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https://towardsdatascience.com/the-quest-of-higher-accuracy-for-cnn-models-42df5d731faf

Algorithms online Course from PRINCETON UNIVERSITY

About this Course
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

All the features of this course are available for free. It does not offer a certificate upon completion

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https://www.coursera.org/learn/algorithms-part1?ranMID=40328&ranEAID=SAyYsTvLiGQ&ranSiteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&siteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ

Diving into Deep Convolutional Semantic Segmentation Networks and Deeplab_V3

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https://sthalles.github.io/deep_segmentation_network/

Not just another GAN paper — SAGAN – Towards Data Science

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https://towardsdatascience.com/not-just-another-gan-paper-sagan-96e649f01a6b

Deep learning lecture

Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe:


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https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf

Stanford Machine Learning

Content
01 and 02: Introduction, Regression Analysis and Gradient Descent

03: Linear Algebra - review

04: Linear Regression with Multiple Variables

05: Octave[incomplete]

06: Logistic Regression

07: Regularization

08: Neural Networks - Representation

09: Neural Networks - Learning

10: Advice for applying machine learning techniques

11: Machine Learning System Design

12: Support Vector Machines

13: Clustering

14: Dimensionality Reduction

15: Anomaly Detection

16: Recommender Systems

17: Large Scale Machine Learning

18: Application Example - Photo OCR

19: Course Summary

http://www.holehouse.org/mlclass/

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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

paper — arxiv👇👇👇
https://arxiv.org/pdf/1905.08233.pdf

video — youtube👇👇👇
https://www.youtube.com/watch?v=p1b5aiTrGzY

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Diving deeper into Reinforcement Learning with Q-Learning

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https://medium.com/free-code-camp/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe

Decoding the Best Papers from ICLR 2019 – Neural Networks are Here to Rule

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https://www.analyticsvidhya.com/blog/2019/05/best-papers-iclr-2019/

SEVEN NEW COURSES that cover Python, R, and SQL. First up is Analyzing Business Data in SQL, where you’ll learn how to write SQL queries to calculate key business metrics and produce report-ready results. Plus our Introduction to Text Analysis in R course, where you’ll learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models.

Courses :

1. Writing Functions and Stored Procedures in SQL Server

2. Analyzing Business Data in SQL

3. Feature Engineering for Machine Learning in Python

4. Introduction to Seaborn (in Python)

5. Advanced Dimensionality Reduction in R

6. Introduction to Text Analysis in R

7. Intermediate Interactive Data Visualization with plotly in R

1. https://www.datacamp.com/courses/writing-functions-and-stored-procedures-in-sql-server?utm_medium=email&utm_source=customerio&utm_campaign=course_7996

2. https://www.datacamp.com/courses/analyzing-business-data-in-sql?utm_medium=email&utm_source=customerio&utm_campaign=course_15268

3. https://www.datacamp.com/courses/feature-engineering-for-machine-learning-in-python?utm_medium=email&utm_source=customerio&utm_campaign=course_14336

4. https://www.datacamp.com/courses/introduction-to-seaborn?utm_medium=email&utm_source=customerio&utm_campaign=course_15192

5. https://www.datacamp.com/courses/advanced-dimensionality-reduction-in-r?utm_medium=email&utm_source=customerio&utm_campaign=course_10590

6. https://www.datacamp.com/courses/introduction-to-text-analysis-in-r?utm_medium=email&utm_source=customerio&utm_campaign=course_14290

7. https://www.datacamp.com/courses/intermediate-interactive-data-visualization-with-plotly-in-r?utm_medium=email&utm_source=customerio&utm_campaign=course_7193

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TOP 15 PYTHON RESOURCES

1) Learn Python the Hard Way (free ebook)
https://learnpythonthehardway.org/book/

2)Codecademy (free code tutorials)
https://www.codecademy.com/learn/learn-python

3)Google's Python class
https://developers.google.com/edu/python/

4)A Byte of Python (free ebook)
https://python.swaroopch.com

5)TutsPlus tutorial
https://code.tutsplus.com/articles/the-best-way-to-learn-python--net-26288

6)LEARN PYTHON ONLINE: BEST PYTHON
https://mikkegoes.com/learn-python-online-best-resources/

7)Best Python Resources for Beginners and Professionals
https://pythontips.com/2013/09/01/best-python-resources/amp/

8)TutorialsPoint
http://www.tutorialspoint.com/python/

9)Learning Python
https://docs.python-guide.org/intro/learning/

10)Full Stack Python
https://www.fullstackpython.com/best-python-resources.html

11)Python For Beginners
https://www.python.org/about/gettingstarted/

12)Codementor community
https://www.codementor.io/community/topic/python

13)How should I start learning Python?
https://www.quora.com/How-should-I-start-learning-Python-1

14)Codeconquest
https://www.codeconquest.com/blog/the-50-best-websites-to-learn-python/

15)Python for Beginners
https://www.pythonforbeginners.com

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1. 10 New Things I Learnt from fast.ai v3
2. 2019 deep learning course Practical Deep Learning for Coders, v3.

10 learning points as such:

1. The Universal Approximation Theorem
2. Neural Networks: Design & Architecture
3. Understanding the Loss Landscape
4. Gradient Descent Optimisers
5. Loss Functions
6. Training
7. Regularisation
8. Tasks
9. Model Interpretability
10. Appendix: Jeremy Howard on Model Complexity & Regularisation

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https://towardsdatascience.com/10-new-things-i-learnt-from-fast-ai-v3-4d79c1f07e33

Free 6-Hour Data Science Course for Beginners

This course covers:

* foundations of data science
* data sourcing
* coding for data scientists
* mathematics for data scientists
* statistics

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https://www.freecodecamp.org/news/data-science-course-for-beginners/

Implement Back Propagation in Neural Networks

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https://medium.com/coinmonks/implement-back-propagation-in-neural-networks-ed09897593e7

Review: FCN — Fully Convolutional Network (Semantic Segmentation)
Covered:
* From Image Classification to Semantic Segmentation
* Upsampling Via Deconvolution
* Fusing the Output
* Results

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https://towardsdatascience.com/review-fcn-semantic-segmentation-eb8c9b50d2d1