100 days of machine learning. Challenge accepted!
Day 0. Continuing the deep learning certification track at Coursera. I am on course 1 ("Neural Networks and Deep Learning"), week 3. Thoughts: I really enjoy Dr. Andrew Ng's lectures; they are thorough, well-organized, and enjoyable. I can't link to coursework due to Coursera's honor code, but will add links to my own projects later in this challenge.
Day 1. Still working on week 3 of the Coursera "Neural Networks and Deep Learning" course. Thoughts: I'm getting so much out of Dr. Ng's lectures. He goes through each step of the process, and decomposes the mathematics in such a way that I am developing an intuitive understanding of neural networks.
Day 2. Today was the Native POP (People of the Plains) festival, and I spent the day visiting with friends and relations instead of working. Thoughts: I made a committment, so although I'm tired I will at least watch one video. Siraj Raval posted a video yesterday about coding an API for machine learning, so I'm going to watch that. Link: https://bit.ly/2UeyxQ1
Day 3. Today I am watching Siraj Raval's video over Capsule Networks, which were first proposed by the amazing Geoffrey Hinton. Thoughts: This is of importance to me as I'm currently working mostly with convolutional neural nets. Link: https://www.youtube.com/watch?v=VKoLGnq15RM
Day 4. The quiz and programming assignment for "Neural Networks and Deep Learning" on Coursera finally opened today, so I'm working on those. Thoughts: I can't share anything, because it would violate the honor code, but I am enjoying the programming assignment.
Day 5. Summer in the Lakota Homelands is gathering time - for ceremonies, festivals, and visiting. Tomorrow I am feeding 20+ people in my home, so I have little time for work today or tomorrow. Thoughts: I'm browsing through the Twitter feed for #100DaysOfMLCode and wow! So many inspiring posts. I'll retweet some of my favorites for the next two days.
Day 6. Today I watched Siraj Raval's video over loss functions. Thoughts: This is review for me, but nevertheless I always learn something new from Siraj's videos. I found this one to be cogent, entertaining, and engaging as always. Link: https://youtu.be/IVVVjBSk9N0
Day 7. Watched Siraj Raval's Bitcoin Trading Bot video (from week 3 of his Machine Learning Journey playlist). Thoughts: Finance is outside my scope of knowledge, but time-series modeling is something we use a lot in hydrology so I was able to follow along. Link: https://youtu.be/F2f98pNj99k
Day 8. Still busy with summer obligations, so I've been watching videos. Today was Siraj Raval's Serverless Computing with Google Cloud and AWS Training video. Thoughts: My mind is popping with the possibilities. I'm going to spend more time going over this useful material.
Links: https://youtu.be/tdhVXKf_WSs
https://youtu.be/zkzED9HvMG0
Day 9. Today I read "Connecting the Dots for a Deep Learning App" - a blog post by Janardhan Shetty. Thoughts: I enjoyed this blog post, particularly because I am interested in different ways of using CNNs (relevant to my dissertation). Link: https://towardsdatascience.com/connecting-the-dots-for-a-deep-learning-app-324e4648720a
Day 10. Finishing up week 3 of the first course in the Deep Learning Specialization on Coursera ("Neural Networks and Deep Learning"). Thoughts: Truly enjoying this course by Andrew Ng. His explanations are very thorough and I'm learning a lot. Link: https://www.coursera.org/learn/neural-networks-deep-learning/home/welcome
Day 11. Started week 4 of Andrew Ng's "Neural Networks and Deep Learning" course. Thoughts: I have learned so much from the two courses I've worked through as part of the Deep Learning specialization. Looking forward to working through them all.
Day 12. Still working on week 4 of Andrew Ng's "Neural Networks and Deep Learning" course- quiz and programming exercises. Thoughts: Great course - definitely recommend.
Day 13. Finished Andrew Ng's "Neural Networks and Deep Learning" course with a grade of 92.8%. Thoughts: I put a lot of effort into this course and it was worth every bit. If you want to dig deep into the math and logic behind the code, this is a great resource.
Day 14. Finished week 5 of Siraj Raval's "Machine Learning" syllabus. Thoughts: I appreciate Siraj's ability to organize and communicate machine learning and deep learning concepts. This syllabus has something for learners at every level. Link: https://github.com/llSourcell/Machine_Learning_Journey
Day 15. I am happy to have been chosen to represent the School of AI in my hometown. Join our local chapter to learn more (https://bit.ly/2LNdqex) or visit the School of AI online (for chapters worldwide). Link: https://www.theschool.ai
Day 16. I am working through week 1 of Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Thoughts: This is the second course in Coursera's Deep Learning specialization, taught by Dr. Andrew Ng. These are rigorous courses over the math and programming behind deep learning. Highly recommended. Link: https://bit.ly/2LPvGE0
Day 17. Working on week 2 of Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by Andrew Ng on Coursera. Thoughts: This week is over various optimization techniques, which is useful. Link: https://bit.ly/2LPvGE0
Day 18. Finished Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization on Coursera with a grade of 97.6%. Thoughts: I am now 40% through the Deep Learning specialization on Coursera. Link: https://bit.ly/2LPvGE0
Day 19. Finished week one of Structuring Machine Learning Projects on Coursera. Thoughts: This is the third course in the Deep Learning specialization. I appreciate listening to and learning from the body of knowledge Dr. Ng has built up over his career. Link: https://bit.ly/2RxLsHU
Day 20. Read Simple Beginner’s guide to Reinforcement Learning & its implementation on Analytics Vidya. Thoughts: This is a nicely organized introduction to reinforcement learning using the Keras library. Link: https://bit.ly/2k4aInG
Day 21. Finished week two of Structuring Machine Learning Projects on Coursera with a grade of 95.8%. Thoughts: This is the third course in the Deep Learning specialization. There is a lot of excellent information this week, so I went through the material a few times. Link: https://bit.ly/2RxLsHU
Day 22. Finished Structuring Machine Learning Projects on Coursera. Thoughts: This is the third course in the Deep Learning specialization. There is a lot of excellent information in this course. Link: https://bit.ly/2RxLsHU
Day 23. Completed Week 1 of Convolutional Neural Networks on Coursera. Thoughts: It took some time to understand shape changes due to convolution - definitely worthwhile to watch the videos a couple times. Link: https://bit.ly/2symYmQ
Day 24. Working on week 2 of Convolutional Neural Networks on Coursera. Thoughts: Inception network architecture is fascinating. I'm also glad to be using Keras for the programming exercises. Link: https://bit.ly/2symYmQ
Day 25. Completed week 3 of Convolutional Neural Networks on Coursera. Thoughts: Programming exercise using TensorFlow this week. Link: https://bit.ly/2symYmQ
Day 26. Completed week 4 of Convolutional Neural Networks on Coursera. Thoughts: Face recognition tasks are a lot of fun! Link: https://bit.ly/2symYmQ
Day 27. Completed Convolutional Neural Networks on Coursera with a grade of 96.7%. Thoughts: My favorite course in the Deep Learning specialization so far Link: https://bit.ly/2symYmQ
Day 28. Began week 1 of Sequence Models on Coursera. Thoughts: This is the final course in the Deep Learning specialization - I'm excited! Link: https://bit.ly/2Twm0TO
Day 29. Finished week 1 of Sequence Models on Coursera. Thoughts: Recurrent neural networks are a bit more opaque compared to some other types. Link: https://bit.ly/2Twm0TO
Day 30. Started week 2 of Sequence Models on Coursera. Thoughts: I'm not sure I'll be using RNNs in the immediate future, but I'm happy to be learning more about them. Link: https://bit.ly/2Twm0TO
Day 31. Finished week 2 of Sequence Models on Coursera. Thoughts: A very challenging week of lectures and assignments. Link: https://bit.ly/2Twm0TO
Day 32. Started week 3 of Sequence Models on Coursera. Thoughts: This looks like a fun week as far as programming assignments. Link: https://bit.ly/2Twm0TO
Day 33. Finished week 3 of Sequence Models on Coursera. Thoughts: The Trigger Word Detection assignment is one of my favorites of the entire specialization. Link: https://bit.ly/2Twm0TO
Day 34. Finished Sequence Models on Coursera with a grade of 98.3%. Thoughts: This was, for me anyway, the most challenging course in the series. I had to work hard to grasp some of the concepts, but I feel like I learned a lot. Link: https://bit.ly/2Twm0TO
Day 35. Finished the Deep Learning Specialization on Coursera. Thoughts: I'm incredibly grateful to Dr. Andrew Ng for producing the courses in the Deep Learning Specialization. He's a gifted thinker, educator, and communicator. Highly recommend. Link: https://bit.ly/2Twm0TO
Day 36. Working on AnalyticsVidhya's "Comprehensive Learning Path for Deep Learning in 2019". Thoughts: Straightforward, good for beginners, well-organized. Link: https://bit.ly/2BEW15y
Day 37. Working on the Linux project on DataCamp. Thoughts: A quick, easy project in Python. Link: https://www.datacamp.com/projects/111
Day 38. Working on GoT network analysis project on DataCamp. Thoughts: A fun project using networkx and pandas. Link: https://www.datacamp.com/projects/76
Day 39. Completed the Word Frequency in Moby Dick project on Data Camp. Thoughts: A quick, easy project in Python. Link: https://www.datacamp.com/projects/38
Day 40. Working on AnalyticsVidhya's "Comprehensive Learning Path for Deep Learning in 2019". Thoughts: I read through the regression and regularization sections. Link: https://bit.ly/2BEW15y
Day 41. Completed Week 1 of Data Lit on School of AI. Thoughts: Accessible, entertaining introduction course. Link: https://bit.ly/2IWPbyn
Day 42. Completed Week 2 of Data Lit on School of AI. Thoughts: . I've been in academia for years and this week has one of the most accessible introductions to statistical concepts that I've seen. Link: https://bit.ly/2IWPbyn
Day 43. Completed 'Naïve Bees: Deep Learning with Images' project on DataCamp. Thoughts: A fun little project building a CNN with Keras. Link: https://projects.datacamp.com/projects/555
Day 44. Completed 'Exploring 67 years of LEGO' project on DataCamp. Thoughts: Very quick, easy project. Link: https://www.datacamp.com/projects/10
Day 45. Completed 'Dr. Semmelweis and the Discovery of Handwashing' project on DataCamp. Thoughts: Fascinating Bootstrap analysis. Link: https://www.datacamp.com/projects/20
Day 46. Completed 'The Hottest Topics in Machine Learning' project on DataCamp. Thoughts: Natural Language Processing using sklearn. Link: https://www.datacamp.com/projects/158
Day 47. Completed 'A New Era of Data Analysis in Baseball' project on DataCamp. Thoughts: Yankees and Statcast - awesome! Link: https://www.datacamp.com/projects/250
Day 48. Completed 'Naïve Bees: Image Loading and Processing' project on DataCamp. Thoughts: Enjoyable project creating an image processing pipeline. Link: https://projects.datacamp.com/projects/374
Day 49. Completed 'Naïve Bees: Predict Species from Images' project on DataCamp. Thoughts: Enjoyable project training a CNN. Link: https://projects.datacamp.com/projects/412
Day 50. Completed 'ASL Recognition with Deep Learning' project on DataCamp. Thoughts: Nice CNN project. Link: https://www.datacamp.com/projects/509
Day 51. Completed Natural Language Processing Fundamentals in Python course on DataCamp. Thoughts: I've taken other courses on this topic, but none were as clear and engaging as this one is. Link: https://bit.ly/2NRIzjS
Day 52. Completed 'Reducing Traffic Mortality in the USA' project on DataCamp. Thoughts: Inconclusive analysis, nice plots in seaborn. Link: https://projects.datacamp.com/projects/462
Day 53. Completed 'Do Left-handed People Really Die Young?' project on DataCamp. Thoughts: Bayesian analysis using pandas. Link: https://projects.datacamp.com/projects/479
Day 54. Completed 'A Visual History of Nobel Prize Winners' project on DataCamp. Thoughts: Could have been improved by normalizing data. Link: https://projects.datacamp.com/projects/441
Day 55. Completed 'The GitHub History of the Scala Language' project on DataCamp. Thoughts: Lots of data manipulation using pandas. Link: https://projects.datacamp.com/projects/163
Day 56. Completed Building Chatbots in Python tutorial. Thoughts: Fun and informative - from early implementations up through trained neural networks. Link: https://www.datacamp.com/courses/building-chatbots-in-python
Day 57. Completed 'Who Is Drunk and When in Ames, Iowa?' project on DataCamp. Thoughts: Easy plotting project. Link: https://projects.datacamp.com/projects/475
Day 58. Completed Week 3 of Data Lit on School of AI. Thoughts: A wealth of information about data visualization. Link: https://bit.ly/2IWPbyn
Day 59. Completed Week 4 of Data Lit on School of AI. Thoughts: Regression! One of my favorite topics. Link: https://bit.ly/2IWPbyn
Day 60. Completed 'Bad passwords and the NIST guidelines' project on DataCamp. Thoughts: Using regex for pull bad passwords out of a pandas dataframe. Link: https://projects.datacamp.com/projects/141
Day 61. Completed 'Classify Song Genres from Audio Data' project on DataCamp. Thoughts: Logistic regression, sklearn. Link: https://projects.datacamp.com/projects/449
Day 62. Completed 'Generating Keywords for Google Ads' project on DataCamp. Thoughts: Pretty basic and a bit buggy. Link: https://projects.datacamp.com/projects/400
Day 63. Completed 'Recreating John Snow's Ghost Map' project on DataCamp. Thoughts: Early geospatial analysis applied to identify a cholera outbreak. Link: https://projects.datacamp.com/projects/132
Day 64. Completed 'Mobile Games A/B Testing with Cookie Cats' project on DataCamp. Thoughts: Basic and straightforward. Link: https://projects.datacamp.com/projects/184
Day 65. Completed 'Name Game: Gender Prediction using Sound' project on DataCamp. Thoughts: Using fuzzy package for sound matching. Link: https://projects.datacamp.com/projects/97
Day 66. Completed 'Risk and Returns: The Sharpe Ratio' project on DataCamp. Thoughts: Using pandas and the Sharpe Ratio to calculate and compare "profitability". Link: https://projects.datacamp.com/projects/66
Day 67. Completed 'Introduction to Linear Modeling in Python' on DataCamp. Thoughts: A well-rounded course in linear modeling with good visualizations. Link: https://bit.ly/2WI24yq
Day 68. Completed 'Exploring the Bitcoin Cryptocurrency Market' project on DataCamp. Thoughts: Interesting little analysis of bitcoin volatility on a particular day. Link: https://projects.datacamp.com/projects/82
Day 69. Completed Week 5 of Data Lit on School of AI. Thoughts: A decent overview of supervised learning for beginners.
Link: https://bit.ly/2IWPbyn
Day 70. Completed 'Visualizing Geospatial Data in Python' on DataCamp. Thoughts: My bread and butter. I was looking for a good course for my students, and this one is good! Link: https://bit.ly/2HJZ3ub
Day 71. Completed 'TV, Halftime Shows, and the Big Game' on DataCamp. Thoughts: Really basic. Link: https://www.datacamp.com/projects/684
Day 72. First day of the Software Engineering Assembly at UCAR/NCAR - Improving Scientific Software. Thoughts: Today's theme is high performance computing - my favorite lecture was by Craig Tierney from NVIDIA over GPU-based clusters for DL. Link: https://sea.ucar.edu/conference/2019
Day 73. Day 2 of the Software Engineering Assembly at UCAR/NCAR - Improving Scientific Software. Thoughts: Today's theme was machine learning - great lectures all day. Link: https://sea.ucar.edu/conference/2019
Day 74. Day 3 of the Software Engineering Assembly at UCAR/NCAR - Improving Scientific Software. Thoughts: Today's theme was containers for HPC. Link: https://sea.ucar.edu/conference/2019
Day 75. Day 4 of the Software Engineering Assembly at UCAR/NCAR - Improving Scientific Software. Thoughts: Excellent tutorials from Penn State and Steve Farrell at Berkeley Lab and Link: https://sea.ucar.edu/conference/2019
Day 76. Day 5 of the Software Engineering Assembly at UCAR/NCAR - Improving Scientific Software. Thoughts: Second day of tutorials - DL for severe weather prediction by my mentor David John Gagne from NCARs CISL. Link: https://sea.ucar.edu/conference/2019
Day 77. Completed "April" reading for deep learning 2019 path on Analytics Vidya. Thoughts: Good written explanations of introductory topics in machine learning. Link: https://bit.ly/2IKN9zC
Day 78. Completed Week 8 of Data Lit on School of AI. Thoughts: Continuation of unsupervised learning methods. Link: https://bit.ly/2vez7i3
Day 79. Completed Week 9 of Data Lit on School of AI. Thoughts: GANS and Transformer networks. Link: https://bit.ly/2vez7i3
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