Discover and read the best of Twitter Threads about #DeepLearning
Most recents (24)
✅Linear Neural Networks for Regression and Classification explained in simple terms and how to use it (with code).
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : Joshua
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : Joshua
1/ Imagine you have a box with a lot of buttons on it, and each button can give you a different answer. You also have a big list of questions that you want to ask, like "What's the weather like today?" or "Is this a cat or a dog?".
2/ Now, instead of asking just one button at a time, you can connect all the buttons together with wires. When you ask a question, it goes through the wires and each button can help decide the answer a little bit.
✅Time Series Forecasting explained in simple terms and how to use it ( with code).
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
1/ Imagine you have a special notebook where you write down the temperature outside every day. You write down the temperature in the morning and also in the afternoon. Now, after a few months, you have a lot of temperature numbers in your notebook.
2/ Time series forecasting is like using magic to predict what the temperature might be in the future. You look at all the numbers you wrote down and try to find a pattern or a trend.
✅Generative Adversarial Networks ( GANs) explained in simple terms and how to use it ( with code).
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
A quick thread 🧵👇🏻
#Python #DataScience #MachineLearning #DataScientist #Programming #Coding #100DaysofCode #hubofml #deeplearning
Pic credits : ResearchGate
1/ Imagine you have two friends, let's call them the "artist" and the "critic." The artist wants to draw something cool, and the critic wants to judge if the drawing is good or not. The artist tries to draw something, and the critic looks at it and says whether it's good or bad.
2/ Now, the artist really wants to improve, so they keep drawing and the critic keeps judging. Over time, the artist gets better and better at drawing because they learn from the critic's feedback. The artist wants to make drawings that the critic will say are really amazing!
📢 Exciting developments from @GoogleAI ! Introducing Imagen Editor & EditBench, revolutionary tools that advance and evaluate text-guided image inpainting. 🎨🖌️ #AI #MachineLearning
Imagen Editor is a diffusion-based model, fine-tuned on Imagen, with improved representations of linguistic inputs, fine-grained control, and high-fidelity outputs. 📸
It uses three user inputs: the image, a binary mask, and a text prompt. #AI
It uses three user inputs: the image, a binary mask, and a text prompt. #AI
The system relies on 3 core techniques: an object detector masking policy, high resolution editing through conditioning on full resolution, and classifier-free guidance (CFG) to bias samples to text prompts.
#AI #DeepLearning
#AI #DeepLearning
La fascinante evolución de la inteligencia artificial (IA) 🌐🤖
Desde los albores de la humanidad hasta hoy, la inteligencia artificial (IA) ha recorrido un camino asombroso. ¡Únete a este hilo y descubre su evolución!
La IA ha pasado de ser un concepto de ciencia ficción a una realidad palpable. Conoce cómo ha evolucionado a lo largo de los años y cómo está transformando nuestro mundo.
Last week, we discussed techniques to speed up the training speed of large language models🔥💨
How about saving memory during inference? 🧠💾 Check out int8 & int4 quantization, which is supported in Lit-LLama 👉github.com/Lightning-AI/l…
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#LLMs #ML #DeepLearning
How about saving memory during inference? 🧠💾 Check out int8 & int4 quantization, which is supported in Lit-LLama 👉github.com/Lightning-AI/l…
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#LLMs #ML #DeepLearning
How does int8 quantization work? 🤔
It's a 2-part procedure with
1) using 8bit quantization
2) 16-bit matmuls for outlier feature dimensions
Check out the LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale paper for details arxiv.org/abs/2208.07339
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It's a 2-part procedure with
1) using 8bit quantization
2) 16-bit matmuls for outlier feature dimensions
Check out the LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale paper for details arxiv.org/abs/2208.07339
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And how about int4? 🤔
It's a one-shot weight quantization method based on approximate second-order information⚙️📉
For more details, see GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers: 📚🔍 arxiv.org/abs/2210.17323
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It's a one-shot weight quantization method based on approximate second-order information⚙️📉
For more details, see GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers: 📚🔍 arxiv.org/abs/2210.17323
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Does the concept of cortical columns extend to higher-level primate cortex?
Using #DeepLearning & physiology, we found that V4 neurons cluster in columns & form functional groups biorxiv.org/content/10.110…
Led by @KonstantinWille @kelli_restivo w/ @sinzlab @kfrankelab @alxecker 🧵
Using #DeepLearning & physiology, we found that V4 neurons cluster in columns & form functional groups biorxiv.org/content/10.110…
Led by @KonstantinWille @kelli_restivo w/ @sinzlab @kfrankelab @alxecker 🧵
Elucidating the brain’s structure-function organization is key to understanding perception. A classic example are cortical columns, a vertical organization of neurons w/ similar function. They exist in primary sensory areas but it’s unclear if they are present throughout cortex
Here, we asked whether area V4, a mid-level area of the macaque visual system, is organized into functional columns. We presented grayscale natural images & recorded responses of >1200 single V4 neurons in 100 electrophysiological recording sessions using 32-channel depth probes
Want to make the most out of @midjourney and other image diffusion models w/ us & the AI gurus at @ohnahji? 💡Here's a pro tip: drill down your prompt to use the tool quickly and efficiently! ⚡️ Get the results you need in no time #imageprocessing #deeplearning #productivityhack
One key way to boost your image diffusion model output is with demonyms in your prompts! What… is THAT? 🤣No, Demonyms have nothing to do with demons lol, they describe inhabitants of a place and help create more specific prompts for better results. #demonyms #promptingtips
In this thread we'll focus on West African country demonyms and show you how to use them in your image prompts for better results. Here are 10:
Nigerian 🇳🇬
Ghanaian 🇬🇭
Senegalese 🇸🇳
Ivorian 🇨🇮
Malian 🇲🇱
Beninese 🇧🇯
Guinean 🇬🇳
Liberian 🇱🇷
Sierra Leonean 🇸🇱
Togolese 🇹🇬
#WestAfrica
Nigerian 🇳🇬
Ghanaian 🇬🇭
Senegalese 🇸🇳
Ivorian 🇨🇮
Malian 🇲🇱
Beninese 🇧🇯
Guinean 🇬🇳
Liberian 🇱🇷
Sierra Leonean 🇸🇱
Togolese 🇹🇬
#WestAfrica
1/ 🧠🌌 Embarking on a journey to explore AI, consciousness, and the cosmos, we'll dive into research and knowledge shaping our understanding of the universe. Are you ready for the fascinating world of AI, consciousness, and cosmic connections? #AI #Consciousness #Cosmology
2/ 🎇🔬 From Einstein's theory of relativity to quantum mechanics discoveries, our understanding of the universe has evolved significantly. These advancements set the stage for exploring AI and consciousness. #Einstein #QuantumMechanics #Physics
3/ 🧬🤖 AI research has progressed since Turing's days. Today, we're making breakthroughs in deep learning, neural networks, and reinforcement learning, pushing the boundaries of AI and consciousness. #DeepLearning #NeuralNetworks #ReinforcementLearning
🧵1/9 A deep dive into the history of #Backpropagation: A key technique in training multilayer architectures for neural networks. This powerful method revolutionized the way we train AI systems, leading to major breakthroughs in various domains. 🤖#DataScience #DeepLearning #AI
🧵2/9 #Backpropagation is based on a simple concept: use gradient descent to optimize multilayer networks. By applying the chain rule for derivatives, it computes gradients efficiently, leading to optimized weight configurations in each layer of the network. #DataScience #AI
🧵3/9 The shift to Rectified Linear Units (ReLU) accelerated learning in deep networks, allowing training without unsupervised pre-training. This non-linear activation function proved more effective than its smoother predecessors like tanh(z) or 1/(1+exp(−z)). #ReLU #DataScience
🚀 1/ Excited to share our (with Aydar Bulatov and @yurakuratov ) report on scaling Recurrent Memory Transformer to 2M (yes, two millions)😮 tokens! 🧠🌐 #AI #NLP #DeepLearning
2/ 📈 We've tackled the quadratic complexity of attention in #Transformers by combining token-based memory & segment-level recurrence, using RMT.
🔸 RMT adapts to any Transformer family model
🔸 Memory tokens provide the recurrent connection 🎛️💡 #AI #NLP #DeepLearning
🔸 RMT adapts to any Transformer family model
🔸 Memory tokens provide the recurrent connection 🎛️💡 #AI #NLP #DeepLearning
3/ 🧠 We tested RMT's memorization capabilities with synthetic datasets requiring fact memorization, detection, & reasoning. The model must separate facts from irrelevant text and use them to answer questions in a 6-class classification. 🎯 #AI #NLP #DeepLearning
Introducing segment-anything-py, an official #Python package for Meta AI's Segment Anything Model. Install it with one command line: 'pip install segment-anything-py' .
PyPI: pypi.org/project/segmen…
GitHub: github.com/opengeos/segme…
#AI #deeplearning #machinelearning
PyPI: pypi.org/project/segmen…
GitHub: github.com/opengeos/segme…
#AI #deeplearning #machinelearning
It will soon be available on conda-forge
github.com/conda-forge/st…
github.com/conda-forge/st…
Try it out
colab.research.google.com/github/opengeo…
colab.research.google.com/github/opengeo…
1/ AI is already impacting the financial industry in a big way, and the same goes for hedge funds. It has the potential to provide insights that are not possible with human analysis, making it an essential tool for the hedge fund industry.
A Thread 🧵
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A Thread 🧵
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2/ Hedge funds can use AI to identify patterns and make predictions that were not possible before. This is important in a market where every second counts, and a slight edge can make all the difference. #DataScience #MachineLearning #HedgeFunds @KirkDBorne
@KirkDBorne 3/ One of the ways Photons Hedge is leveraging AI is by using natural language processing to analyze news and social media sentiment. This helps the fund to understand how the market is reacting to news and events, enabling them to make better-informed trades. #SentimentAnalysis
Forget ChatGPT, here are some new AI tools that will blow your mind! In this thread, we'll explore some of the best AI tools available and examine how you can put them to work for you.
1. @playground_ai - allows creators to generate new and unique images using AI technology. With PlaygroundAI, you can import your own images and use them as prompts to create custom images that perfectly match the style and theme of your content.
2. @MixoTeam & @Durableteam - AI-powered platforms that allow you to create a website by simply providing basic information about your business or brand, such as the name and type of business.
Yapay zeka asistan mı yoksa akıl hocası mı? Belki de her ikisi de! Günlük hayatımızdan iş hayatına, eğitimimize kadar birçok alanda yapay zeka uygulamalarını kullanabiliriz. #YapayZeka #AkılHocası #İşHayatı #Eğitim #Teknoloji #Gelecek #Yenilikçilik
#ChatGPT
#ChatGPT
chat.openai.com/chat ChatGPT orjinal sitesi. GPT-3.5 versiyonu ücretsiz 2021 yılına kadar verdi işlemektedir. GPT-4 yeni versiyonudur ve güncel veriyi içermektedir, ücretlidir. #ChatGPT #chatgpt4
chatspot.ai ücretsiz, yazı, konuşma ve resim çizdirme sitesi #chatspot
SQL Interviews Question for Product companies
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create table entries (
name varchar(20),
address varchar(20),
email varchar(20),
floor int,
resources varchar(10));
#SQL #DataScience #DataAnalytics #MachineLearning #Deeplearning
name varchar(20),
address varchar(20),
email varchar(20),
floor int,
resources varchar(10));
#SQL #DataScience #DataAnalytics #MachineLearning #Deeplearning
" SQL Interview problem "
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This is a REAL SQL Interview question that might seem impossible to solve just by using SQL at first. But during the video the problem by breaking it into multiple parts
#SQL #DataScience #DataCleaningchallenge #DataAnalytics #dataviz #MachineLearning #deeplearning
#SQL #DataScience #DataCleaningchallenge #DataAnalytics #dataviz #MachineLearning #deeplearning
Youtube Video Link -
" SQL Puzzle Interview Question "
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Table script:
create table input (
id int,
formula varchar(10),
value int
)
insert into input values (1,'1+4',10),(2,'2+1',5),(3,'3-2',40),(4,'4-1',20);
create table input (
id int,
formula varchar(10),
value int
)
insert into input values (1,'1+4',10),(2,'2+1',5),(3,'3-2',40),(4,'4-1',20);
🧩Three intriguing concepts in the world of #DeepLearning: #BackwardCompatibility, #StationaryRepresentation, and #NeuralCollapse. Their connection may hold the key to better deep learning models with possibly novel ways to use them
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🤝Backward Compatibility enables matching internal feature representations from different neural networks. Stationary Representation maintains feature spatial configurations fixed during learning. How does Neural Collapse fit in?
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💥Neural Collapse is when feature representations converge towards a simpler structure during training. This fascinating phenomenon is strongly related to compatibility and stationary representation.
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The model will not perform well if training data is small, or noisy with errors outlier or if #data is not representative consist of irrelevant feature(garbage in garbage out) lastly neither too simple(result in #underfitting) nor too complex(results in #overfitting
#DataScience
#DataScience
Not enough training data.
Poor Quality of data.
Irrelevant features.
Nonrepresentative training data.
Overfitting and Underfitting.
#DataCleaningchallenge #MachineLearning #Deeplearning #DataAnalytics #DataVisualization #Python #Powerbi #SQL #MYSQL
analyticsvidhya.com/blog/2021/06/5…
Poor Quality of data.
Irrelevant features.
Nonrepresentative training data.
Overfitting and Underfitting.
#DataCleaningchallenge #MachineLearning #Deeplearning #DataAnalytics #DataVisualization #Python #Powerbi #SQL #MYSQL
analyticsvidhya.com/blog/2021/06/5…
SQL JOINS for beginners { Must Read }
Joins are the most fundamental concepts in SQL and having a clear understanding of Joins is very essential to write good SQL queries.
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Joins are the most fundamental concepts in SQL and having a clear understanding of Joins is very essential to write good SQL queries.
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Topics -
✅What are JOINS in SQL
✅Inner Joins in SQL
✅Left Join in SQL
✅Right Join in SQL
✅What are JOINS in SQL
✅Inner Joins in SQL
✅Left Join in SQL
✅Right Join in SQL
✅FULL OUTER JOIN in SQL
✅ CROSS JOIN in SQL
✅ NATURAL JOIN in SQL
✅ SELF JOIN in SQL
✅ CROSS JOIN in SQL
✅ NATURAL JOIN in SQL
✅ SELF JOIN in SQL
Roadmap to becoming Data Analyst in three months absolutely free. No need to pay a penny for this.
I have mentioned a roadmap with free resources.
A thread🧵👇
I have mentioned a roadmap with free resources.
A thread🧵👇
1. First Month Foundations of Data Analysis
A. Corey Schafer - Python Tutorials for Beginners:
B. StatQuest with Josh Starmer - Statistics Fundamentals:
C. Ken Jee - Data Analysis with Python
A. Corey Schafer - Python Tutorials for Beginners:
B. StatQuest with Josh Starmer - Statistics Fundamentals:
C. Ken Jee - Data Analysis with Python
2. Second Month - Advanced Data Analysis Techniques
A. Sentdex - Machine Learning with Python
B. StatQuest with Josh Starmer - Machine Learning Fundamentals
C. Brandon Foltz - Business Analytics
A. Sentdex - Machine Learning with Python
B. StatQuest with Josh Starmer - Machine Learning Fundamentals
C. Brandon Foltz - Business Analytics
Python project ideas for beginners with source code
A thread 🧵👇
A thread 🧵👇
Python for data science beginners roadmap
A thread 🧵👇
A thread 🧵👇
1. Python Basics
Codecademy's Python Course (codecademy.com/learn/learn-py…)
Python for Everybody Course (py4e.com)
Codecademy's Python Course (codecademy.com/learn/learn-py…)
Python for Everybody Course (py4e.com)
2. Data Analysis Libraries
NumPy User Guide (numpy.org/doc/stable/use…)
Pandas User Guide (pandas.pydata.org/docs/user_guid…)
Matplotlib Tutorials (matplotlib.org/stable/tutoria…)
NumPy User Guide (numpy.org/doc/stable/use…)
Pandas User Guide (pandas.pydata.org/docs/user_guid…)
Matplotlib Tutorials (matplotlib.org/stable/tutoria…)
