Python For Data Science - Jupyter Notebook

Source: https://www.datacamp.com/community/data-science-cheatsheets

Python For Data Science - NumPy Basics

Source: https://www.datacamp.com/community/data-science-cheatsheets

Machine Learning Modelling in R

Source: https://www.rstudio.com/resources/cheatsheets/

Deep Learning with Keras Cheat Sheet

Source: https://www.rstudio.com/resources/cheatsheets/

Search Algorithms

Types of Search

Uninformed Search

It uses no domain knowledge. The basic strategies include

• Breadth-first search (BFS): expand shallowest node first (node having the lowest depth)
• Depth-first search (DFS): expand deepest node first
• Depth-limited search (DLS): depth first with increasing level
• Iterative-deepening search (IDS): depth limited with increasing limit
• Uniform-cost search (UCS): expand least cost node

Informed Search

It uses domain knowledge. The basic strategies include

• Best first search (Greedy search): expand the node that appears to be closet to goal
• A* search: minimize the total estimated solution cost

Intelligent agent (IA)

What is Intelligent Agent (IA)?

Agent = Architecture + Program

Architecture = infrastructure to host the agent program, e.g. physical sensors, computing devices

Program = the logic and algorithms

In general, IA contains the sensors to precept the environment, think how to do, and then act through the actuators.

[Wikipedia] In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is "rational", as defined in economics[1]). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex. A reflex machine, such as a thermostat, is considered an example of an intelligent agent.

Type of IA

• simple reflex agents
• model-based reflex agents
• goal-based agents
• utility-based agents
• learning agents

Intelligence Level

Simple Reflex Agents

Environment: fully observable

Agent Function: based on condition-action rule

Model-based Reflex Agents

Environment: partially observable

Precepts -> State -> Internal Model -> Action

Goal-based Agents

Environment: partially observable

Precepts -> State (Goal or Non-Goal) -> Internal Model -> Action

Utility-based Agents

Environment: partially observable

Similar to goal-based, and additionally use utility function to determine which actions is better

Learning Agents

Environment: can be unknown

Learning from its experiences and knowledge to achieve their goal.

R Programming Cheat Sheet (the basics)

Source: http://datasciencefree.com/cheatsheets.html

Advanced R Cheat Sheet

Source: http://datasciencefree.com/cheatsheets.html

R Programming Cheat Sheet (advanced)

Source: http://datasciencefree.com/cheatsheets.html

Numpy Cheat Sheet

Source: http://datasciencefree.com/cheatsheets.html

Data Analysis with PANDAS

Source: http://datasciencefree.com/cheatsheets.html

Python Cheat Sheet (the Basics)

Source: http://datasciencefree.com/cheatsheets.html

Artificial Intelligence Overview

Can machines think? In 1950, Alan Turing published "Computing Machinery and Intelligence" and he asked the question "Can machines think?". Instead of answering this question directly, Alan Turing proposed an alternate question "Can a machine behave intelligently so that a human interrogator can not tell whether it is a human or a machine?".

In 1955, John McCarthy together with Marvin Minsky, Allen Newell and Herbert A. Simon coined the term "artificial intelligence", and organized the famous Dartmouth Conference in Summer 1956.

Around 2010, with the availability of big data, dramatically improved machine learning approaches and algorithms, and more advanced computing power, the industry has been increasing much investment in AI and many AI applications become production in this decade.

What is Artificial Intelligence?

The machine is considered to have AI, when it can

• think humanly: the cognitive modeling approach
• think rationally: the "laws of thought" approach
• acting humanly: the Turing Test approach
• acting rationally: the rational agent approach

In Wikipedia, Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". Source: https://en.wikipedia.org/wiki/Artificial_intelligence

Types of AI

We can categorize AI into 2 types: weak AI and strong AI.

Weak AI (also known as narrow AI): an AI system is designed and trained for particular tasks
Strong AI (also known as artificial general intelligence): an AI system with generalized human cognitive abilities so that it has enough intelligence to find a solution and perform unfamiliar task.

Neutral Network Graphs

Source: http://www.asimovinstitute.org/blog/

Machine Learning Overview

What is Machine Learning?

Below are the definition of Machine Learning we googled.

• [Wikipedia] Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.
• [Nvidia] Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.
• [Stanford] Machine learning is the science of getting computers to act without being explicitly programmed.
• [McKinsey & Co.] Machine learning is based on algorithms that can learn from data without relying on rules-based programming.
• [University of Washington] Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.
• [Carnegie Mellon University] The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?
• [Investopedia] Machine learning is the concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computer’s built-in algorithms current regardless of changes in the worldwide economy.
• [SearchEnterpriseAI] Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.

From above, we can conclude that the Machine Learning 

• is a field of artificial intelligence
• use algorithms to learn from data
• can predict from new data without relying on rules-based programming

Machine Learning Algorithms

Main categories:

• Supervised Learning - learn from labeled data so as to predict with new data
• Unsupervised Learning - learn from data that has not been labeled, classified or categorized
• Semi-supervised Learning - learn from combination of small amount labeled and massive unlabeled data
• Reinforcement Learning - how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward, e.g. Google’s DeepMind AlphaGo computer

Misc Concepts

Transfer Learning - the technique of applying the trained model to a different but related problem

Neural Network Architectures

Source: http://www.asimovinstitute.org/neural-network-zoo/