Agents in AI
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Posts by Tags
Artificial Intelligence
Basics
Multi-Layer Perceptron
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This post covers the history of Deep Learning, from the Perceptron to the Multi-Layer Perceptron Network.
Basics of Deep Learning
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Activation functions
Auto-encoder
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Autoencoder
Introduction to optimization
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Gentle introduction to the convexity, derivatives and the taxonomy of problems in optimization.
Agents in AI
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Agents
Introduction to Machine Learning
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In a nutshell, Machine Learning is a sub-field of Artificial Intelligence whose aim is to convert experience into expertise of knowledge. Born in the 1960s, it quickly grew as a separated field because of a focus shift from decisional AI (logical and knowledge-based approach); its aim is not General Artificial Intelligence but rather tackle solvable problems of a practical nature. And ever since the 1990s, Machine Learning is progressing and flourishing rapidly. This success can largely be attributed to two factors:
Graph theory
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Networks are a central aspect of any systems, and Graph theory - a branch of Mathematics - is fundamental to grasp and represent those networks. From degrees to degree distributions, from paths to distances and learn to distinguish weighted, directed and bipartite networks.
Big Data
Map Reduce
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MapReduce is:
- A simple programming model for processing huge data sets in a distributed way.
- A framework that runs these programs on clusters of commodity servers, automatically handling the details of distributed computing :
- Division of labor.
- Distribution.
- Synchronization.
- Fault-tolerance.
Big data
Spark introduction
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This lecture will be an abstract overview, we will discuss:
- Spark
- Spark vs MapReduce
- Spark RDDs
- Spark DataFrames
Hadoop
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When data gets too large to be dealt with in memory (most computers have up to 32 GB in RAM usually), it is possible to use a distributed system.
Decision Modeling
Utility theory
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This blogpost is the first one of a series, whose aim is to both introduce what is Decision Modeling and build the corresponding mathematical framework. Decisions model are very important - particularly in business - as they reduce stress and deal with uncertainty. Supported by ever increasing amounts of data and sophisticated algorithms, their growing power have captured plenty of C-suite attention in the recent years. From very accurate predictions to guiding knotty optimization choices, decisions models are essential and worthy of interest.
Introduction Decision Modeling
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Decision process
Synthesis criterion
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1. Introduction
Social choice theory
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Social choice theory is an established field and a cornerstone of countless others: Economics, Political science, Computer science, Applied mathematics, Operational Research. As for AI applications, it turns out to be really useful for developing Multiple Agents systems.
Outranking methods
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A collective decision problem
Multiple criteria decision
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Multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine).
Deep Learning
Recurrent Neural Networks
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A major limitation of Vanilla Neural Network and Convolutional Neural Networks is that API is rather constrained:
Interpretability in Deep Learning
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Deep learning has been all the rage for the last few years. Powered by ever-increasing power, memory and bigger datasets, neural networks are easier to train than before. However, despite the tremendous success of certain applications (computer vision, Go and Dota…), the theoretical understanding of what a neural net actually takes more time to burgeon.
Optimizing Deep Learning
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Architectures as priors on function space, initializations as random nonlinear projections
Word embeddings
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An embeddings is a representation of an object (word, image) formulated as continuous vectors. They are constructed so that similar objects can have similar embeddings (metric learning). Usually, embeddings are not the final goal but are rather used as features (feature learning).
Word embeddings
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Goal: Use word embeddings to embed larger chunks of text!
Optimizing Deep Learning
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First-order methods: gradient descent and variants.
Multi-Layer Perceptron
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This post covers the history of Deep Learning, from the Perceptron to the Multi-Layer Perceptron Network.
Basics of Deep Learning
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Activation functions
Auto-encoder
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Autoencoder
Deep learning
MXNet in a nuthsell
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As per MXNet documentation:
Framework
MXNet in a nuthsell
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As per MXNet documentation:
Hadoop
Hadoop
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When data gets too large to be dealt with in memory (most computers have up to 32 GB in RAM usually), it is possible to use a distributed system.
Machine Learning
Naive Bayes
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Naïve Bayes is a generative learning algorithm for discrete valued input. In particular, it is known to work great on texts classification tasks like spam detection.
Probabilistic classifiers
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| Discriminative learning algorithms are algorithms that try to learn $p(y | x)$ directly (such as logistic regression), or algorithms that try to learn mappings directly from the space of inputs $X$ to the labels ${0, 1}$, (such as the perceptron algorithm). |
Linear Regression
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In layman terms, a Linear Regression consists in predicting a continuous dependent variable $Y$ as a linear combination of the independent variables $X = {x_{1}, …, x_{n} }$. The contribution of each variable $x_{i}$ is expressed by a parameter $\beta_{i}$. Altogether, it is a simple weighted sum:
Linear Discriminant Analysis
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LDA is closely related to PCA as it is linear.
Decision trees
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Decision Tree Induction
Logistic Regression
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TL; DR: Logistic Regression is a simple but oftentimes efficient algorithm that tackles binary classification.
Introduction to Machine Learning
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In a nutshell, Machine Learning is a sub-field of Artificial Intelligence whose aim is to convert experience into expertise of knowledge. Born in the 1960s, it quickly grew as a separated field because of a focus shift from decisional AI (logical and knowledge-based approach); its aim is not General Artificial Intelligence but rather tackle solvable problems of a practical nature. And ever since the 1990s, Machine Learning is progressing and flourishing rapidly. This success can largely be attributed to two factors:
NLP
Language modelling
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“But it must be recognized that the notion ’probability of a sentence’ is an entirely useless one, under any known interpretation of this term.”
Dialogue
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Word2Vec rocks!
Words representation
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Word2Vec rocks!
Language modelling
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1. Social Media Analytics: Introduction
Word embeddings
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An embeddings is a representation of an object (word, image) formulated as continuous vectors. They are constructed so that similar objects can have similar embeddings (metric learning). Usually, embeddings are not the final goal but are rather used as features (feature learning).
Word embeddings
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Goal: Use word embeddings to embed larger chunks of text!
Network Science
The Random Network Model
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Network science aims to build models that reproduce the properties of real networks. As most encountered networks are irregular alnd look like they were spun randomly.
Network science
Link prediction
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In this lecture, we learn the basics of how to perform unsupervised link prediction and supervised ling prediction. We will overview the following techniques:
Link prediction
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Spreading through Network
Community detection
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The notion of community structure captures the tendency of nodes to be organized into communities, where members within a community are more similar among each other.
The scale free property
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Hubs are encountered in most real networks. They represent a signature of a deeper organizing principle that we call the scale-free property.
Barabási-Albert Model
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Hubs represent the most striking difference between a random and a scale-free network.
Graph theory
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Networks are a central aspect of any systems, and Graph theory - a branch of Mathematics - is fundamental to grasp and represent those networks. From degrees to degree distributions, from paths to distances and learn to distinguish weighted, directed and bipartite networks.
Optimization
Introduction to optimization
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Gentle introduction to the convexity, derivatives and the taxonomy of problems in optimization.
Constrained optimization
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Goal: Make use of the Lagrangian and other methods to accomodate constraints.
Unconstrained optimization
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Main assumption: $f: \mathbb{R}^{n} \rightarrow \mathbb{R}$ is $\mathcal{C}^{1}$ or $\mathcal{C}^{2}$.
Reinforcement learning
Predictions in Reinforcement Learning
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In a model-free setting, the transition probabilities are unknown and the agent must interact with the environment. An additional challenge is the possibility that the control policy is different to the one to estimate. This is called off-policy but we will focus on on-policy in this blogpost. We will leverage a simulator and a policy $\pi$ - coupled with our knowledge of $S, A, \gamma$ to run episodes and improve the latter from sampled data.
Introduction to reinfocement learning
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Reinforcement learning find its roots in several scientific fields, such as Deep learning, Psychology, Control, Statistics (but not limited to!). It typically consists of taking suitable action to maximize reward in a particular situation. Below is a common illustration of its core idea:
Introduction to dynamic programming
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Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving (often recursively) each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc).
Spark
Spark introduction
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This lecture will be an abstract overview, we will discuss:
- Spark
- Spark vs MapReduce
- Spark RDDs
- Spark DataFrames
Statistics
Statistics basics
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1. Introduction
2. Generalities
3. Robust estimation
3.1. L-Statistics
3.2. M-estimates M-estimates of Location ### 3.3. M-estimates of scale
4. Robust regression
Statistics basics
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1. Statistical modelling
Statistics basics
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1. Generalities
Statistics basics
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1. Simulations
Statistics basics
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1. Convergence of random variables
Stats
Statistical Modeling and Parameter estimation
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1. Introduction
Overview to supervised learning The missing piece BOW: drawbacks
Visual computing
First order scattering transform
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1. Introduction
First order scattering transform
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Much like Fourier transform expresses periodic functions as a sum of sinus and cosinus, the Wavelet transform expresses signals as a weighted sum of a special kind of functions, wavelets. Both use some inner product (scalar product and convolution) of an input signal and a given kernel / mask. The difference lies in the kernel the kernel of the transformation.
Gabor filters
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In image processing, aa Gabor filter, (Dennis Gabor) is a linear filter used for texture analysis.
Low-level vision framework
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The problem of image analysis and understanding has gained high prominence over the last decade, and has emerged at forefront of signal and image processing research (read more here). In consequence, my first post on computer vision will deal with the basics of image understand.
Filters
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This post deals with the basics of Filtering (see a family of methods) which is extremely useful in computer vision.
Filters
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