nanodegree

Watch this repo to know set of topics you need to cover before jumping to learn machine learning. Happy Learning!

Some online MOOCs and materials for studying some of the Mathematics topics needed for Machine Learning are:

• Khan Academy’s Linear Algebra, Probability & Statistics, Multivariable Calculus and Optimization.
  • https://www.khanacademy.org/math/linear-algebra
  • https://www.khanacademy.org/math/multivariable-calculus
  • https://www.khanacademy.org/math/differential-calculus
  • https://www.khanacademy.org/math/statistics-probability
• MIT Lecture : https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/
• Digital Learning Hub - Imperial College London: https://www.youtube.com/channel/UCSzae1ITUdw9DCdELMduaQw/playlists
• Coding the Matrix: Linear Algebra through Computer Science Applications by Philip Klein, Brown University.
• https://www.coursera.org/specializations/mathematics-machine-learning
• Linear Algebra — Foundations to Frontiers by Robert van de Geijn, University of Texas.
• Applications of Linear Algebra, Part 1 and Part 2. A newer course by Tim Chartier, Davidson College.
• Joseph Blitzstein — Harvard Stat 110 lectures.
• Larry Wasserman’s book — All of statistics: A Concise Course in Statistical Inference.
• Boyd and Vandenberghe’s course on Convex optimization from Stanford.
• Linear Algebra — Foundations to Frontiers on edX.
• Udacity’s Introduction to Statistics.
• 3Blue1Brown: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
• Math Tutorial: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw/channels

Some online MOOCs and materials for studying Machine Learning are:

• A quick FREE course of AI https://course.elementsofai.com
• Numpy Library : https://jakevdp.github.io/PythonDataScienceHandbook/
• Machine Learning Resource: https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal
• Neural Network: https://www.youtube.com/channel/UC4UJ26WkceqONNF5S26OiVw
• Data Science : https://towardsdatascience.com/
• Activation Function : http://primo.ai/index.php?title=Activation_Functions
• Activation function and its type: https://towardsdatascience.com/activation-functions-and-its-types-which-is-better-a9a5310cc8f
• Logistic Gradient Descent : https://www.youtube.com/watch?v=z_xiwjEdAC4
• Perceptron Algorithm :https://www.youtube.com/watch?v=jbluHIgBmBo&feature=youtu.be
• Dropout Algorithm : https://www.youtube.com/watch?v=ARq74QuavAo , https://www.youtube.com/watch?v=XmLYl17DbbA [***************]
• Softmax VS Sigmoid : https://medium.com/aidevnepal/for-sigmoid-funcion-f7a5da78fec2
• Optimization Algorithm: https://towardsdatascience.com/types-of-optimization-algorithms-used-in-neural-networks-and-ways-to-optimize-gradient-95ae5d39529f
• First Research paper on Dropout: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
• CNN intro : https://www.youtube.com/watch?v=YRhxdVk_sIs
• CNN by Andrew Ng https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF
• CNN intro : https://www.youtube.com/watch?v=FmpDIaiMIeA
• Max Pooling : https://www.youtube.com/watch?v=ZjM_XQa5s6s
• CNN Architecture
  • https://www.youtube.com/watch?v=VKoLGnq15RM
  • https://medium.com/analytics-vidhya/cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5
  • https://towardsdatascience.com/illustrated-10-cnn-architectures-95d78ace614d#81e0
• Stacked LSTM: https://machinelearningmastery.com/stacked-long-short-term-memory-networks/
• Basics of RNN - https://www.youtube.com/watch?v=ogZi5oIo4fI&t=1505s
• LSTM With animation:
  • https://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45
  • https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
• MatplotLib :
  • https://www.youtube.com/watch?v=qErBw-R2Ybk
  • https://matplotlib.org/tutorials/introductory/usage.html#sphx-glr-tutorials-introductory-usage-py
  • https://matplotlib.org/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py
• Binray Cross Entropy :https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a
• LSTM Reads
  • http://colah.github.io/posts/2015-08-Understanding-LSTMs/
  • http://blog.echen.me/2017/05/30/exploring-lstms/
  • https://www.youtube.com/watch?v=iX5V1WpxxkY

Interesting Medium articles

• Deep learning for specific information extraction from unstructured texts : https://towardsdatascience.com/deep-learning-for-specific-information-extraction-from-unstructured-texts-12c5b9dceada

SOME BOOK's LINKS

• Pytorch : https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf

Useful Github Project links

• ML Project Repos : https://github.com/Kulbear?tab=repositories

Books to Read

• Grokking Deep Learning by Andrew Trask. Use our exclusive discount code traskud17 for 40% off. This provides a very gentle introduction to Deep Learning and covers the intuition more than the theory.
• Neural Networks And Deep Learning by Michael Nielsen. This book is more rigorous than Grokking Deep Learning and includes a lot of fun, interactive visualizations to play with.
• The Deep Learning Textbook from Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This online book contains a lot of material and is the most rigorous of the three books suggested.

Interesting Read

• Fortune Magazine : http://fortune.com/2017/01/26/stanford-ai-skin-cancer/
• Bloomberg: https://www.bloomberg.com/news/articles/2017-06-29/diagnosing-skin-cancer-with-google-images
• BBC: http://www.bbc.com/news/health-38717928
• Wall Street Journal : https://www.wsj.com/articles/computers-turn-medical-sleuths-and-identify-skin-cancer-1486740634?emailToken=JRrzcPt+aXiegNA9bcw301gwc7UFEfTMWk7NKjXPN0TNv3XR5Pmlyrgph8DyqGWjAEd26tYY7mAuACbSgWwvV8aXkLNl1A74KycC8smailE=
• Forbes : https://www.forbes.com/sites/forbestechcouncil/2017/09/27/what-can-computer-vision-do-in-the-palm-of-your-hand/#4d2c686847a7 Scientific American: https://www.scientificamerican.com/article/deep-learning-networks-rival-human-vision1/

Papers:

https://www.scientificamerican.com/article/deep-learning-networks-rival-human-vision1/ ELMAN Network https://doi.org/10.1207/s15516709cog1402_1

Youtube Videos:

AI, Deep Learning, and Machine Learning: A Primer