“Much Needed Mathematics for Machine Learning Algorithms” by Kolla Leela Kishan https://link.medium.com/SWIrEa5FLZ

# Tag Archives: machine learning

# Math in Machine Learning

“Certainly having a strong background in mathematics (eg. Linear Algebra, Multi-variables Calculus, Baeysian Probability, etc) will make it easier to understand machine learning at a

conceptuallevel.“

“If the math seems tough, focus on the practical first, learn through analogies and by building something yourself.

But if the math comes easy, you’re starting with a solid foundation.”

# Mathematics for Machine Learning full Course || Linear Algebra || Part-1

IMPERIAL COLLEGE OF LONDIN

# Machine Learning is Just Mathematics! Free Machine Learning Resources

# Make your pictures beautiful with a touch of machine learning magic

Make your pictures beautiful with a touch of machine learning magic.

# Machine Learning is Fun! – Adam Geitgey – Medium

https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471

(中文) :

https://zhuanlan.zhihu.com/p/24339995

Unsupervised learning is the future **ML** **(Machine Learning)** – of which **AI** is a branch – with the latest algorithm **Deeplearning** showing only 5% of its potential (more yet to be invented).

Singapore has recently launched an AI program to educate 10,000 students & workers. (Partnership with Microsoft and IBM, a 3-hour free lesson).

The world’s 4 AI gurus :

- (UK/Canada) Prof Geoffrey Hinton (*) , the inventor of DeepLearning, and
- his post-doctorate associate (France) Prof Yann Lecun ,
- The ex-Google & ex-Baidu AI Chief Prof Andrew NG 吴恩达,
- The AlphaGo creator Demis Hassabis

**Note**:

Andrew and Demis both studied in Singapore secondary schools (NG in Raffles Institution) before pursuing university in Stanford and Cambridge, respectively.

Note (*) : Prof Geoffrey Hinton was involved in the 80s Expert Systems where rule-based knowledge engine was the AI (2.0) . This AI failed because of fixed rules knowledge base under “**supervised learning**” from human domain experts, who each differed from another in opinions, to give an un-biased “weights” (rule probabilities from 0 to 1). Prof Hinton continued the AI research by moving from UK to Canada, where he developed the Deeplearning algorithm with **unsupervised learning** from Big Data Training feed to calculate the “Costs” (ie deviations of AI result versus actual result, using Cauchy’s Calculus eg. “Gradient Descent”, etc).

https://tomcircle.wordpress.com/2018/01/20/ai-deeplearning-machine-learnung/