Friday, November 1, 2019
More and more cities are looking to go green. And renewable energy is, if current trends hold, the future of the energy industry.
Monday, October 28, 2019
Performing Repetitive Analysis
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Creation of Drugs
Health Management Apps
6 Steps to Bring Clarity to Industrial IoT (IIoT) Automation
“The key to artificial intelligence has always been the representation.” — Jeff Hawkins
As we know Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with the self-driving cars, medical diagnosis and even betting humans at strategy games like Go and Chess.
The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge.
Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this article, I’ve covered exactly that. Idesigned a complete articleto help you master the mathematical foundation required for writing programs and algorithms for AI and ML.
So I will directly go to the main objective of this article:
My recommendation of learning mathematics for AI goes like this:
Linear algebra is used in Machine Learning to describe the parameters and structure of different machine learning algorithms. This makes linear algebra a necessity to understand how neural networks are put together and how they are operating.
It cover topics like this:
- Scalars, Vectors, Matrices, Tensors
- Matrix Norms
- Special Matrices and Vectors Eigenvalues and Eigenvectors
- Principle component analysis
- Singular value decomposition
This is used to supplement the learning part of machine learning. It is what is used to learn from examples, update the parameters of different models and improve the performance.
It cover topics like this:
- Derivatives(Scalar Derivative-Chain rule),Partial and Directional Derivative.
- Differential Operators
- Convex Optimization
- Gradient algorithms- local/global maxima and minima,SGD,NAG,MAG,Adams
The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms. It is important for us to understand the key probability distributions.
It covers topics such as:
- Elements of Probability
- Random Variables
- Distributions( binomial, bernoulli, poisson, exponential, gaussian)
- Variance and Expectation
- Bayes’ Theorem, MAP, MLE
- Special Random Variables
- Markov Chain
- Information Theory
From where you can learn:
- Youtube Videos
- Online Course
- Google Search
Reading above topics, you will not have not only the knowledge to build your own algorithms, but also the confidence to actually start putting your algorithms to use in your next projects and learn exacly how to use concepts in real life.
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