learningmachines101.com

Revisión web de learningmachines101.com

Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning

 Generado el 15 Abril 2026 10:45 AM

Resultados antiguos? ACTUALIZAR !

La puntuación es 50/100

Contenido SEO

Título

Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning

Longitud : 93

Preferiblemente, tu título debería contener entre 10 y 70 caracteres (espacios incluidos). Usa esta herramienta gratuita para calcular la longitud del texto.

Descripción

Learning Machines 101 is committed to providing an accessible introduction to the complex and fascinating world of Artificial Intelligence which now has an impact on everyday life throughout the world! The intended audience for this podcast series is the general public and the intended objective of this podcast series is to help popularize and de-mystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner. However, many advanced topics in artificial intelligence and machine learning will be discussed at a “high-level” so students, scientists, and engineers working in the machine learning area will find this podcast series beneficial for identifying relevant “entry points” into advanced statistical machine learning topics.

Longitud : 777

Preferiblemente tu descripción meta debe contener entre 70 y 160 caracteres (espacios incluidos). Usa esta herramienta gratuita para calcular la longitu del texto.

Palabras Claves (Keywords)

Muy mal. No hemos encontrado palabras clave (meta keywords) en tu página. Usa este generador de meta tags gratuito para crear tus palabras clave.

Propiedades Meta Og

Bien. Tu página usa propiedades Og (etiquetas og).

Propiedad Contenido
locale en_US
type website
title Learning Machines 101 - A Gentle Introduction to Artificial Intelligence and Machine Learning
description Learning Machines 101 is committed to providing an accessible introduction to the complex and fascinating world of Artificial Intelligence which now has an impact on everyday life throughout the world! The intended audience for this podcast series is the general public and the intended objective of this podcast series is to help popularize and de-mystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner. However, many advanced topics in artificial intelligence and machine learning will be discussed at a “high-level” so students, scientists, and engineers working in the machine learning area will find this podcast series beneficial for identifying relevant “entry points” into advanced statistical machine learning topics.
url https://www.learningmachines101.com/
site_name Learning Machines 101
image https://www.learningmachines101.com/wp-content/uploads/2021/07/Episode86graphicWideScreen.jpg
image:secure_url https://www.learningmachines101.com/wp-content/uploads/2021/07/Episode86graphicWideScreen.jpg
image:width 959
image:height 540

Titulos

H1 H2 H3 H4 H5 H6
1 50 0 0 0 0
  • [H1] Learning Machines 101
  • [H2] LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
  • [H2] LM101-085: Ch7: How to Guarantee your Batch Learning Algorithm Converges
  • [H2] LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
  • [H2] LM101-083: Ch5: How to Use Calculus to Design Learning Machines
  • [H2] LM101-082: Ch4: How to Analyze and Design Linear Machines
  • [H2] LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
  • [H2] LM101-080: Ch2: How to Represent Knowledge using Set Theory
  • [H2] LM101-079: Ch1: How to View Learning as Risk Minimization
  • [H2] LM101-078: Ch0: How to Become a Machine Learning Expert
  • [H2] LM101-077: How to Choose the Best Model using BIC
  • [H2] LM101-076: How To Choose the Best Model using AIC or GAIC
  • [H2] LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix)
  • [H2] LM101-074: How to Represent Knowledge using Logical Rules (remix)
  • [H2] LM101-073: How to Build a Machine that Learns Checkers (remix)
  • [H2] LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix)
  • [H2] LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
  • [H2] LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding
  • [H2] LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
  • [H2] LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
  • [H2] LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
  • [H2] LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
  • [H2] LM101-065: How to Design Gradient Descent Learning Machines (Rerun)
  • [H2] LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)
  • [H2] LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine
  • [H2] LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine
  • [H2] LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)
  • [H2] LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms
  • [H2] LM101-059: How to Properly Introduce a Neural Network
  • [H2] LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis
  • [H2] LM101-057: How to Catch Spammers using Spectral Clustering
  • [H2] LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
  • [H2] LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)
  • [H2] LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)
  • [H2] LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)
  • [H2] LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear
  • [H2] LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun]
  • [H2] LM101-050: How to Use Linear Regression Software to Make Predictions (RERUN)
  • [H2] LM101-049: How to Experiment with Lunar Lander Software
  • [H2] LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)
  • [H2] LM101-047: How to Build a Support Vector Machine to Classify Patterns (Rerun)
  • [H2] LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
  • [H2] LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
  • [H2] LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
  • [H2] LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun)
  • [H2] LM101-042: What happened at the Monte Carlo Markov Chain Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
  • [H2] LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
  • [H2] LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
  • [H2] LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)[Rerun]
  • [H2] LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets (Educational Technology)
  • [H2] LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory

Imagenes

Hemos encontrado 106 imágenes en esta web.

55 atributos alt están vacios o no existen. Agrega texto alternativo para que los motores de búsqueda puedan entender las imágenes.

Ratio Texto/HTML

Ratio : 9%

El ratio entre texto y código HTML de esta página es menor que el 15 por ciento, esto significa que tu web posiblemente necesite más contenido en texto.

Flash

Perfecto, no se ha detectado contenido Flash en la página.

Iframe

Genial, no se han detectado Iframes en la página.

Reescritura URL

Bien. Tus enlaces parecen amigables

Guiones bajos en las URLs

Perfecto! No hemos detectado guiones bajos en tus URLs

Enlaces en página

Hemos encontrado un total de 265 enlaces incluyendo 47 enlace(s) a ficheros

Ancla Tipo Jugo
Learning Machines 101 Interna Pasando Jugo
Skip to content Interna Pasando Jugo
Home Interna Pasando Jugo
Join the Community! Interna Pasando Jugo
About Learning Machines 101 Interna Pasando Jugo
About Dr. Golden Interna Pasando Jugo
Episode Archive Interna Pasando Jugo
2020 Episodes Interna Pasando Jugo
2019 Episodes Interna Pasando Jugo
2018 Episodes Interna Pasando Jugo
2017 Episodes Interna Pasando Jugo
2015 Episodes Interna Pasando Jugo
2014 Episodes Interna Pasando Jugo
Book Stuff! Interna Pasando Jugo
Dr. Goldens New Book! Externo Pasando Jugo
Book Review Archive Interna Pasando Jugo
Software Interna Pasando Jugo
Linear Machine Interna Pasando Jugo
Nonlinear Machine (RBF) Interna Pasando Jugo
Lunar Lander Software Interna Pasando Jugo
Licenses Interna Pasando Jugo
FAQ Interna Pasando Jugo
Contact Us Interna Pasando Jugo
LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes Interna Pasando Jugo
LM101-085: Ch7: How to Guarantee your Batch Learning Algorithm Converges Interna Pasando Jugo
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems Interna Pasando Jugo
LM101-083: Ch5: How to Use Calculus to Design Learning Machines Interna Pasando Jugo
LM101-082: Ch4: How to Analyze and Design Linear Machines Interna Pasando Jugo
https://traffic.libsyn.com/secure/learningmachines101/LM101-086.mp3 Externo Pasando Jugo
Embed Interna noFollow
tweet Externo Pasando Jugo
BOOK Interna Pasando Jugo
Probabilistic Inference Interna Pasando Jugo
SMLBOOK Interna Pasando Jugo
Topic Interna Pasando Jugo
absolutely continuous density Interna Pasando Jugo
Banach-Tarski Interna Pasando Jugo
mixed random vector Interna Pasando Jugo
https://traffic.libsyn.com/secure/learningmachines101/LM101-085.mp3 Externo Pasando Jugo
Deep Learning Interna Pasando Jugo
Gradient Descent Learning Interna Pasando Jugo
batch learning Interna Pasando Jugo
descent direction Interna Pasando Jugo
gradient descent Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-084.mp3 Externo Pasando Jugo
book Interna Pasando Jugo
convergence Interna Pasando Jugo
dynamical systems Interna Pasando Jugo
matrix calculus Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-082.mp3 Externo Pasando Jugo
Machine Learning Interna Pasando Jugo
linear algebra Interna Pasando Jugo
matrix multiplication Interna Pasando Jugo
SVD Interna Pasando Jugo
LM101-081: Ch3: How to Define Machine Learning (or at Least Try) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-081.mp3 Externo Pasando Jugo
Book Review Interna Pasando Jugo
complete relation Interna Pasando Jugo
LM101-080: Ch2: How to Represent Knowledge using Set Theory Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-080.mp3 Externo Pasando Jugo
Features Interna Pasando Jugo
Rule-based Inference Interna Pasando Jugo
logic Interna Pasando Jugo
logical rules Interna Pasando Jugo
LM101-079: Ch1: How to View Learning as Risk Minimization Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/lm101-079.mp3 Externo Pasando Jugo
empirical risk Interna Pasando Jugo
reinforcement learning Interna Pasando Jugo
LM101-078: Ch0: How to Become a Machine Learning Expert Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-078.mp3 Externo Pasando Jugo
machine learning books Interna Pasando Jugo
machine learning mathematics Interna Pasando Jugo
machine learning software Interna Pasando Jugo
LM101-077: How to Choose the Best Model using BIC Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-077.mp3 Externo Pasando Jugo
Generalization Performance Interna Pasando Jugo
Model Selection Interna Pasando Jugo
Bayesian Information Criterion Interna Pasando Jugo
BIC Interna Pasando Jugo
Marginal Likelihood Interna Pasando Jugo
LM101-076: How To Choose the Best Model using AIC or GAIC Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-076.mp3 Externo Pasando Jugo
AIC Interna Pasando Jugo
Akaike Information Criterion Interna Pasando Jugo
cross-validation Interna Pasando Jugo
LM101-075: Can computers think? A Mathematician’s Response using a Turing Machine Argument (remix) Interna Pasando Jugo
LM101-074: How to Represent Knowledge using Logical Rules (remix) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-074.mp3 Externo Pasando Jugo
feature vector Interna Pasando Jugo
features Interna Pasando Jugo
LM101-073: How to Build a Machine that Learns Checkers (remix) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-073.mp3 Externo Pasando Jugo
Reinforcement Learning Interna Pasando Jugo
artificial intelligence Interna Pasando Jugo
Artificial Neural Networks Interna Pasando Jugo
Evaluation Function Interna Pasando Jugo
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (LM101-001+LM101-002 remix) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-072.mp3 Externo Pasando Jugo
learning machines 101 Interna Pasando Jugo
machine learning Interna Pasando Jugo
Neural Networks Interna Pasando Jugo
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-071.mp3 Externo Pasando Jugo
Constraint Satisfaction Interna Pasando Jugo
Monte Carlo Markov Chain Interna Pasando Jugo
common-sense knowledge Interna Pasando Jugo
CYC Interna Pasando Jugo
CYCL Interna Pasando Jugo
LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-070.mp3 Externo Pasando Jugo
Clustering Algorithms Interna Pasando Jugo
Unsupervised Learning Interna Pasando Jugo
clustering Interna Pasando Jugo
Emotions Interna Pasando Jugo
Face Recognition Interna Pasando Jugo
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference? Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-069.mp3 Externo Pasando Jugo
curricula Interna Pasando Jugo
neural information processing systems Interna Pasando Jugo
NIPS 2017 Interna Pasando Jugo
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-068.mp3 Externo Pasando Jugo
Backtracking Linesearch Interna Pasando Jugo
Convergence Theorem Interna Pasando Jugo
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun) Interna Pasando Jugo
Boltzmann Machine Interna Pasando Jugo
Constraint Satisfaction Interna Pasando Jugo
Dreams Interna Pasando Jugo
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-066.mp3 Externo Pasando Jugo
Gibbs Sampler Interna Pasando Jugo
LM101-065: How to Design Gradient Descent Learning Machines (Rerun) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/lm101-065.mp3 Externo Pasando Jugo
line search Interna Pasando Jugo
LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-064.mp3 Externo Pasando Jugo
Genetic Algorithms Interna Pasando Jugo
Darwin Natural Selection Interna Pasando Jugo
Evolution Interna Pasando Jugo
genetic algorithm Interna Pasando Jugo
LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-063.mp3 Externo Pasando Jugo
Expectation Maximization Interna Pasando Jugo
Monte Carlo Expectation Maximization Interna Pasando Jugo
policy gradient Interna Pasando Jugo
LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-062.mp3 Externo Pasando Jugo
Deep Reinforcement Learning Interna Pasando Jugo
Game playing Interna Pasando Jugo
Q learning Interna Pasando Jugo
LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-061.mp3 Externo Pasando Jugo
Deep Learning Interna Pasando Jugo
off-policy Interna Pasando Jugo
on-policy Interna Pasando Jugo
LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-060.mp3 Externo Pasando Jugo
Anodot Interna Pasando Jugo
Anomaly Detection Interna Pasando Jugo
Berlin Buzzwords Interna Pasando Jugo
LM101-059: How to Properly Introduce a Neural Network Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-059.mp3 Externo Pasando Jugo
Biological Neural Networks Interna Pasando Jugo
Software Interna Pasando Jugo
biological neural networks Interna Pasando Jugo
Computational Neuroscience Interna Pasando Jugo
Convolutional Neural Networks Interna Pasando Jugo
LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-058.mp3 Externo Pasando Jugo
correct specification Interna Pasando Jugo
goodness-of-fit Interna Pasando Jugo
information matrix test Interna Pasando Jugo
LM101-057: How to Catch Spammers using Spectral Clustering Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/lm101-057.mp3 Externo Pasando Jugo
harvest bots Interna Pasando Jugo
harvesters Interna Pasando Jugo
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-056.mp3 Externo Pasando Jugo
Correlated Topic Models Interna Pasando Jugo
Information Matrix Tests Interna Pasando Jugo
Latent Dirichlet Allocation Interna Pasando Jugo
LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-055.mp3 Externo Pasando Jugo
learning machine Interna Pasando Jugo
MAP estimation Interna Pasando Jugo
maximum likelihood estimation Interna Pasando Jugo
LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-054.mp3 Externo Pasando Jugo
Automatic Essay Grading Interna Pasando Jugo
Latent Semantic Analysis Interna Pasando Jugo
Latent Semantic Indexing Interna Pasando Jugo
LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-053.mp3 Externo Pasando Jugo
Markov Field Interna Pasando Jugo
Metropolis-Hastings Interna Pasando Jugo
Monte Carlo Markov Chain Interna Pasando Jugo
LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-052.mp3 Externo Pasando Jugo
Function Approximation Interna Pasando Jugo
Supervised Learning Interna Pasando Jugo
kernel trick Interna Pasando Jugo
mercers theorem Interna Pasando Jugo
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun] Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-051.mp3 Externo Pasando Jugo
gaussian mixture model Interna Pasando Jugo
Hidden Units Interna Pasando Jugo
LM101-050: How to Use Linear Regression Software to Make Predictions (RERUN) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-050.mp3 Externo Pasando Jugo
free software Interna Pasando Jugo
iris data set Interna Pasando Jugo
linear regression Interna Pasando Jugo
LM101-049: How to Experiment with Lunar Lander Software Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-049.mp3 Externo Pasando Jugo
Adaptive gradient descent Interna Pasando Jugo
lunar lander Interna Pasando Jugo
supervised learning Interna Pasando Jugo
LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-048.mp3 Externo Pasando Jugo
control theory Interna Pasando Jugo
LM101-047: How to Build a Support Vector Machine to Classify Patterns (Rerun) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-047.mp3 Externo Pasando Jugo
Logistic Regression Interna Pasando Jugo
Support Vector Machine Interna Pasando Jugo
svm Interna Pasando Jugo
LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-046.mp3 Externo Pasando Jugo
Educational Technology Interna Pasando Jugo
educational technology; recurrent networks; item response theory; student learning Interna Pasando Jugo
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-045.mp3 Externo Pasando Jugo
Recurrent Networks Interna Pasando Jugo
Turing Test Interna Pasando Jugo
recurrent networks Interna Pasando Jugo
Turing Test Interna Pasando Jugo
LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference? Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-044.mp3 Externo Pasando Jugo
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-043.mp3 Externo Pasando Jugo
LM101-042: What happened at the Monte Carlo Markov Chain Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference? Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-042.mp3 Externo Pasando Jugo
Gibbs Interna Pasando Jugo
MCMC Interna Pasando Jugo
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial? Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-041.mp3 Externo Pasando Jugo
nips Interna Pasando Jugo
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-040.mp3 Externo Pasando Jugo
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)[Rerun] Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-039.mp3 Externo Pasando Jugo
Gibbs Sampler algorithm Interna Pasando Jugo
Markov random fields Interna Pasando Jugo
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets (Educational Technology) Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-038.mp3 Externo Pasando Jugo
bayesian network Interna Pasando Jugo
educational technology Interna Pasando Jugo
hidden Markov model Interna Pasando Jugo
LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Interna Pasando Jugo
http://traffic.libsyn.com/learningmachines101/LM101-037.mp3 Externo Pasando Jugo
CAT Interna Pasando Jugo
Computerized adaptive testing Interna Pasando Jugo
2 Interna Pasando Jugo
- Externo Pasando Jugo
Listen on Stitcher Externo Pasando Jugo
Iconic One Pro Externo Pasando Jugo
Wordpress Externo Pasando Jugo

Palabras Clave SEO

Nube de Palabras Clave

machines machine how episode new learning download using summary podcast

Consistencia de las Palabras Clave

Palabra Clave (Keyword) Contenido Título Palabras Claves (Keywords) Descripción Titulos
learning 103
how 56
episode 50
machine 42
podcast 33

Usabilidad

Url

Dominio : learningmachines101.com

Longitud : 23

Favicon

Genial, tu web tiene un favicon.

Imprimibilidad

No hemos encontrado una hoja de estilos CSS para impresión.

Idioma

Genial. Has declarado el idioma en.

Dublin Core

Esta página no usa Dublin Core.

Documento

Tipo de documento (Doctype)

HTML 5

Codificación

Perfecto. Has declarado como codificación UTF-8.

Validez W3C

Errores : 0

Avisos : 0

Privacidad de los Emails

Atención! Hemos encontrado por lo menos una dirección de correo electrónico en texto plano. Usa este protector antispam gratuito para ocultarla de los spammers.

HTML obsoleto

Etiquetas obsoletas Ocurrencias
<font> 1

Hemos detectado etiquetas HTML obsoletas que ya no se usa. Es recomendable que cambies esas etiquetas por otras similares.

Consejos de Velocidad

Excelente, esta web no usa tablas.
Muy mal, tu web está usando estilos embenidos (inline CSS).
Muy mal, tu página web usa demasiados ficheros CSS (más de 4).
Muy mal, tu sitio usa demasiados ficheros JavaScript (más de 6).
Su sitio web se beneficia del tipo de compresión gzip. ¡Perfecto!

Movil

Optimización Móvil

Icono para Apple
Etiqueta Meta Viewport
Contenido Flash

Optimización

Mapa del sitio XML

¡Perfecto! Su sitio tiene un mapa del sitio en XML.

https://www.learningmachines101.com/wp-sitemap.xml

Robots.txt

https://learningmachines101.com/robots.txt

¡Estupendo! Su sitio web tiene un archivo robots.txt.

Herramientas de Analítica

No disponible

No hemos encontrado ninguna herramienta de analítica en esta web.

La analítica Web le permite medir la actividad de los visitantes de su sitio web. Debería tener instalada al menos una herramienta de analítica y se recomienda instalar otra más para obtener una confirmación de los resultados.

PageSpeed Insights

Dispositivo
Categorias

Free SEO Testing Tool

Free SEO Testing Tool es una herramienta seo gratuita que te ayuda a analizar tu web