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Support Vector Machine (SVM) Theory
In this tutorial, we'll describe the mathematics behind Support Vector Machines. This tutorial is going to be much more math heavy as compared to other tutorials. If you're mostly interested in applying SVMs to solve problems, then our first tutorial on SVMs is sufficient. However, if you would like to understand the mathematical basis of Support Vector Machines, then you'll find this tutorial interesting.
In this tutorial, we will focus on the hard-margin SVM and soft-margin SVM. However, we will not be considering kernels or the hyper-parameter \gamma (gamma).
In SVM, the decision function for predicting data points x corresponds to the equation of an hyperplane:
Dropout is a widely used regularization technique for neural networks. Neural networks, especially deep neural networks, are flexible machine learning algorithms and hence prone to overfitting. In this tutorial, we'll explain what is dropout and how it works, including a sample TensorFlow implementation.
If you [have] a deep neural net and it's not overfitting, you should probably be using a bigger one and using dropout, ... - Geoffrey Hinton 
Dropout is a regularization technique where during each iteration of gradient descent, we drop a set of neurons selected at random. By drop, what we mean is that we essentially act as if they do not exist.
This list consists of ~10 tutorials to learn bioinformatics. You can think of this list as a "Free Online Nano Book". We'll cover important bioinformatics topics, learning (a) the biological significance of each problem, and (b) the computational algorithms used to solve the problem. Everything is 100% free.
What is Bioinformatics?
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What is a DNA? What is a Protein?
This section (single tutorial) provides the biological context for the rest of this course. It describes what DNAs and proteins are, and how we can model them as a sequence of characters.
Conserved Sequences and Regulatory Motifs with Brute-Force Algorithms and Randomized Algorithms
Conserver sequences and regulatory motifs are short sequences (say of length 15-30) which occur frequency in the genome. These sequences serve a variety of functions - such as regulating gene expression (and hence how much of a protein is produced) and indicating where genes begin. We'll see how to find these sequences using brute-force algorithms and randomized algorithms.
Genome Assembly and Sequence Alignment with Graph Algorithms and Dynamic Programming
Recently, we've figured out cost effective ways of sequencing a human genome, i.e. taking a human genome and reading sequence of 3-4 billion nucleic acids (A, C, G and T) that it comprises of. In this section, we'll see how this is achieved. We'll also see how we can find similar regions in two different genomes, which allows us to do things like infer evolutionary history and predict protein function.
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DNA and Protein: What they are, and what role do they play?
Machine learning is a part of artificial intelligence that we use so that computers can do things without being directly programmed. Machine learning focuses on the study of algorithms and data that allows the system to make decisions without writing manual code.
It is tough to mention the best programming language for machine learning. There are many factors to select a programming language in machine learning, but an essential element is the type of project.
Here is a list of programming languages used in machine learning.