Courses
Introduction
Repository with all courses about artificial intelligence I have finished or I am doing right now. They concerned :
- python
- tensorflow
- keras
- numpy
- pandas
- scrapy
- matplotlib
- mathematics stuff
All courses
- Google developers
- Hands-on Machine Learning with Scikit-learn and TensorFlow
- Kaggle
- Matplotlib
- MNIST
- notMNIST
- Scrapy Utils
- Tensorboard
Links
I try to put here all available ressources on internet that I found useful.
Official documentation
References
- https://hackernoon.com/index-of-best-ai-machine-learning-resources-71ba0c73e34d
- http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html
Datasets
- https://www.kaggle.com/
- http://archive.ics.uci.edu/ml/index.php
- https://www.kaggle.com/datasets
- https://aws.amazon.com/fr/datasets/
- https://opendatamonitor.eu/frontend/web/index.php?r=dashboard%2Findex
- https://www.quandl.com/
- https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
- https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public
- https://www.reddit.com/r/datasets/
AI Solutions
People to follow
- Siraj Raval
- Yann LeCun
- SendTex
- https://www.youtube.com/watch?v=er8RQZoX3yk&list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ
- https://petewarden.com/
- https://lukasbiewald.com/
Programs
- https://medium.freecodecamp.org/every-single-machine-learning-course-on-the-internet-ranked-by-your-reviews-3c4a7b8026c0
- https://www.coursera.org/learn/machine-learning/
- https://www.coursera.org/learn/neural-networks
- https://eu.udacity.com/
- https://www.dataquest.io/
- https://www.quora.com/What-are-the-best-regularly-updated-machine-learning-blogs-or-resources-available
- http://deeplearning.net/
- https://github.com/ageron/handson-ml
- https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/
- http://course.fast.ai/
- https://codelabs.developers.google.com/codelabs/cpb102-txf-learning/index.html?index=..%2F..%2Findex#0
- https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf
- https://deeplearning4j.org/deeplearningforbeginners.html
- https://www.youtube.com/watch?v=vq2nnJ4g6N0&t=854s or in french https://www.youtube.com/watch?v=BtAVBeLuigI
- https://www.youtube.com/watch?v=Otr-epDknd0&list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ
- https://www.youtube.com/watch?v=OGxgnH8y2NM&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v
- https://www.college-de-france.fr/site/yann-lecun/course-2016-02-12-14h30.htm (french)
- https://www.kaggle.com/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy
- http://jessica2.msri.org/attachments/10778/10778-boost.pdf
- https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv
Books
- Amazon books
- How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil
- Data Science from Scratch by Joel Grus
- Machine Learning: An Algorithmic Perspective by Stephen Marsland,
- Python Machine Learning by Sebastian Raschka
- Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin,
- Artificial Intelligence: A Modern Approach, 3rd Edition by Stuart Russell and Peter Norvig,
- Programming Collective Intelligence by Toby Segaran
- Machine Learning for Hackers by Drew Conway and John Myles White
- Machine Learning by Tom M Mitchell
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Artificial Intelligence for Humans by Jeff Heaton
- Paradigm of Artificial Intelligence Programming by Peter Norvig
- Artificial Intelligence: A New Synthesis by Nils J. Nilsson
- The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of Human Mind by Marvin Minsky
- Think Stats: Probability and Statistics for Programmers by Allen B. Downey
- Probabilistic Programming & Bayesian Methods for Hackers by Cameron Davidson-Pilon
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
- Programmer's Guide to Data Mining: The Ancient Art of the Numerati by Ron Zacharski
- Mining of Massive Datasets by Anand Rajaraman and Jeffrey David Ullman
- Machine Learning Yearning by Andrew Ng
- Building Machine Learning Systems with Python by Willi Richert, Luis Pedro Coelho
- Learning scikit-learn: Machine Learning in Python by Raúl Garreta, Guillermo Moncecchi
- Machine Learning in Action by Peter Harrington
- Machine Learning For Dummies by John Paul Mueller and Luca Massaron
- Machine Learning: The New AI by Ethem Alpaydin
- Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter Flach
- Make Your Own Neural Network by Tariq Rashid
- Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee and Aoife D'Arcy
- Matrix Computations by Gene H. Golub and Charles F. Van Loan
- A Probabilistic Theory of Pattern Recognition by Luc Devroye, Laszlo Györfi, Gabor Lugosi
- Advanced Engineering Mathematics by Erwin Kreyszig
- Probability and Statistics Cookbook by Matthias Vallentin
- Bayesian Reasoning and Machine Learning by David Barber
- Information Theory, Inference, and Learning Algorithms by de David J. C. MacKay
- Neural Networks and Deep Learning by Michael Nielsen
- Supervised Sequence Labelling with Recurrent Neural Networks by Alex Graves
- Reinforcement Learning: An Introduction by Richard Sutton
- Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido
- Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Nathan Yau
Algorithms
General
- https://github.com/arcyfelix/Courses
- https://towardsdatascience.com/a-tour-of-the-top-10-algorithms-for-machine-learning-newbies-dde4edffae11
- https://towardsdatascience.com/the-8-neural-network-architectures-machine-learning-researchers-need-to-learn-11a0c96d6073
Pix2code
- https://blog.floydhub.com/Turning-design-mockups-into-code-with-deep-learning/
- https://airbnb.design/sketching-interfaces/
- https://arxiv.org/abs/1705.07962
- https://github.com/emilwallner/Screenshot-to-code-in-Keras/blob/master/README.md
Gans
- https://github.com/wiseodd/generative-models
- http://wiseodd.github.io/techblog/2016/09/17/gan-tensorflow/
Text2img
Python
Scrapy
Unity Machine Learning
Machine Learning
Image classifiers
- https://m.oursky.com/using-tensorflow-and-support-vector-machine-to-create-an-image-classifications-engine-7ee51b5617d5
- https://hackernoon.com/creating-insanely-fast-image-classifiers-with-mobilenet-in-tensorflow-f030ce0a2991
- https://hackernoon.com/building-an-insanely-fast-image-classifier-on-android-with-mobilenets-in-tensorflow-dc3e0c4410d4
- https://www.kernix.com/blog/image-classification-with-a-pre-trained-deep-neural-network_p11
- https://kwotsin.github.io/tech/2017/02/11/transfer-learning.html
- https://github.com/kwotsin/transfer_learning_tutorial/blob/master/train_flowers.py
- https://medium.com/google-cloud/keras-inception-v3-on-google-compute-engine-a54918b0058
- https://hackernoon.com/deep-learning-cnns-in-tensorflow-with-gpus-cba6efe0acc2
- http://blog.bitfusion.io/2016/08/31/training-a-bird-classifier-with-tensorflow-and-tflearn
- https://medium.com/initialized-capital/we-need-to-go-deeper-a-practical-guide-to-tensorflow-and-inception-50e66281804f
- https://boingboing.net/tag/machine-learning
- https://boingboing.net/2018/01/18/declassified-classifier.html
Style Transfers
Gcloud
Corrupted data
- Cleaning up incorrectly labeled data
- Identifying Mislabeled Training Data
- Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
- Learning to Classify with Missing and Corrupted Features
Others
- https://futurism.com/an-ai-can-recreate-your-favorite-old-school-video-game-just-by-watching-someone-play-it/
- https://www.ted.com/talks/joseph_redmon_how_a_computer_learns_to_recognize_objects_instantly#t-394057
- http://www.kennybastani.com/2014/08/using-graph-database-for-deep-learning-text-classification.html
- https://www.kernix.com/blog/an-efficient-recommender-system-based-on-graph-database_p9
- https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3
- https://stackoverflow.com/questions/37353759/how-do-i-generate-an-adjacency-matrix-of-a-graph-from-a-dictionary-in-python
- https://security.stackexchange.com/questions/135211/can-a-neural-network-crack-hashing-algorithms
- https://www.ted.com/talks/jim_simons_a_rare_interview_with_the_mathematician_who_cracked_wall_street#t-1375411
- https://www.ted.com/talks/oscar_schwartz_can_a_computer_write_poetry/transcript