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Tutorials Alternatives
Similar projects and alternatives to tutorials
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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pytorch-lightning
Discontinued The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. [Moved to: https://github.com/PyTorchLightning/pytorch-lightning] (by williamFalcon)
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pytorch_geometric
Discontinued Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric] (by rusty1s)
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PyTorch-NLP
Discontinued Basic Utilities for PyTorch Natural Language Processing (NLP)
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Bootstrap
The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
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materials
Bonus materials, exercises, and example projects for our Python tutorials
tutorials reviews and mentions
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Best Portfolio Projects for Data Science
Pytorch Documentation
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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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Introduction to PyTorch
PyTorch Tutorials
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Looking For A Master Thesis Website Project Ideas For Graduating Computer And Communication Engineering Student.
If you are looking for inspiration in terms of what the app will actually do, I'd actually look over the PyTorch tutorials here: https://pytorch.org/tutorials/ There are quite a few categories with examples and datasets that might give you inspiration for a project.
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Why are some PyTorch missing from pytorch.org/tutorials ?
However, it is reachable in tutorial's github, https://github.com/pytorch/tutorials/tree/master/beginner_source/basics
I discovered that some tutorials like https://pytorch.org/tutorials/beginner/basics/data_tutorial.html is not included in https://pytorch.org/tutorials/.
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[N] PyTorch 1.8 Release, including Compiler and Distributed Training updates, New Mobile Tutorials
Since it provides you a general computational graph, you can also perform arbitrary analyses on your graph, like using it to profile each operation in your graph (https://github.com/pytorch/tutorials/blob/master/intermediate_source/fx_profiling_tutorial.py).
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pytorch/tutorials is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of tutorials is Jupyter Notebook.