- https://github.com/krishnamali1101/Amazing-python
- Basic python: https://www.youtube.com/playlist?list=PLsyeobzWxl7poL9JTVyndKe62ieoN-MZ3
- Advance python: https://www.youtube.com/playlist?list=PL-osiE80TeTt2d9bfVyTiXJA-UTHn6WwU
- ML basics-(intro- by Udacity): https://www.youtube.com/playlist?list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH
- ML normal-(by sentdex): https://www.youtube.com/playlist?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v
- ML Advance(by Andrew NG): https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN
- https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw/playlists
- Linear Algebra: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
- Calculas: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
- DL-Intro (by DeepLearning.TV): https://www.youtube.com/playlist?list=PLjJh1vlSEYgvGod9wWiydumYl8hOXixNu
- Deep Learning Normal (Udacity Nanodegree-Siraj Raval): https://www.youtube.com/playlist?list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3
- DL-Advance(By Andrew NG): https://www.youtube.com/playlist?list=PLBAGcD3siRDguyYYzhVwZ3tLvOyyG5k6K
- Nice tutorials to understand basics of deep-reinforcement learning
- Deep Learning with Tensorflow(by cognitive class): https://www.youtube.com/playlist?list=PL-XeOa5hMEYxNzHM7YLRjIwE1k3VQpqEh
- DL with TensorFlow(by tensorflow): https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ/playlists
- by sentdex: https://www.youtube.com/playlist?list=PLSPWNkAMSvv5DKeSVDbEbUKSsK4Z-GgiP
- Probability
- statistics
- Calculus
- Linear algebra
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Use of Mathematics in Machine Learning(siraj)
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most fundamental math concepts in Machine Learning(siraj)
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Probability(jbstatistics)
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Probability(statisticsfun)
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Statistics(Khan Academy)
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Linear Algebra(3Blue1Brown)
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Differential equations(3Blue1Brown)
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Probability & statistics in detail (jbstatistics)
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Probability & statistics in detail (statisticsfun)
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Multivariable calculus(3Blue1Brown)
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Geometry(3Blue1Brown)
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Top YouTube Channels to Learn Statistics and Math
- https://skymind.ai/wiki/open-datasets
- https://makingnoiseandhearingthings.com/2018/04/19/datasets-for-data-cleaning-practice/
- https://www.springboard.com/blog/free-public-data-sets-data-science-project/
- https://archive.ics.uci.edu/ml/index.php
- https://archive.ics.uci.edu/ml/datasets.php
- Top Sources For Machine Learning Datasets: https://towardsdatascience.com/top-sources-for-machine-learning-datasets-bb6d0dc3378b
- https://medium.com/towards-artificial-intelligence/the-50-best-public-datasets-for-machine-learning-d80e9f030279
- https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/
- https://elitedatascience.com/datasets
- https://lionbridge.ai/datasets/the-50-best-free-datasets-for-machine-learning/
- https://www.drivendata.org/competitions/55/schneider-cold-start/page/110/
- https://www.drivendata.org/competitions/55/schneider-cold-start/data/
- https://github.com/kailashahirwar/cheatsheets-ai
- https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463
http://www.cs.ccsu.edu/~markov/ccsu_courses/datamining-3.html
- Part 1: https://www.hackerearth.com/blog/developers/data-visualization-techniques/
- Part 2: https://www.hackerearth.com/blog/developers/data-visualization-for-beginners-part-2/
- Part 3: https://www.hackerearth.com/blog/developers/data-visualization-for-beginners-part-3/
- http://visualdl.paddlepaddle.org/documentation/visualdl/en/develop/getting_started/introduction_en.html
- https://github.com/PaddlePaddle/VisualDL
- http://visualdl.paddlepaddle.org
- An example of how to use VisualDL with PyTorch: https://nbro.github.io/blogging/2019/01/06/an-example-of-how-to-use-visualdl-with-pytorch/
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Part 1: A Decision Tree: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
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Part 2: Bias and Variance: http://www.r2d3.us/visual-intro-to-machine-learning-part-2/
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Explained Visually: http://setosa.io/ev/
- Explained Visually (EV) is an experiment in making hard ideas intuitive inspired the work of Bret Victor's Explorable Explanations. Sign up to hear about the latest.
- Topics
-
Setosa: http://setosa.io/#/
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Visualise any Math equation:
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ConvNetJS: https://cs.stanford.edu/people/karpathy/convnetjs/index.html
- Deep Learning in your browser
- ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat.
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Four Experiments in Handwriting with a Neural Network: https://distill.pub/2016/handwriting/
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Google Magenta: https://magenta.tensorflow.org/demos
- A primary goal of the Magenta project is to demonstrate that machine learning can be used to enable and enhance the creative potential of all people.
- The demos and apps listed on this page illustrate the work of many people--both inside and outside of Google--to build fun toys, creative applications, research notebooks, and professional-grade tools that will benefit a wide range of users.
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AI Experiments (experiments.withgoogle): https://experiments.withgoogle.com/collection/ai
- AI Experiments is a showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more.
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Top-10-deep-learning-experiences-run-on-your-browser: https://www.dlology.com/blog/top-10-deep-learning-experiences-run-on-your-browser/
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A Neural Network Playground - TensorFlow: https://playground.tensorflow.org
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DL Demos: http://deeplearning.net/demos/
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CNN training Visual Demo: https://teachablemachine.withgoogle.com/
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GAN training visual demo: https://poloclub.github.io/ganlab/
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Visualization in Deep Learning: https://medium.com/multiple-views-visualization-research-explained/visualization-in-deep-learning-b29f0ec4f136
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Machine Learning for Visualization https://medium.com/@enjalot/machine-learning-for-visualization-927a9dff1cab
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Data-Driven Documents: https://d3js.org
-
Visualizing K-Means Clustering https://www.naftaliharris.com/blog/visualizing-k-means-clustering/
Master Dimensionality Reduction with these 5 Must-Know Applications of Singular Value Decomposition (SVD) in Data Science
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This is a curated list of papers that I have encountered in some capacity and deem worth including in the NLP practitioner's library
-
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. https://nlpprogress.com/
- Machine Translation (https://lnkd.in/fAYvEne)
- Question Answering (Like Chat-bot) (https://lnkd.in/fFZmP4f)
- Sentiment Analysis (https://lnkd.in/fUDGAQW)
- Text Search (with Synonyms) (https://lnkd.in/fnU_a_H)
- Text Classifications (https://lnkd.in/f8mjKAP)
- Spelling Corrector (https://lnkd.in/f8JXNUv)
- Entity (Person, Place, or Brand) Recognition (https://lnkd.in/f2fzgAa)
- Text Summarization (https://lnkd.in/fdzWqXC)
- Text Similarity (https://lnkd.in/fv_sWuM)
- Topic Detection (https://lnkd.in/fxmhJZc)
- Emotion Recognition (https://lnkd.in/fK4m66Q)
- Language Identification (https://lnkd.in/fqfjxF9)
- Document Ranking (https://lnkd.in/fJZnkqz)
- Fake News Detection (https://lnkd.in/fkrkF8Q)
- Know Data Science https://lnkd.in/fMHtxYP
- Understand How to answer Why https://lnkd.in/f396Dqg
- Machine Learning Terminology https://lnkd.in/fCihY9W
- Understand Machine Learning Implementation https://lnkd.in/f5aUbBM
- Machine Learning on Retail https://lnkd.in/fihPTJf
- and Marketing https://lnkd.in/fUDGAQW
-
Natural Language Processing With Python by Steven Bird, Ewan Klein, and Edward Lo
-
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks:
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Modern NLP Tutorial with Python Code (Jupyter Notebook)
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A Quick and Easy Text Summarization tutorial( Code + Deploy )
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Learn Data Science (NLP) By Coding First!
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✔ spaCy Cheat Sheet:
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✔NLTK Cheat Sheet:
A curated list of awesome computer vision resources, inspired by awesome-php.
- https://github.com/jbhuang0604/awesome-computer-vision
- For a list people in computer vision listed with their academic genealogy, please visit here
- A curated list of deep learning resources for computer vision, inspired by awesome-php and awesome-computer-vision.
- https://github.com/kjw0612/awesome-deep-vision
- WER are we? An attempt at tracking states of the art(s) and recent results on speech recognition. Feel free to correct! (Inspired by Are we there yet?)
- https://github.com/syhw/wer_are_we
- Tutorial:
- Code: https://github.com/realpython/python-speech-recognition
https://experiments.withgoogle.com/collection/ai
http://www.yaronhadad.com/deep-learning-most-amazing-applications/
https://opensource.google/projects/list/machine-learning
https://medium.com/topic/technology
https://www.analyticsvidhya.com/
https://github.com/terryum/awesome-deep-learning-papers
https://www.wordstream.com/blog/ws/2017/07/28/machine-learning-applications
https://machinelearningmastery.com/inspirational-applications-deep-learning/
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Python Project(.py files): https://repl.it/repls
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Jupyter Notebooks (JPNB): https://colab.research.google.com/notebooks/welcome.ipynb
- The Most Comprehensive Data Science & Machine Learning Interview Guide You’ll Ever Need
- https://www.analyticsvidhya.com/blog/2018/06/comprehensive-data-science-machine-learning-interview-guide/?utm_source=facebook.com&utm_medium=social