The curated list of deep learning resources
- Deep Learning foundation
- Deep Learning Visualization
- Pytorch
- Tensorflow
- Paper
- Computer Vision
- Tool
- MLOps
- The spelled-out intro to neural networks and backpropagation: building micrograd - YouTube
- This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school.
- Links:
- micrograd on github: https://github.com/karpathy/micrograd
- jupyter notebooks I built in this video: randomfun/lectures/micrograd at master · karpathy/randomfun
- my website: https://karpathy.ai
- my twitter: https://twitter.com/karpathy
- Deep Learning Specialization-Andrew NG
- Convolution Visualizer:
- julrog/nn_vis: A project for processing neural networks and rendering to gain insights on the architecture and parameters of a model through a decluttered representation.
- Convolution Neural Network Visualization - Made with Unity 3D and lots of Code
- CNN Explainer
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Deep Learning with PyTorch: A 60 Minute Blitz — PyTorch Tutorials 1.12.0+cu102 documentation: Pytorch official tutorial
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deeplizard - PyTorch - Python Deep Learning Neural Network API
- Deep explaination of tensor
- My Course Notes
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Practical Deep Learning for Coders - Practical Deep Learning
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A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
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After finishing this course you will know:
- How to train models that achieve state-of-the-art results in:
- Computer vision, including image classification (e.g., classifying pet photos by breed)
- Natural language processing (NLP), including document classification (e.g., movie review sentiment analysis) and phrase similarity
- Tabular data with categorical data, continuous data, and mixed data
- Collaborative filtering (e.g., movie recommendation)
- How to turn your models into web applications, and deploy them
- Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models
- The latest deep learning techniques that really matter in practice
- How to implement stochastic gradient descent and a complete training loop from scratch
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datawhalechina/thorough-pytorch: PyTorch入门教程: Chinese pytorch tutorial
- MrGemy95/Tensorflow-Project-Template: A best practice for tensorflow project template architecture.: a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design.
- Browse the State-of-the-Art in Machine Learning | Papers With Code
- The latest in Machine Learning | Papers With Code
- Stateoftheart AI: An open-data and free platform built by the research community to facilitate the collaborative development of AI
- labmlai/annotated_deep_learning_paper_implementations: 🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝
- DengBoCong/nlp-paper: 自然语言处理领域下的相关论文(附阅读笔记),复现模型以及数据处理等(代码含TensorFlow和PyTorch两版本)