world of Deep Learning? Our latest blog takes you through a step-by-step tutorial on how to build and train an Image Recognizer using your own dataset. This is the perfect guide for anyone looking to get hands-on experience in building a real-world application using Deep Learning!

🔍 What You Will Learn:

How to scrape images from Google and create your own dataset 📸

Building and training an Image Recognizer using Deep Convolutional Neural Networks (CNN) 🧠

Visualizing classification results and interpreting model performance 📊

Testing your model with new images ✨

Whether you're new to Deep Learning or just want to refine your skills, this blog has got you covered. And the best part? You can do all of this in Google Colab with just a few lines of code! 🖥️

👉 Read the full tutorial here: Build an Image Recognizer in Less Than 10 Minutes

#AI #MachineLearning #DeepLearning #ImageRecognition #DataScience #AnalyticsJobs #Fastai #GoogleColab

[ankitcodinghub]

CSE641 - Deep Learning Assignment - 1 Solved

Instructions. • This coding assignment revolves around developing a basic Multi-Layer Feedforward network and manually training it. Additional instructions for each question are provided accordingly. • You have to submit the GoogleColab file named as rollno-A1.ipynb. Files submitted without following naming convention, .py files and any other will not be evaluated. For example if your roll number…

[ingoampt]

Compare iPad M4 chip, Mac M4 Chip , Cloud & GoogleColab for building deep learning model - day 10

Aspect Details Considerations CPU and GPU Multi-core CPU, 10-core GPU Efficient for basic processing and on-device inference but limited for intensive deep learning tasks. Neural Engine 16-core Neural Engine Optimized for running pre-trained Core ML models efficiently, but limited utility for training tasks. Memory Configuration 16GB unified memory (on 1TB model) Adequate for small to…

[ranarh]

I've been playing around with AI generated images using Disco Diffusion for a few days and find it very intriguing. DD is open source software that Google Colab provides free access to (as you might expect, you can get nicer features by paying) and can also be run from home. It is a text-to-image generator, meaning you enter a description called prompt, adjust a bunch of parameters to your liking, and watch as an initial colour noise develops into something it "thinks" you described. [Allow me to deviate a bit here: there is a lot of confusion about what computers can and can't do and even more about the capabilities of AI. Right now, they are just dumb machines. Yes, they can produce smalltalk - who can't - and as with any task, if we can use the computer's strength, it shows better results than humans, for example in diagnosing skin cancer, because no human can compare millions of images. But they are just machines. They can't think. They are not alive. We cannot trust them to make decisions for us. Please never forget that, people.]

Diffusion is the math, and Disco is the way it is applied, there are other ways like Latent Diffusion or Progrock (see a pattern here?). If you've used programs like Apophysis or Mandelbulber, you'll feel right at home - change a bunch of numbers, go away for coffee (rendering these things takes time), and come back to find pretty pictures have materialised. Other powerful AI image generators are currently invite only, like the impressive Midjourney and Dall-E, or NVIDIA's beta Canvas that requires quite good hardware.

What I like about the image DD procudes is the density and arbitrary additions taking cues from sources you never knew existed - you literally describe the image to someone else -, it's like being on the client side of illustration for once, and nothing turns out the way you meant to, but is still cool, so you run with it. The same feature of course makes it hard to get it to do what you want (although you can use initial images as a template it will then fill, with mixed success). As a learning experience for myself as an artist, I think it's very interesting to see how much I need to put to words to describe what I want. I'm not one of the artists who smartly write down a description and keywords for their intended artwork beforehand - maybe I should - so I found I had to really make an effort and think, what kind of image, how dense the detail, the camera setup, art style, maybe influenced by an artist? To DD, it's a big difference if you present the same description for a rainy or sunny day. And everything you leave out it will fill on its own; since the AI was trained with images from the internet, I'm sure the eponymous farm will get a burger drive-thru. That's where many surprises lie, what was clear to you, but you didn't say, may have been left out or filled unexpectedly. Even with a good visual library, I think this experience of recalling it in words is very useful to artists. Many images cannot (and I guess, should not) be used as-is, certainly not for illustrations, but they are nice starting points and free to use. However, people have indeed managed to make very coherent, detailed images with it, especially landscapes. Output sizes are quite small with a free account; on the other hand, there are tools available that can upscale nicely to workable solutions.

So, don't be afraid of what looks like math, it's really quite harmless, and give it a try to see what you can make it do. Another tool I have also played with is WOMBO Dream, an app that creates small prompts in premade styles (but also accepts "by <artist>"), with much greater abstraction than Disco Diffusion, and only in portrait format. They too are free to use.

Disco Diffusion: https://colab.research.google.com/github/alembics/disco-diffusion/blob/c8704249481926a126f93741100facbd5471f32b/Disco_Diffusion.ipynb Most quoted starting guide: https://docs.google.com/document/d/1l8s7uS2dGqjztYSjPpzlmXLjl5PM3IGkRWI3IiCuK7g/mobilebasic WOMBO Dream: https://app.wombo.art/

Check Discord and reddit for helpful communities; more links in the comments. Now finally, a diverse assortment of images I had DD create with their respective prompts! They are unaltered.

[onlyconcurseira]

Função DataFrame na Biblioteca Pandas(phyton)

Função DataFrame na Biblioteca Pandas(phyton)

Esse artigo tem a finalidade de pesquisar a função DataFrame e a máquina de aprendizagem (machine learning) conhecida como Pandas da linguagem de programação Python. Já tinha iniciado um curso em 2020, tendo como base um livro de programação que comprei de “segunda mão”. Mas o entendimento parou quando cheguei em DataFrame. E agora, seguindo uma playlist do Youtube da UNIVESP, que é uma…

[codesolutionsstuff]

TOP 10 IN-DEMAND DATA ANALYTICS SKILLS TO LEARN IN 2022

Data science is a game-changing technology that has become increasingly popular in an extensive number of industries.

Table of Content

- Machine Learning - Python - R - Cloud Computing - Deep Learning - Tableau - Google Colab - Statistics - Data Visualisation - Artificial Intelligence (AI) Data science is a game-changing technology that has become increasingly popular in an extensive number of industries. The demand for data scientists has been steadily increasing over the last few years. Many companies, such as yours and mine, are looking to hire a professional who can handle our company's ever-growing volume of data. Data scientists are responsible for making the most of all business data, so I know this job is perfect for you. Data scientists are in high demand, with a shortage of skilled professionals to take on the task. When looking to hire someone for this position, it is important to consider an online program that can ensure candidates are well versed in both new techniques and technology. Visualization is becoming a very important way of making sense of the Excel or Google Sheets which are becoming increasingly common. This will happen as big data becomes more common; the age of machine-analysis has already arrived. Data Visualisation is a powerful way of converting data into something easier to understand. It can make patterns clear, show the most important numbers and present data in a way that's easy to understand. Infographics offer a unique way to make your data more understandable but it’s not as easy as adding an “info” element. You also need to balance between the way information is presented with its functionality. For example, people will be more interested in this infographic since it combines aesthetics and functionality by using visuals that convey the data sets you are representing. The success of a graph lies in the details. The lack of any detail can make it unnoticeable or unclear, but on the other hand if too much is included it may detract from the main idea or "say" too much. It's no secret that making data work together is an art form. Here are the top 10 skills you should study if you want to be a data scientist in 2022

1. Machine Learning

A lot of organizations use machine learning algorithms to predict upcoming events. It's important for these companies to hire data science experts who can create effective analytics algorithms. Data scientists are also able to go a step further and analyze the data further using machine learning technology. To learn more about the importance of machine learning in data science, you should consider enrolling in our ‘PG Program in Data Analytics and ML.’

2. Python

Python has popularised itself as a Data Science language due to its simplicity. Python is great for: data munging, analysis, and visualization of data. Python is one of the most commonly-used languages among data scientists. There are many different things they work on and Python makes it easy to start doing them all. This can help your business grow, as did happen with my company.

3. R

R is another popular programming language in the data science field. It's very easy to learn if you use a reputable online course. It'll teach you all about Data Science through practical examples and lectures. R is great for pulling critical data from huge datasets. This makes it the perfect language for anyone who needs to work with data in a variety of sectors, like healthcare, e-commerce and finance.

4. Cloud Computing

Many firms are turning to cloud computing to simplify their IT infrastructure. It's been proven as a reliable way of keeping up with the latest technology trends. The data analytics course at Imarticus Learning, for example, can help you get ahead in this field.

5. Deep Learning

Deep learning is being used for a wide range of tasks, such as speech recognition, natural language processing, robotics and more. It can help us advance our careers by assisting data scientists in their work

6. Tableau

Tableau is used by businesses worldwide to visualize and analyze data. A huge benefit of Tableau is being able to view the data in easy-to-grasp dashboards. Tableau can connect to many data sources, which gives data scientists a lot of options. To learn more about Tableau read 'Imarticus Learning's Pro-Degree Program in Data Science'.

7. Google Colab

Google Colab is a browser-based platform that enables users to run Python code. The Data Analytics course offered by Imarticus Learning can help you understand the benefits of using Google Colab. The PG Program in Analytics & AI educates students about Google Colab and its position in the business.

8. Statistics

Statistical skills are very important when it comes to data sorting, sampling, and analysis. An understanding of the principals involved in these processes will allow you to develop an effective machine learning algorithm that can extract valuable insights from unstructured data sets.Data scientists are required to carry out statistical analysis on their dataset to check for patterns - Imarticus offers the best resource for learning about this topic.

9. Data Visualization

It is not possible for data scientists to communicate their findings with words alone. Visuals are essential for people to understand the information you are trying to communicate. The best data scientists will have expert skills in data visualisation, which allow them to provide the information in a way that everyone can understand and take action quickly.

10. Artificial Intelligence (AI)

Adding artificial intelligence can help you automate analysis & forecast accuracy. Data scientists are using AI to generate real-time insights from large datasets - and it's the most in-demand skill right now! I hope you will like the content and it will help you to learn the TOP 10 IN-DEMAND DATA ANALYTICS SKILLS TO LEARN IN 2022. If you like this content, do share it. Read the full article

[aiandemily]

コード生成AIツール完全ガイド！おすすめ5選と活用のコツを徹底解説

[mauimauricio]

Dandelion #stablediffusion #googlecolab #deforum #AIart #machinelearning #ai #artificialintelligence #digitalart #aiartwork #aiartcommunity https://www.instagram.com/p/CijPpQmPpyn/?igshid=NGJjMDIxMWI=

[generatedart]

Art by @geodesic.doom Alternate world landscapes . . . #promptism #discodiffusion #neuralart #googlecolab #aiartcommunity #aiart #artificialintelligence ... #fantasyworld #epiclandscape #digitallandscape #alienlandscape #anotherworld #spacelovers #alternatereality #environmentalart #planetb #wanderlust #landscapelovers #mountainlandscape #surreallandscape #extraterrestrial #spaceexploration #spacetravel ... #conceptart #scifiart #digitalartist #artistoninstagram #everydayart #psychedelicart #curatedartist https://www.instagram.com/p/CcvbrgNr0_k/?igshid=NGJjMDIxMWI=