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To memorize the journey start from self-driving Car Engineer Nanodegree Program to Artificial Intelligence Nanodegree Program to Flying Car Nanodegree Program then Robotics Software Engineer Nanodegree Program and AI for Trading Nanodegree program

udacity-facebook-scholar 60daysofudacity self-driving-car flying-car artificial-intelligence robotics ai-for-trading computer-vision recurrent-neural-networks deep-neural-networks

the-love-i-receive-from-udacity-reviewer-resources's Introduction

The-love-I-receive-from-Udacity-reviewer-resources

My gratitude to Sir Sebastion Thrun, Instructor Katie Malone, Sir Peter Norvig, Sir Bjarne Stroustrup, Sir Thad Starner, Sir Nicholas Roy, Sir Andrew Ng, Sir David J. Malan, Sir David Silver, Sir Raffaello D'Andrea, Sir Ryan Keenan, Instructor Jay Alammar, Madam Dana Sheahen, Instructor Alexis Cook, Instructor Cezanne Camacho, Instructor Erica, Instructor Karim, Instructor Julia, Sir Stephen Welch, Instructor Andreas Haja, Sir Aaron Brown, Sir Andy, Instructor Stefanie, Instructor Angela, Sir Andrew Trask, Sir Jonathan Larkin, Sir Gordon Ritter, Sir Justin Sheetz, Instructor Luis Serrano, Instructor Liz Otto Hamel, Instructor Eddy Shyu, Instructor Josh Bernhard, Instructor Parnian Barekatain, Instructor Miriam, Instructor Mat Leonard, Instructor Cindy, Instructor Brok Bucholtz, Instructor Arpan Chakraborty, Instructor Juan Delgado, Sir Jake, Sir Michael Virgo, Sir Tucker, Instructor Juno Lee, Sir Akshit, Brenda.Udacity, Palak.Udacity, Grace.Udacity, Instructor Grant Sanderson, Instructor Ortal, Instructor Jennifer Staab, Instructor Amanda Moran, Instructor Thomas Hossler, Instructor Antje Muntzinger, Instructor Mathilde Badoul, Instructor Munir Jojo Verge, Instructor Brandy Camacho, Instructor Jessica Lin, Instructor Joe Nyzio, Instructor Rachna Ralhan, Instructor PK Rasam, Instructor Nik Kalyani, Instructor Elena Nadolinski.

To memorize the journey starts from the Self-Driving Car Engineer Nanodegree Program to Artificial Intelligence Nanodegree Program to the Flying Car Nanodegree Program then Robotics Software Nanodegree Program, Computer Vision Nanodegree Program, AI for trading Nanodegree Program, C++ Nanodegree Program and CS50: Introduction to Computer Science 2018, To memorize the year of my enrollment in Udacity from 2017 to present.

To memorize mentors Donald, Christopher, Jafar, Renaud Béchade, Pranjal Chaubey, David, Ray, and John who encourage and support me a lot through my enrollment years. Here I collect the feedback and paper references from my Udacity reviewers and friendships I got from Udacity and CS50X. I want to extend what I learn and also extend the spirit, and share knowledge with all my classmates at least this is what I can do if any of these citation papers help you please send a star to them or references their papers will be an honorable action:).

Within these periods I want to say thank you to Madam Olga Uskova, Mylene doublet o'kane, and Sir Luigi Morelli. I love your writings and very much appreciate every like you send to me. My gratitude to Martin McGovern, Karen E. Baker, Brenda Law, Palak Sadani, Grace Cho, Isabella Navarro, Monique, Chris, Shivani, JP Miller, and Udacity team you let me learn to be Udacious and persistent :D!

From Madam Olga Uskova:

https://en.wikipedia.org/wiki/Olga_Uskova
https://vimeo.com/cognitivetech
http://www.taipeitimes.com/News/feat/archives/2018/01/08/2003685375
http://www.agriland.ie/farming-news/fully-driverless-combine-harvester-by-2024/

Paper References from Self-Driving Car Reviewers:

https://airccj.org/CSCP/vol5/csit53211.pdf
https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
http://www.kerrywong.com/2009/05/07/canny-edge-detection-auto-thresholding/
https://medium.com/@vivek.yadav/improved-performance-of-deep-learning-neural-network-models-on-traffic-sign-classification-using-6355346da2dc
https://medium.com/@jeremyeshannon/udacity-self-driving-car-nanodegree-project-2-traffic-sign-classifier-f52d33d4be9f
https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py
https://keras.io/callbacks/#callback
https://keras.io/callbacks/#modelcheckpoint
http://cs229.stanford.edu/proj2015/054_report.pdf
http://ruder.io/optimizing-gradient-descent/
http://ruder.io/optimizing-gradient-descent/index.html#adam
http://alexlenail.me/NN-SVG/LeNet.html
https://keras.io/visualization/
https://www.researchgate.net/publication/257291768_A_Much_Advanced_and_Efficient_Lane_Detection_Algorithm_for_Intelligent_Highway_Safety
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017478/
https://medium.com/@mohankarthik/feature-extraction-for-vehicle-detection-using-hog-d99354a84d10
https://chatbotslife.com/towards-a-real-time-vehicle-detection-ssd-multibox-approach-2519af2751c
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
https://stackoverflow.com/questions/27466642/what-kind-of-optimization-does-const-offer-in-c-c
https://www.youtube.com/watch?v=aUkBa1zMKv4
http://correll.cs.colorado.edu/?p=965
https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume9/mazer98a-html/node2.html
http://www.roborealm.com/help/Path_Planning.php
https://robotics.stackexchange.com/questions/8302/what-is-the-difference-between-path-planning-and-motion-planning
https://www.robotshop.com/community/forum/t/excellent-tutorial-on-a-robot-path-planning/13170
https://www.mathworks.com/help/robotics/examples/path-planning-in-environments-of-difference-complexity.html;jsessionid=a7ff890a4e697fe79be723535659
http://ais.informatik.uni-freiburg.de/teaching/ss11/robotics/slides/18-robot-motion-planning.pdf
http://ais.informatik.uni-freiburg.de/teaching/ss10/robotics/slides/16-pathplanning.pdf
http://ai.stanford.edu/~ddolgov/papers/dolgov_gpp_stair08.pdf
https://webpages.uncc.edu/~jmconrad/GradStudents/Thesis_Ghangrekar.pdf
http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review
https://arxiv.org/pdf/1411.4038.pdf
http://cs231n.github.io/neural-networks-2/#init
http://cs231n.github.io/neural-networks-2/#reg
https://stats.stackexchange.com/questions/164876/tradeoff-batch-size-vs-number-of-iterations-to-train-a-neural-network
https://www.jeremyjordan.me/nn-learning-rate/
https://arxiv.org/abs/1806.02446
https://arxiv.org/abs/1903.03273
https://arxiv.org/abs/1901.00114
https://roscon.ros.org/2018/#program
http://driving.stanford.edu/papers/ISER2010.pdf

Paper References from Flying Car reviewers:

http://planning.cs.uiuc.edu/
https://docs.ufpr.br/~danielsantos/ProbabilisticRobotics.pdf
https://www.youtube.com/playlist?list=PLX2gX-ftPVXU3oUFNATxGXY90AULiqnWT
https://simondlevy.academic.wlu.edu/
https://docs.google.com/viewer?url=https%3A%2F%2Fwww.seas.harvard.edu%2Fcourses%2Fcs281%2Fpapers%2Funscented.pdf
http://andrew.gibiansky.com/downloads/pdf/Quadcopter%20Dynamics,%20Simulation,%20and%20Control.pdf http://aeroconf.org/
https://arxiv.org/abs/1902.01465
http://navion.mit.edu/
https://www.researchgate.net/publication/322020415_Adaptive_Super-twisting_Second-order_Sliding_Mode_Control_for_Attitude_Control_of_Quadcopter_UAVs
https://arxiv.org/abs/1801.10130
https://utm.arc.nasa.gov/documents.shtml

Paper References from Artificial Intelligence Nanodegree Program reviewers:

https://people.csail.mit.edu/rivest/pubs/Riv87c.pdf
https://www.semanticscholar.org/paper/Deep-Blue-Campbell-Hoane/378f933bbdb70d6f373e32e7182b6a5669c95d02
https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf
http://www.cs.nott.ac.uk/~psznza/G52PAS/lecture9.pdf
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture10FinalPart1.pdf
https://artint.info/html/ArtInt_206.html
http://www.cs.umd.edu/~djacobs/CMSC828/ApplicationsHMMs.pdf
https://www.quora.com/What-are-some-applications-of-Probabilistic-Graphical-Models

Paper References from Robotics Software Engineer Nanodegree Program reviewers:

https://www.youtube.com/watch?time_continue=33&v=J_lXNPRIwag
http://www.cs.cmu.edu/~15464-s13/lectures/lecture6/IK.pdf
http://www.cs.columbia.edu/~allen/F15/NOTES/jacobians.pdf
https://wiki.python.org/moin/UsingPickle
https://distill.pub/2016/augmented-rnns/
https://indico.io/blog/sequence-modeling-neural-networks-part2-attention-models/
https://blog.keras.io/building-autoencoders-in-keras.html
https://arxiv.org/pdf/1511.06309.pdf
https://pjreddie.com/darknet/yolo/
http://cs231n.github.io/classification/
https://towardsdatascience.com/deep-learning-for-image-classification-why-its-challenging-where-we-ve-been-and-what-s-next-93b56948fcef
https://blog.paralleldots.com/data-science/must-read-path-breaking-papers-about-image-classification/
https://blog.openai.com/adversarial-example-research/
https://blog.xix.ai/how-adversarial-attacks-work-87495b81da2d
http://robots.stanford.edu/papers/thrun.robust-mcl.pdf
https://ai.googleblog.com/2018/12/exploring-quantum-neural-networks.html
https://realsense.intel.com/deep-learning-for-vr-ar/
http://vision.stanford.edu/pdf/mandlekar2018corl.pdf
http://robot.cc/papers/thrun.graphslam.pdf
http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
https://s3-us-west-1.amazonaws.com/udacity-drlnd/bookdraft2018.pdf
https://github.com/udacity/rl-cheatsheet/blob/master/cheatsheet.pdf
https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision
https://movingai.com/astar-var.html
http://theory.stanford.edu/~amitp/GameProgramming/Variations.html
https://www.cs.cmu.edu/~maxim/files/pathplanforMAV_icra13.pdf
https://arxiv.org/pdf/1611.03673.pdf
http://proceedings.mlr.press/v48/mniha16.pdf
https://deepmind.com/blog/article/reinforcement-learning-unsupervised-auxiliary-tasks
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c-c88f72a5e9f2
https://arxiv.org/pdf/1609.05143.pdf
https://openai.com/blog/ingredients-for-robotics-research/
https://arxiv.org/pdf/1708.05866.pdf
https://openai.com/resources/
https://www.groundai.com/project/self-supervised-deep-reinforcement-learning-with-generalized-computation-graphs-for-robot-navigation/
http://raiahadsell.com/uploads/3/6/4/2/36428762/erf2017_keynote_talk.pdf
https://papers.nips.cc/paper/1999/file/54f5f4071faca32ad5285fef87b78646-Paper.pdf
http://read.pudn.com/downloads142/sourcecode/others/617477/inventory%20supply%20chain/04051310570412465(1).pdf
https://deeplearning.mit.edu/
https://www.youtube.com/channel/UCXZCJLdBC09xxGZ6gcdrc6A
https://github.com/openai/gym
https://www.youtube.com/watch?v=iX5V1WpxxkY
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://www.youtube.com/watch?v=UNmqTiOnRfg
https://www.youtube.com/watch?v=WCUNPb-5EYI
http://twistedoakstudios.com/blog/Post554_minkowski-sums-and-differences
https://www.toptal.com/game/video-game-physics-part-ii-collision-detection-for-solid-objects

SLAM
Mapping

Paper References from Secure and Private AI Scholarship Challenge Nanodegree Program:

https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
https://arxiv.org/pdf/1607.00133.pdf
https://blog.openmined.org/federated-learning-of-a-rnn-on-raspberry-pis/ from Sarah.
https://towardsdatascience.com/pysyft-android-b28da47a767e
https://course.fast.ai/
CNN's for Visual Recognition
Deep Conv nets for image classification
Large Scale image Recognition using DNN's
Transfer Learning
Awesome Deep Learning Papers

Paper References from AI for Trading Nanodegree Program:

https://machinelearningmastery.com/statistical-hypothesis-tests/
https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/
https://www.datacamp.com/community/tutorials/finance-python-trading
https://towardsdatascience.com/inferential-statistics-series-t-test-using-numpy-2718f8f9bf2f
https://www.zipline.io/appendix.html#zipline.pipeline.factors.Factor.rank
https://towardsdatascience.com/how-the-mathematics-of-fractals-can-help-predict-stock-markets-shifts-19fee5dd6574
http://www.econ.yale.edu/~shiller/
https://www.nytimes.com/search/?query=economic+view+and+shiller&srchst=nyt
https://www.fooledbyrandomness.com/
https://iai.tv/video/how-do-you-solve-a-problem-like-uncertainty
https://www.nytimes.com/2012/12/24/opinion/stabilization-wont-save-us.html
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1921537
http://www.statsmodels.org/dev/generated/statsmodels.stats.diagnostic.het_breuschpagan.html
https://www.investopedia.com/articles/trading/08/turtle-trading.asp
https://github.com/quantopian/alphalens/blob/master/alphalens/performance.py
https://quantopian.github.io/alphalens/alphalens.html?highlight=alphalens%20performance#alphalens.utils.get_clean_factor_and_forward_returns
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
http://blog.echen.me/2017/05/30/exploring-lstms/
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://www.youtube.com/watch?v=iX5V1WpxxkY
https://www.crummy.com/software/BeautifulSoup/bs4/doc/#differences-between-parsers
https://github.com/udacity/deep-learning-v2-pytorch
https://pytorch.org/docs/stable/nn.html#recurrent-layers
https://video.udacity-data.com/topher/2018/October/5bc56d28_word2vec-mikolov/word2vec-mikolov.pdf
https://video.udacity-data.com/topher/2018/October/5bc56da8_distributed-representations-mikolov2/distributed-representations-mikolov2.pdf
https://github.com/pytorch/tnt/blob/master/torchnet/dataset/tensordataset.py
https://stocktwits.com
https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
https://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27
https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
https://github.com/slundberg/shap
https://github.com/Polarbeargo/artificial-intelligence-for-trading/blob/master/quiz/m7/m7l6/calculate_shap_solution.ipynb
https://arxiv.org/pdf/1802.03888.pdf
https://github.com/Polarbeargo/artificial-intelligence-for-trading/blob/master/quiz/m7/m7l6/tree_shap_solution.ipynb
https://shap.readthedocs.io/en/latest/
https://github.com/Polarbeargo/artificial-intelligence-for-trading/blob/master/quiz/m7/m7l6/rank_features_solution.ipynb
https://www.youtube.com/watch?v=Q8rTrmqUQsU
https://stackabuse.com/overview-of-classification-methods-in-python-with-scikit-learn/
https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052
https://heartbeat.fritz.ai/introduction-to-machine-learning-model-evaluation-fa859e1b2d7f
https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
https://towardsdatascience.com/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e
https://www.quora.com/What-is-the-difference-between-autocorrelation-cross-correlation
https://pythonprogramming.net/rolling-statistics-data-analysis-python-pandas-tutorial/
https://rabinpoudyal.medium.com/train-test-split-and-cross-validation-in-python-434ecba10909
https://datascience.stackexchange.com/questions/15135/train-test-validation-set-splitting-in-sklearn
https://towardsdatascience.com/understanding-random-forest-58381e0602d2
https://towardsdatascience.com/fighting-imbalance-data-set-with-code-examples-f2a3880700a6
https://stackoverflow.com/questions/10032579/how-to-evaluate-a-dataset-for-class-overlapping
https://www.datatechnotes.com/2019/03/classification-with-bagging-classifier.html
https://www.geeksforgeeks.org/ml-bagging-classifier/
https://www.programcreek.com/python/example/86713/sklearn.ensemble.BaggingClassifier
http://datagrid.lbl.gov/backtest/index.php
https://web.stanford.edu/~hastie/Papers/ESLII.pdf
https://medium.com/kaggle-blog
https://arxiv.org/pdf/1811.05230.pdf
http://scipy-lectures.org/advanced/mathematical_optimization/
http://web.stanford.edu/class/ee364b/lectures.html
https://cims.nyu.edu/~ritter/ritter2017machine.pdf
https://cims.nyu.edu/~ritter/
https://towardsdatascience.com/backtesting-your-first-trading-strategy-ad3977f3f2a
https://towardsdatascience.com/backtesting-trading-strategies-less-sorcery-and-more-statistics-on-your-side-241ac41d18b0
https://trade.collective2.com/ai-powered-strategy-backtesting-and-forecasting-platform-neotic-integrates-with-collective2-to-provide-automated-trading/
https://medium.com/@alexrachnog/ai-for-algorithmic-trading-7-mistakes-that-could-make-me-broke-a41f94048b8c
https://www.naftaliharris.com/blog/visualizing-k-means-clustering/
http://jalammar.github.io

References from C++ Nanodegree Program:

https://www.stroustrup.com/papers.html
https://www.valgrind.org/info/
https://github.com/sowson/valgrind
https://docs.microsoft.com/en-us/visualstudio/debugger/finding-memory-leaks-using-the-crt-library?view=vs-2019
https://darkdust.net/files/GDB%20Cheat%20Sheet.pdf
https://github.com/hishamhm/htop/commit/da4877f48c70f765f8bfb60c7668e8499055662e
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#S-glossary
https://en.cppreference.com/w/cpp/language/identifiers
https://github.com/CppCon
https://en.cppreference.com/w/cpp/language/raii
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#rsmart-smart-pointers
https://en.cppreference.com/w/cpp/memory
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#c135-use-multiple-inheritance-to-represent-multiple-distinct-interfaces
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#c136-use-multiple-inheritance-to-represent-the-union-of-implementation-attributes
https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md#Rh-get
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#c2-use-class-if-the-class-has-an-invariant-use-struct-if-the-data-members-can-vary-independently
https://en.cppreference.com/w/cpp/chrono
https://www.geeksforgeeks.org/c-data-types/
https://en.cppreference.com/w/cpp/language/types
https://en.cppreference.com/w/cpp/language/default_constructor
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#cctor-constructors-assignments-and-destructors
https://docs.microsoft.com/en-us/cpp/cpp/scope-resolution-operator?view=msvc-160&viewFallbackFrom=vs-2019
https://visualstudiomagazine.com/Kunk0211
https://github.com/hishamhm/htop
https://www.learncpp.com/
https://www.geeksforgeeks.org/basic-concepts-of-object-oriented-programming-using-c/
https://www.youtube.com/watch?v=ZOKLjJF54Xc
https://www.roberthalf.com/blog/salaries-and-skills/4-advantages-of-object-oriented-programming
https://www.geeksforgeeks.org/differences-between-procedural-and-object-oriented-programming/
https://ms.sapientia.ro/~manyi/teaching/c++/CPP_v1.1.pdf
https://www.youtube.com/playlist?list=PLqCJpWy5FohcehaXlCIt8sVBHBFFRVWsx
https://www.youtube.com/watch?v=-DP1i2ZU9gk
https://www.youtube.com/watch?v=xXXt3htgDok&list=PLrOv9FMX8xJE8NgepZR1etrsU63fDDGxO&index=18
https://www.youtube.com/watch?v=kxKKHKSMGIg&t=651s
https://www.youtube.com/watch?v=iChalAKXffs
https://www.learncpp.com/cpp-tutorial/configuring-your-compiler-warning-and-error-levels/
https://www.viva64.com/en/k/0048/
https://github.com/boostorg/boost/wiki/Guidelines:-Warnings
https://software.intel.com/en-us/cpp-compiler-developer-guide-and-reference-remarks-warnings-and-errors
http://man7.org/linux/man-pages/man5/proc.5.html
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#S-glossary
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#c4-make-a-function-a-member-only-if-it-needs-direct-access-to-the-representation-of-a-class
https://en.cppreference.com/w/cpp/language/operators
https://gcc.gnu.org/
https://www.gnu.org/gnu/thegnuproject.en.html
https://www.gnu.org/software/make/manual/html_node/index.html#Top
https://cmake.org/
https://stackoverflow.com/questions/19736281/what-are-the-differences-between-overriding-virtual-functions-and-hiding-non-vir
https://isocpp.org/wiki/faq/strange-inheritance
http://www.cs.cmu.edu/~motionplanning/reading/PlanningforDynamicVeh-1.pdf
http://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#Rh-override
https://www.onlinegdb.com/
https://github.com/mvirgo/Rideshare-Simulation

References from Computer Vision Nanodegree Program:

https://blogs.nvidia.com/blog/2017/06/09/drone-navigates-without-gps/
https://github.com/udacity/CVND_Exercises
https://www.affectiva.com/
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.html
https://bair.berkeley.edu/blog/2018/05/17/delayed-impact/
https://hbr.org/2018/02/can-we-keep-our-biases-from-creeping-into-ai?utm_campaign=hbr&utm_source=twitter&utm_medium=social
https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms
https://towardsdatascience.com/teaching-cars-to-see-advanced-lane-detection-using-computer-vision-87a01de0424f
https://video.udacity-data.com/topher/2018/June/5b2c01ba_gender-shades-paper/gender-shades-paper.pdf
https://godatadriven.com/blog/fairness-in-machine-learning-with-pytorch/
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_features_harris/py_features_harris.html
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_contours/py_table_of_contents_contours/py_table_of_contents_contours.html
https://pytorch.org/docs/stable/nn.html#pooling-layers
https://pytorch.org/docs/master/optim.html
https://pytorch.org/docs/master/nn.html#loss-functions
https://medium.com/@smallfishbigsea/a-walk-through-of-alexnet-6cbd137a5637
https://experiments.withgoogle.com/what-neural-nets-see
https://github.com/alexisbcook/tsne
https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9
https://github.com/jwyang/faster-rcnn.pytorch
https://vivek-yadav.medium.com/part-1-generating-anchor-boxes-for-yolo-like-network-for-vehicle-detection-using-kitti-dataset-b2fe033e5807
https://pjreddie.com/media/files/papers/YOLOv3.pdf
https://pjreddie.com/darknet/
https://video.udacity-data.com/topher/2018/May/5af0e03b_video-classification/video-classification.pdf
https://en.wikipedia.org/wiki/Vanishing_gradient_problem
https://en.wikipedia.org/wiki/Time_delay_neural_network
https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog1402_1
https://en.wikipedia.org/wiki/Recurrent_neural_network#Elman_networks_and_Jordan_networks
http://www.bioinf.jku.at/publications/older/2604.pdf
https://en.wikipedia.org/wiki/Sepp_Hochreiter
https://people.idsia.ch//~juergen/
https://engineering.fb.com/2016/10/25/ml-applications/building-an-efficient-neural-language-model-over-a-billion-words/
https://arxiv.org/pdf/1511.06939.pdf
http://blog.datumbox.com/tuning-the-learning-rate-in-gradient-descent/
https://cs231n.github.io/neural-networks-3/#loss
http://jalammar.github.io/
https://www.deeplearningbook.org/contents/guidelines.html
https://arxiv.org/abs/1206.5533
http://neuralnetworksanddeeplearning.com/chap3.html#how_to_choose_a_neural_network's_hyper-parameters
http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
https://arxiv.org/abs/1507.05523
https://arxiv.org/abs/1606.02228
https://arxiv.org/abs/1506.02078
https://arxiv.org/pdf/1502.03044.pdf
https://arxiv.org/pdf/1707.07998.pdf
https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S19-04.pdf
https://arxiv.org/pdf/1507.05738.pdf
https://arxiv.org/pdf/1708.02711.pdf
https://arxiv.org/pdf/1607.05910.pdf
https://arxiv.org/abs/1706.03762
https://www.youtube.com/watch?v=rBCqOTEfxvg

References from Bertelsmann Scholarship Introduction to AI in Business Nanodegree Program:

https://cezannec.github.io/Intro_Neural_Networks/
https://www.pnas.org/content/pnas/114/50/13108.full.pdf
https://www.pnas.org/content/pnas/suppl/2017/11/27/1700035114.DCSupplemental/pnas.1700035114.sapp.pdf
https://papers.nips.cc/paper/2016/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf
https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0
https://www.washingtonpost.com/graphics/2018/business/alexa-does-not-understand-your-accent/?noredirect=on&utm_term=.a17219a26daa
https://github.com/topics/video-annotation
https://www.invisionapp.com/design-defined/mockup/
https://www.figure-eight.com/dataset/parking-sign-detection/
https://www.figure-eight.com/datasets/

References from Data Engineering Nanodegree Program:

https://www.postgresqltutorial.com/postgresql-upsert/
https://www.postgresql.org/docs/9.5/sql-insert.html
https://www.datastax.com/blog/allow-filtering-explained
https://www.xenonstack.com/blog/nosql-databases/
https://learnsql.com/blog/companies-that-use-postgresql-in-business/
https://www.w3resource.com/PostgreSQL/foreign-key-constraint.php
https://www.geeksforgeeks.org/introduction-of-dbms-database-management-system-set-1/
https://learn.panoply.io/hubfs/Data%20Engineering%20-%20Introduction%20and%20Epochs.pdf
https://softwareengineering.stackexchange.com/questions/375704/why-should-i-use-foreign-keys-in-database
https://norvig.com/21-days.html

References from the Intel® Edge AI for IoT Developers Nanodegree Program:

https://thegradient.pub/semantic-segmentation/

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