For Anyone interested in VLM check this blog it will help you understand the implications simply
https://towardsdatascience.com/how-to-apply-vision-language-models-to-long-documents/
https://towardsdatascience.com/how-to-apply-vision-language-models-to-long-documents/
Towards Data Science
How to Apply Vision Language Models to Long Documents | Towards Data Science
Learn how to apply powerful VLMs for long context document understanding tasks
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𝕋𝕙𝕚𝕤 𝕨𝕖𝕖𝕜 𝕛𝕦𝕤𝕥 𝕜𝕖𝕖𝕡𝕤 𝕘𝕖𝕥𝕥𝕚𝕟𝕘 𝕓𝕖𝕥𝕥𝕖𝕣🔥
365 Data Science has unlocked its entire AI & Data learning platform 100% free for 15 days (Nov 6–21).
I took Data Strategy, Data Visualization, and Web Scraping courses last year and they turned out to be some of the most practical ones I’ve ever done.
I was able to apply those skills directly at work, strengthen my analyses, and genuinely level up my career. If you’ve been planning to dive deeper into data or AI, this is your week.
Now everyone can access:
• 115+ AI and Data Science courses
• CPE-accredited certificates
• Real-world projects and practical case studies
• Trusted by 2M+ learners globally
No credit card. No catch. Just learning.
👉 https://365datascience.com/free-weeks-2025/
Credit: Miklol
365 Data Science has unlocked its entire AI & Data learning platform 100% free for 15 days (Nov 6–21).
I took Data Strategy, Data Visualization, and Web Scraping courses last year and they turned out to be some of the most practical ones I’ve ever done.
I was able to apply those skills directly at work, strengthen my analyses, and genuinely level up my career. If you’ve been planning to dive deeper into data or AI, this is your week.
Now everyone can access:
• 115+ AI and Data Science courses
• CPE-accredited certificates
• Real-world projects and practical case studies
• Trusted by 2M+ learners globally
No credit card. No catch. Just learning.
👉 https://365datascience.com/free-weeks-2025/
Credit: Miklol
365 Data Science
Start Learning Data Science for Free – 365 Data Science
Kickstart your data science journey with free lessons, hands-on exercises, and real-world data projects. Sign up now--no credit card required, no time limit.
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For Those who might not know it, 365 Data Science is one of the best Places to learn Data Science and Data Analysis in details! Use this advantage well!
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The thing about Financial Services in Ethiopia is that Consistency is key...
Was about to pay using Santim pay at a Cafe and the Waiters just told me that it won't work... bec customers has experienced system errors and lags....
Now just bec of few bad exp people won't even try... 🤷♂
Anybody tried to pay using Santim Pay recently...what was your experience...
Was about to pay using Santim pay at a Cafe and the Waiters just told me that it won't work... bec customers has experienced system errors and lags....
Now just bec of few bad exp people won't even try... 🤷♂
Anybody tried to pay using Santim Pay recently...what was your experience...
❤3
Forwarded from Frectonz
Devtopia is back.
We talked about what we were up to for the last 6 months. We are planning to do one episode per two weeks.
We also have an editor now, Bisrat, so hopefully this will help us maintain our release schedule.
[Devtopia Interlude E04]
We talked about what we were up to for the last 6 months. We are planning to do one episode per two weeks.
We also have an editor now, Bisrat, so hopefully this will help us maintain our release schedule.
[Devtopia Interlude E04]
YouTube
Devtopia Interlude E04 - We are back!!!
Fraol and Yafet discuss what they have been up to for the last 6 months.
Edited by Bisrat - https://www.instagram.com/bashagre07/
00:00 - Intro
01:55 - The Last 6 months
03:40 - Fayda Hackathon
10:56 - Better Hack
20:07 - Fraol’s Jobs
30:50 - pg-when
41:03…
Edited by Bisrat - https://www.instagram.com/bashagre07/
00:00 - Intro
01:55 - The Last 6 months
03:40 - Fayda Hackathon
10:56 - Better Hack
20:07 - Fraol’s Jobs
30:50 - pg-when
41:03…
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Supernovae
It is okay to be seen trying
The problem we have with our Generation is that, we are afraid to be seen trying... most people don't even try out of ego... but they actually really broke...
Thats not even logical eko!
This is our time to Grind and risk more💯
Thats not even logical eko!
This is our time to Grind and risk more
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So this is why they started acting up weird 😂
https://shega.co/news/commercial-bank-of-ethiopia-unveils-digital-loans-up-to-150000-birr
https://shega.co/news/commercial-bank-of-ethiopia-unveils-digital-loans-up-to-150000-birr
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Shega
Commercial Bank of Ethiopia Unveils Digital Loans up to 150,000 Birr
As Telebirr intensifies loan recovery efforts, CBE is moving in a different direction, building its own in-house digital credit system through CBE Birr. The bank unveiled CBE Beje, a new digital loan feature within its CBE Birr mobile app, allowing customers…
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I think we are done for today...
All tests passed ✅️
All tests passed ✅️
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I am not really a fan of Cursor, but they definitely got the Marketing team 👏
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✅ 25 AI & Machine Learning Abbreviations You Should Know 🤖🧠
1️⃣ AI – Artificial Intelligence: The big umbrella for machines mimicking human smarts, from chatbots to self-driving cars.
2️⃣ ML – Machine Learning: AI subset where models learn from data without explicit programming—think predictive analytics.
3️⃣ DL – Deep Learning: ML using multi-layered neural nets for complex tasks like image recognition.
4️⃣ NLP – Natural Language Processing: Handling human language for chatbots or sentiment analysis.
5️⃣ CV – Computer Vision: AI that "sees" and interprets visuals, powering facial recognition.
6️⃣ ANN – Artificial Neural Network: Brain-inspired structures for pattern detection in data.
7️⃣ CNN – Convolutional Neural Network: DL for images/videos, excels at feature extraction like edges in photos.
8️⃣ RNN – Recurrent Neural Network: Handles sequences like time series or text, remembering past inputs.
9️⃣ GAN – Generative Adversarial Network: Two nets competing to create realistic data, like fake images.
🔟 RL – Reinforcement Learning: Agents learn via rewards/punishments, used in games like AlphaGo.
1️⃣1️⃣ SVM – Support Vector Machine: Classification algo drawing hyperplanes to separate data classes.
1️⃣2️⃣ KNN – K-Nearest Neighbors: Simple ML for grouping based on closest data points—lazy learner!
1️⃣3️⃣ PCA – Principal Component Analysis: Dimensionality reduction to simplify datasets without losing info.
1️⃣4️⃣ API – Application Programming Interface: Bridges software, like calling OpenAI's models in your app.
1️⃣5️⃣ GPU – Graphics Processing Unit: Hardware accelerating parallel computations for training big models.
1️⃣6️⃣ TPU – Tensor Processing Unit: Google's custom chips optimized for tensor ops in DL.
1️⃣7️⃣ IoT – Internet of Things: Networked devices collecting data, feeding into AI for smart homes.
1️⃣8️⃣ BERT – Bidirectional Encoder Representations from Transformers: Google's NLP model understanding context both ways.
1️⃣9️⃣ LSTM – Long Short-Term Memory: RNN variant fixing vanishing gradients for long sequences.
2️⃣0️⃣ ASR – Automatic Speech Recognition: Converts voice to text, like Siri or trannoscription tools.
2️⃣1️⃣ OCR – Optical Character Recognition: Extracts text from images, e.g., scanning docs.
2️⃣2️⃣ Q-Learning – Q-Learning: A model-free RL algorithm estimating action values for optimal decisions.
2️⃣3️⃣ MLP – Multilayer Perceptron: Feedforward ANN with hidden layers for non-linear problems.
2️⃣4️⃣ LLM – Large Language Model: Massive text-trained nets like GPT for generating human-like responses (swapped the repeat API for this essential one!).
2️⃣5️⃣ TF-IDF – Term Frequency-Inverse Document Frequency: Scores word importance in text docs for search/retrieval.
@datawithsimon
1️⃣ AI – Artificial Intelligence: The big umbrella for machines mimicking human smarts, from chatbots to self-driving cars.
2️⃣ ML – Machine Learning: AI subset where models learn from data without explicit programming—think predictive analytics.
3️⃣ DL – Deep Learning: ML using multi-layered neural nets for complex tasks like image recognition.
4️⃣ NLP – Natural Language Processing: Handling human language for chatbots or sentiment analysis.
5️⃣ CV – Computer Vision: AI that "sees" and interprets visuals, powering facial recognition.
6️⃣ ANN – Artificial Neural Network: Brain-inspired structures for pattern detection in data.
7️⃣ CNN – Convolutional Neural Network: DL for images/videos, excels at feature extraction like edges in photos.
8️⃣ RNN – Recurrent Neural Network: Handles sequences like time series or text, remembering past inputs.
9️⃣ GAN – Generative Adversarial Network: Two nets competing to create realistic data, like fake images.
🔟 RL – Reinforcement Learning: Agents learn via rewards/punishments, used in games like AlphaGo.
1️⃣1️⃣ SVM – Support Vector Machine: Classification algo drawing hyperplanes to separate data classes.
1️⃣2️⃣ KNN – K-Nearest Neighbors: Simple ML for grouping based on closest data points—lazy learner!
1️⃣3️⃣ PCA – Principal Component Analysis: Dimensionality reduction to simplify datasets without losing info.
1️⃣4️⃣ API – Application Programming Interface: Bridges software, like calling OpenAI's models in your app.
1️⃣5️⃣ GPU – Graphics Processing Unit: Hardware accelerating parallel computations for training big models.
1️⃣6️⃣ TPU – Tensor Processing Unit: Google's custom chips optimized for tensor ops in DL.
1️⃣7️⃣ IoT – Internet of Things: Networked devices collecting data, feeding into AI for smart homes.
1️⃣8️⃣ BERT – Bidirectional Encoder Representations from Transformers: Google's NLP model understanding context both ways.
1️⃣9️⃣ LSTM – Long Short-Term Memory: RNN variant fixing vanishing gradients for long sequences.
2️⃣0️⃣ ASR – Automatic Speech Recognition: Converts voice to text, like Siri or trannoscription tools.
2️⃣1️⃣ OCR – Optical Character Recognition: Extracts text from images, e.g., scanning docs.
2️⃣2️⃣ Q-Learning – Q-Learning: A model-free RL algorithm estimating action values for optimal decisions.
2️⃣3️⃣ MLP – Multilayer Perceptron: Feedforward ANN with hidden layers for non-linear problems.
2️⃣4️⃣ LLM – Large Language Model: Massive text-trained nets like GPT for generating human-like responses (swapped the repeat API for this essential one!).
2️⃣5️⃣ TF-IDF – Term Frequency-Inverse Document Frequency: Scores word importance in text docs for search/retrieval.
@datawithsimon
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Fun to watch but, really good POV mentioned as well!
Hope you guys can learn something from it.
Hope you guys can learn something from it.
Forwarded from Henok | Neural Nets
Google Colab is Coming to VS Code, this is very nice.
https://developers.googleblog.com/en/google-colab-is-coming-to-vs-code/
https://developers.googleblog.com/en/google-colab-is-coming-to-vs-code/
Googleblog
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Connect your VS Code notebooks to Colab's powerful runtimes with the new Google Colab extension, bringing the best of both platforms together.
This is really Nice, All the benefits of Colab in VSCode is seriously dope 👌
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