tutorials
seaborn
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tutorials | seaborn | |
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30 | 76 | |
7,808 | 11,958 | |
2.1% | - | |
9.4 | 8.4 | |
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Jupyter Notebook | Python | |
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tutorials
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Ask HN: Is there a tutorial avaible for Deep Learning based Upscaling
There are plenty of tutorials for Deep Learning available, https://pytorch.org/tutorials/. Does anyone know of a tutorial or example of Image Upscaling in a similar vain to Nvidia's DLSS?
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Best Portfolio Projects for Data Science
Pytorch Documentation
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unique game idea ( literally )
PyTorch: https://pytorch.org/tutorials/
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How to learn PyTorch?
There's a TON of tutorials in the pytorch tutorials section, they're pretty solid. If you know what area you're specifically interested in, check to see if you can find some relevant tutorials to start with.
- What are some good pytorch courses online?
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How do I get started with ML?
Learn Python: Python is the most popular language for ML and AI projects. Start by learning the basics of Python, then move on to more advanced topics. Some great resources for learning Python include: Codecademy's Python course: https://www.codecademy.com/learn/learn-python Real Python: https://realpython.com/ Mathematics: A solid understanding of mathematics, particularly linear algebra, calculus, probability, and statistics, is essential for ML. Here are some resources to help you learn: Khan Academy courses: Linear Algebra: https://www.khanacademy.org/math/linear-algebra Calculus: https://www.khanacademy.org/math/calculus-1 Probability and Statistics: https://www.khanacademy.org/math/statistics-probability 3Blue1Brown's YouTube series on Linear Algebra: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Data processing and manipulation: Familiarize yourself with popular Python libraries for data manipulation and analysis, such as NumPy, pandas, and matplotlib: NumPy: https://numpy.org/doc/stable/user/quickstart.html pandas: https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/index.html matplotlib: https://matplotlib.org/stable/tutorials/index.html Machine learning concepts: Learn about the basic concepts of ML, including supervised learning, unsupervised learning, and reinforcement learning. Some great resources include: Coursera's Machine Learning course by Andrew Ng: https://www.coursera.org/learn/machine-learning Google's Machine Learning Crash Course: https://developers.google.com/machine-learning/crash-course Fast.ai's Practical Deep Learning for Coders course: https://course.fast.ai/ Deep learning libraries: Get familiar with popular deep learning libraries such as TensorFlow and PyTorch: TensorFlow: https://www.tensorflow.org/tutorials PyTorch: https://pytorch.org/tutorials/ Specialize and work on projects: Choose an area of interest (such as natural language processing, computer vision, or reinforcement learning), and start working on projects to apply your skills. You can find datasets and project ideas from sources like: Kaggle: https://www.kaggle.com/ Papers With Code: https://paperswithcode.com/ Stay up-to-date and join the community: Follow ML blogs, podcasts, and conferences to stay current with the latest developments. Join ML communities and forums like r/MachineLearning on Reddit, AI Stack Exchange, or specialized Discord and Slack groups.
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How do I activate the TPU when using pytorch (code inside)?
The code looks almost identical to this: https://github.com/pytorch/tutorials/blob/master/beginner_source/chatbot_tutorial.py
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How to Implement Feed Forward NN in PyTorch for Classification
Well the pytorch documentation is pretty good. (https://pytorch.org/tutorials/)
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PyTorch Tutorial for People with Keras/Tensorflow experience?
Pytorch tutorials https://pytorch.org/tutorials/ on their official website has all the basic commands and should be easier to pickup since you already know tensorflow/ keras.
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PyTorch introduces ‘nvFuser’: a Deep Learning Compiler for NVIDIA GPUs that automatically just-in-time compiles fast and flexible kernels to reliably accelerate users’ networks
Continue reading |Github link | Reference article
seaborn
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Apache Superset
If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.
I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.
I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.
For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.
Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).
The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.
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Seaborn bug responsible for finding of declining disruptiveness in science
It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).
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Why Pandas feels clunky when coming from R
While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.
https://seaborn.pydata.org/
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.
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Seven Python Projects to Elevate Your Coding Skills
Matplotlib Seaborn Example data sets
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Seaborn - Statistical data visualization using Matplotlib.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/mwaskom/seaborn
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Best Portfolio Projects for Data Science
Seaborn Documentation
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[OC] Nationwide Public Transit Ridership is down 30% from pre-lockdown levels; San Francisco's BART ridership is down almost 70%
You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts
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Introducing seaborn-polars, a package allowing to use Polars DataFrames and LazyFrames with Seaborn
I'm sure that your package is great, but seaborn will soon support the interchange protocol and will work relatively seamlessly with polars. https://github.com/mwaskom/seaborn/pull/3340
What are some alternatives?
dex-lang - Research language for array processing in the Haskell/ML family
bokeh - Interactive Data Visualization in the browser, from Python
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Altair - Declarative statistical visualization library for Python
FlexFlow - FlexFlow Serve: Low-Latency, High-Performance LLM Serving
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
adaptdl - Resource-adaptive cluster scheduler for deep learning training.
ggplot - ggplot port for python
pytorch-lightning - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. [Moved to: https://github.com/PyTorchLightning/pytorch-lightning]
plotnine - A Grammar of Graphics for Python
pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]
matplotlib - matplotlib: plotting with Python