1.【博客】A comprehensive beginners guide to Linear Algebra for Data Scientists
Table of contents
- Motivation – Why learn Linear Algebra?
- Representation of problems in Linear Algebra
2.1. Visualising the problem: Line
2.2. Complicate the problem
3.1 Terms related to Matrix
3.2 Basic operations on Matrix
3.3 Representing in Matrix form
- Solving the problem
4.1. Row Echelon form
4.2. Inverse of a Matrix
4.2.1 Finding Inverse
4.2.2 The power of Matrices: solving the equations in one go
4.2.3 Use of Inverse in Data Science
- Eigenvalues and Eigenvectors
5.1 Finding Eigenvectors
5.2 Use of Eigenvectors in Data Science: PCA algorithm
- Singular Value Decomposition of a Matrix
- End Notes
2.【博客】Image classification Api — deep learning
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
3.【博客】Applying deep learning to real-world problems
deep learning. Three major drivers caused the breakthrough of (deep) neural networks: the availability of huge amounts of training data, powerful computational infrastructure, and advances in academia. Thereby deep learning systems start to outperform not only classical methods, but also human benchmarks in various tasks like image classification or face recognition. This creates the potential for many disruptive new businesses leveraging deep learning to solve real-world problems.
4.【博客】Deep, Deep Trouble
I am really confused. I keep changing my opinion on a daily basis, and I cannot seem to settle on one solid view of this puzzle. No, I am not talking about world politics or the current U.S. president, but rather something far more critical to humankind, and more specifically to our existence and work as engineers and researchers. I am talking about…deep learning.
While you might find the above statement rather bombastic and overstated, deep learning indeed raises several critical questions we must address. In the following paragraphs, I hope to expose one key conflict related to the emergence of this field, which is relevant to researchers in the image processing community.
5.【博客】Fitting Gaussian Process Models in Python
A common applied statistics task involves building regression models to characterize non-linear relationships between variables. It is possible to fit such models by assuming a particular non-linear functional form, such as a sinusoidal, exponential, or polynomial function, to describe one variable's response to the variation in another. Unless this relationship is obvious from the outset, however, it involves possibly extensive model selection procedures to ensure the most appropriate model is retained. Alternatively, a non-parametric approach can be adopted by defining a set of knots across the variable space and use a spline or kernel regression to describe arbitrary non-linear relationships. However, knot layout procedures are somewhat ad hoc and can also involve variable selection. A third alternative is to adopt a Bayesian non-parametric strategy, and directly model the unknown underlying function. For this, we can employ Gaussian process models.