TEN mind-blowing AI websites that youβve probably never heard of:
π Magic Eraser. Remove unwanted things from images in seconds. Upload an image and mark the bit you want removed. Download your improved image.
magiceraser.io
π Autoenhance. AI photo editor that enhances your workflow. Instantly get brighter, colourful and evenly lit images. Sky replacement and perspective correction to transform photos.
autoenhance.ai
π Writesonic. AI writer that helps you write long-form blog posts & articles. Break free from writers block and scale your content production. Take content creation to the next level.
writesonic.com
π Your own writing assistant and personal collaborator. Powered by AI to write better & faster. Create viral hooks and headlines in seconds.
tribescaler.com
π Rytr - An AI writing assistant to help you write 10x faster.Enter your post topic, select the variants and creativity level. Choose the variants that are best suited to your style.
rytr.me
π Namelix. Generate a short, catchy business name using AI. Decide using key words or domain extensions. Short names are unique, memorable and affordable.
namelix.com
π Replika. Your AI companion who cares. There to listen and talk, always on your side.Ready to chat when you need an empathetic friend.
replika.com
π Your AI assistant for meetings. Instantly record meetings across any web-conferencing platform.Transcribe, and search across your voice conversations.
fireflies.ai
π Excel Formula Bot. Stop wasting hours creating Excel formulas. Turn your spreadsheet problem into a formula in seconds. Experience the full power of Excel & Google Sheets AI.
excelformulabot.com
π Talk To Books. Get quotes from books that respond to your question. A creativity tool by Google to explore new ideas. Explore an index of > 100,000 books.
books.google.com/talktobooks/
π Magic Eraser. Remove unwanted things from images in seconds. Upload an image and mark the bit you want removed. Download your improved image.
magiceraser.io
π Autoenhance. AI photo editor that enhances your workflow. Instantly get brighter, colourful and evenly lit images. Sky replacement and perspective correction to transform photos.
autoenhance.ai
π Writesonic. AI writer that helps you write long-form blog posts & articles. Break free from writers block and scale your content production. Take content creation to the next level.
writesonic.com
π Your own writing assistant and personal collaborator. Powered by AI to write better & faster. Create viral hooks and headlines in seconds.
tribescaler.com
π Rytr - An AI writing assistant to help you write 10x faster.Enter your post topic, select the variants and creativity level. Choose the variants that are best suited to your style.
rytr.me
π Namelix. Generate a short, catchy business name using AI. Decide using key words or domain extensions. Short names are unique, memorable and affordable.
namelix.com
π Replika. Your AI companion who cares. There to listen and talk, always on your side.Ready to chat when you need an empathetic friend.
replika.com
π Your AI assistant for meetings. Instantly record meetings across any web-conferencing platform.Transcribe, and search across your voice conversations.
fireflies.ai
π Excel Formula Bot. Stop wasting hours creating Excel formulas. Turn your spreadsheet problem into a formula in seconds. Experience the full power of Excel & Google Sheets AI.
excelformulabot.com
π Talk To Books. Get quotes from books that respond to your question. A creativity tool by Google to explore new ideas. Explore an index of > 100,000 books.
books.google.com/talktobooks/
Google
Talk to Books
Talk to Books was a new way to explore ideas and discover books. Select one of
the samples to view its archived search results.
the samples to view its archived search results.
βββββπ€·π€·Tensor Parallelism
There are >= 3 1/2 paradigms for training deep neural nets on multiple GPUs:
1) Data parallelism
2) Model parallelism
3) Pipeline parallelism
( 4) Tensor parallelism)
Which one(s) are you usually using; and anything missing?
1) Data parallelism: splits batches & distributes those across several GPUs. Here, in each iteration, the gradient estimate (for the model update) is computed from as a weighted avg over sub-batches.
Eg via DataParallel or DistributedDataParallel (recommended) in PyTorch
2) Model parallelism: divide model onto separate GPUs; usually to deal with limited VRAM. Note it doesn't imply that the training happens in parallel!
How? L1. to('cuda:0'), L2. to('cuda:1') etc.
Tutorial: pytorch.org/tutorials/inteβ¦
3) Pipeline parallelism: related to model parallelism where you put different blocks of the model onto different GPUs(, and data parallelism where you split batches). But here you make those blocks run (somewhat) in parallel.
4) Tensor parallelism. It's basically a flavor of model parallelism, but instead of dividing the neural network by layer (horizontally), you divide the tensors themselves (vertically). E.g., put half of a weight layer on one GPU, and the other onto another GPU
βπͺ‘excellent info βΉοΈββββ
There are >= 3 1/2 paradigms for training deep neural nets on multiple GPUs:
1) Data parallelism
2) Model parallelism
3) Pipeline parallelism
( 4) Tensor parallelism)
Which one(s) are you usually using; and anything missing?
1) Data parallelism: splits batches & distributes those across several GPUs. Here, in each iteration, the gradient estimate (for the model update) is computed from as a weighted avg over sub-batches.
Eg via DataParallel or DistributedDataParallel (recommended) in PyTorch
2) Model parallelism: divide model onto separate GPUs; usually to deal with limited VRAM. Note it doesn't imply that the training happens in parallel!
How? L1. to('cuda:0'), L2. to('cuda:1') etc.
Tutorial: pytorch.org/tutorials/inteβ¦
3) Pipeline parallelism: related to model parallelism where you put different blocks of the model onto different GPUs(, and data parallelism where you split batches). But here you make those blocks run (somewhat) in parallel.
4) Tensor parallelism. It's basically a flavor of model parallelism, but instead of dividing the neural network by layer (horizontally), you divide the tensors themselves (vertically). E.g., put half of a weight layer on one GPU, and the other onto another GPU
βπͺ‘excellent info βΉοΈββββ
BoonDock Tip : { Section - Python}
Do not initialise the empty String with quotes. Try to use 'None' in your projects. It is the best practices though.
sqlConnection=ββ ββ- Not good practice
sqlConnection=None β- Best practice
Do not initialise the empty String with quotes. Try to use 'None' in your projects. It is the best practices though.
sqlConnection=ββ ββ- Not good practice
sqlConnection=None β- Best practice
15 Websites To Follow As A Developer
1. Stackoverflow
2. Google
3. YouTube
4. DevDocs. io
5. Github
6. Freecodecamp
7. LeetCode
8. IndieHackers
9. Udemy
10. Hashnode
11. Medium
12. Dev. to
13. W3Schools
14. Codecademy
15. Hacker News
May be you have another list ππ
1. Stackoverflow
2. Google
3. YouTube
4. DevDocs. io
5. Github
6. Freecodecamp
7. LeetCode
8. IndieHackers
9. Udemy
10. Hashnode
11. Medium
12. Dev. to
13. W3Schools
14. Codecademy
15. Hacker News
May be you have another list ππ
πΈπ¬ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
β’ High-performance Deep Learning models for Text2Speech tasks.
β¦ Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).
β¦ Speaker Encoder to compute speaker embeddings efficiently.
β¦ Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN)
β’ Fast and efficient model training.
β’ Detailed training logs on the terminal and Tensorboard.
β’ Support for Multi-speaker TTS.
β’ High-performance Deep Learning models for Text2Speech tasks.
β¦ Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).
β¦ Speaker Encoder to compute speaker embeddings efficiently.
β¦ Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN)
β’ Fast and efficient model training.
β’ Detailed training logs on the terminal and Tensorboard.
β’ Support for Multi-speaker TTS.
https://github.com/TheAlgorithms/Python/blob/master/DIRECTORY.md
One place where you can find hundreds of algorithms.
Even you can find them in another languages as well.
https://github.com/TheAlgorithms
One place where you can find hundreds of algorithms.
Even you can find them in another languages as well.
https://github.com/TheAlgorithms
GitHub
Python/DIRECTORY.md at master Β· TheAlgorithms/Python
All Algorithms implemented in Python. Contribute to TheAlgorithms/Python development by creating an account on GitHub.
Meet LAVIS - a one-stop library for language-and-vision research and applications!
π₯Github: https://github.com/salesforce/LAVIS
πTech Report: arxiv.org/abs/2209.09019
LAVIS features
- Unified and modular interface to access 10+ tasks, 20+ datasets, 30+ pre-trained models!
π₯Github: https://github.com/salesforce/LAVIS
πTech Report: arxiv.org/abs/2209.09019
LAVIS features
- Unified and modular interface to access 10+ tasks, 20+ datasets, 30+ pre-trained models!
π¦π¦All tools Data Engineers need! Categorized into cloud native (only available on that platform) and cloud agnostic (use anywhere) platforms & tools on the top. On the left you find categories and subcategories for the tools.
ππThe goal for every engineer is to at least have knowledge of one tool in every category (row).
ππAs example:
- If you are on Azure then learn when and how to use for at least one of the tools in every row of Azure
- Or go fully cloud agnostic and open source. It's your choice.
- You can also combine cloud agnostic with cloud platforms together by replacing the cloud native tools of one row with a cloud agnostic one.
π€·ββοΈ thatβs it man π¨!!
ππThe goal for every engineer is to at least have knowledge of one tool in every category (row).
ππAs example:
- If you are on Azure then learn when and how to use for at least one of the tools in every row of Azure
- Or go fully cloud agnostic and open source. It's your choice.
- You can also combine cloud agnostic with cloud platforms together by replacing the cloud native tools of one row with a cloud agnostic one.
π€·ββοΈ thatβs it man π¨!!
π¦π¦βοΈβοΈ
OpenAI trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.
Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web.
https://openai.com/blog/whisper/
Check out above link for paper, code and more details
π½π½
OpenAI trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.
Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web.
https://openai.com/blog/whisper/
Check out above link for paper, code and more details
π½π½
βοΈβοΈWhat is πππ₯π’ππ«πππ’π¨π§ π’π§ ππππ‘π’π§π ππππ«π§π’π§π ?
πCalibration is the property that tells us how well the estimated probabilities of a model match the actual probabilities, a.k.a the observed frequency of occurrences.
πCalibration can be represented using the Brier score. The Brier score is nothing more than the MSE between the actual and the estimated probabilities.
πThe two most common methods to address poor calibration is:
πplatt scaling and
πisotonic regression
πCalibration is the property that tells us how well the estimated probabilities of a model match the actual probabilities, a.k.a the observed frequency of occurrences.
πCalibration can be represented using the Brier score. The Brier score is nothing more than the MSE between the actual and the estimated probabilities.
πThe two most common methods to address poor calibration is:
πplatt scaling and
πisotonic regression
=======================================
βοΈβοΈVisual explanations of core machine learning concepts.
=======================================
πNothing can beat the use of infographics and interactivity when explaining some concept,
πFor Linear Regression
https://mlu-explain.github.io/linear-regression/
πFor all,
https://mlu-explain.github.io/
βοΈβοΈVisual explanations of core machine learning concepts.
=======================================
πNothing can beat the use of infographics and interactivity when explaining some concept,
πFor Linear Regression
https://mlu-explain.github.io/linear-regression/
πFor all,
https://mlu-explain.github.io/
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π«₯π«₯FLAG: Flow-based 3D Avatar Generation from Sparse Observations
πPaper :
https://microsoft.github.io/flag/files/paper.pdf
πLink :
https://www.microsoft.com/en-us/research/publication/flag-flow-based-3d-avatar-generation-from-sparse-observations/
πPaper :
https://microsoft.github.io/flag/files/paper.pdf
πLink :
https://www.microsoft.com/en-us/research/publication/flag-flow-based-3d-avatar-generation-from-sparse-observations/
𧬠The data structure for unstructured multimodal data · Neural Search · Vector Search · Document Store
For doc
https://docarray.jina.ai/
For GitHub
https://github.com/jina-ai/docarray
For doc
https://docarray.jina.ai/
For GitHub
https://github.com/jina-ai/docarray
Transformers in Time Series: A Survey
A curated list of awesome resources (papers, code, data) on Transformers in Time Series categorized by tasks, including:
β’ Forecasting
β’ Anomaly detection
β’ Classification
Transformers capture long-range dependencies and interactions.
abs: https://arxiv.org/abs/2202.07125
pdf: https://arxiv.org/pdf/2202.07125.pdf
Awesome list repo: https://github.com/qingsongedu/time-series-transformers-review
A curated list of awesome resources (papers, code, data) on Transformers in Time Series categorized by tasks, including:
β’ Forecasting
β’ Anomaly detection
β’ Classification
Transformers capture long-range dependencies and interactions.
abs: https://arxiv.org/abs/2202.07125
pdf: https://arxiv.org/pdf/2202.07125.pdf
Awesome list repo: https://github.com/qingsongedu/time-series-transformers-review
googlefinance
Python module to get stock data from Google Finance API. This module provides no delay, real time stock data in NYSE & NASDAQ.
$pip install googlefinance
https://github.com/hongtaocai/googlefinance
Python module to get stock data from Google Finance API. This module provides no delay, real time stock data in NYSE & NASDAQ.
$pip install googlefinance
https://github.com/hongtaocai/googlefinance
GitHub
GitHub - hongtaocai/googlefinance: Python module to get real-time stock data from Google Finance API
Python module to get real-time stock data from Google Finance API - hongtaocai/googlefinance