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7. [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu/)
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8. [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf)
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9. [Artificial intelligence and machine learning: Topic wise explanation](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/)
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+10. [Dive into Deep Learning](https://d2l.ai/) - numpy based interactive Deep Learning book
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### Courses
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28. [MIT Intro to Deep Learning 7 day bootcamp](https://introtodeeplearning.com) - A seven day bootcamp designed in MIT to introduce deep learning methods and applications (2019)
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29. [Deep Blueberry: Deep Learning](https://mithi.github.io/deep-blueberry) - A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more (2019)
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30. [Spinning Up in Deep Reinforcement Learning](https://spinningup.openai.com/) - A free deep reinforcement learning course by OpenAI (2019)
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+31. [Deep Learning - UC Berkeley | STAT-157](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) by Alex Smola and Mu Li (2019)
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### Videos and Lectures
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73. [NIST Fingerprint and handwriting](ftp://sequoyah.ncsl.nist.gov/pub/databases/data) - datasets - thousands of images (Formats: unknown)
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74. [NIST Fingerprint data](ftp://ftp.cs.columbia.edu/jpeg/other/uuencoded) - compressed multipart uuencoded tar file
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75. [NLM HyperDoc Visible Human Project](http://www.nlm.nih.gov/research/visible/visible_human.html) - Color, CAT and MRI image samples - over 30 images (Formats: jpeg)
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-76. [National Design Repository](http://www.designrepository.org) - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineerign designs. (Formats: gif,vrml,wrl,stp,sat)
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+76. [National Design Repository](http://www.designrepository.org) - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineering designs. (Formats: gif,vrml,wrl,stp,sat)
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77. [Geometric & Intelligent Computing Laboratory](http://gicl.mcs.drexel.edu)
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79. [OSU (MSU) 3D Object Model Database](http://eewww.eng.ohio-state.edu/~flynn/3DDB/Models/) - several sets of 3D object models collected over several years to use in object recognition research (Formats: homebrew, vrml)
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80. [OSU (MSU/WSU) Range Image Database](http://eewww.eng.ohio-state.edu/~flynn/3DDB/RID/) - Hundreds of real and synthetic images (Formats: gif, homebrew)
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29. [Tensorflow - Open source software library for numerical computation using data flow graphs](https://github.com/tensorflow/tensorflow)
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30. [DMTK - Microsoft Distributed Machine Learning Tookit](https://github.com/Microsoft/DMTK)
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31. [Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)](https://github.com/google/skflow)
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-32. [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/dmlc/mxnet/)
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+32. [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/apache/incubator-mxnet)
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33. [Veles - Samsung Distributed machine learning platform](https://github.com/Samsung/veles)
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34. [Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework](https://github.com/PrincetonVision/marvin)
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35. [Apache SINGA - A General Distributed Deep Learning Platform](http://singa.incubator.apache.org/)
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3. [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit
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4. [Visual Studio Tools for AI](https://visualstudio.microsoft.com/downloads/ai-tools-vs) - Develop, debug and deploy deep learning and AI solutions
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5. [dowel](https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call to `logger.log()`
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+6. [Neptune](https://neptune.ml/) - Lightweight tool for experiment tracking and results visualization.
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### Miscellaneous
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