tutorials
PyTorch-NLP
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tutorials | PyTorch-NLP | |
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30 | 1 | |
7,808 | 2,180 | |
2.1% | - | |
9.4 | 0.0 | |
2 days ago | 10 months ago | |
Jupyter Notebook | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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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
PyTorch-NLP
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Introduction to PyTorch
PyTorch-NLP
What are some alternatives?
dex-lang - Research language for array processing in the Haskell/ML family
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
FlexFlow - FlexFlow Serve: Low-Latency, High-Performance LLM Serving
NLTK - NLTK Source
adaptdl - Resource-adaptive cluster scheduler for deep learning training.
pytext - A natural language modeling framework based on PyTorch
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]
Jieba - 结巴中文分词
pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages