Tal vez no sorprendente-mente el centro de Monterrey tiene una variedad de tiendas de componentes de electronica que le pueden servir a cualquier maker, hacedor, cacharrero, Inventor y demás gente inquieta interesada en construir o arreglar algún aparato electrónico

El centro de toda ciudad es un lugar a explorar, y la cantidad de tiendas que venden componentes de todos tipos (no solo de electronica) es mayor de lo que puede una persona tener suficiente experiencia en compras como para poder hablar de todas ellas con debida justicia. Por eso vamos a hablar de las tiendas principales cerca de la esquina mágica de Juarez y colon en el centro de Monterrey

Me referí a esa esquina en términos exagerados porque los componentes que podemos encontrar aquí son muy variados, desde robotica, control, iluminación LED, energía, audio e instrumentos musicales.

A continuación una pequeña descripción de las tiendas marcadas con Estrella en…

Deep learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch ofmachine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or otherwise, composed of multiple non-linear transformations.^{[1]}^{[2]}^{[3]}^{[4]}^{[5]}

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc.. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition^{[6]}) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervisedfeature learning and hierarchical feature extraction.^{[7]}

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.^{[8]}

Eratosthenes calculated the circumference of the Earth without leaving Egypt. He knew that at local noon on the summer solstice in Syene (modern Aswan, Egypt), the Sun was directly overhead. He knew this because the shadow of someone looking down a deep well at that time in Syene blocked the reflection of the Sun on the water. He measured the Sun’s angle of elevation at noon on the same day in Alexandria. The method of measurement was to make a scale drawing of that triangle which included a right angle between a vertical rod and its shadow. This turned out to be about 7°, or 1/50th of the way around a circle. Taking the Earth as spherical, and knowing both the distance and direction of Syene, he concluded that the Earth’s circumference was fifty times that distance.

His knowledge of the size of Egypt was founded on the work of many generations of surveying trips. Pharaonic bookkeepers gave a distance between Syene and Alexandria of 5,000 stadia (a figure that was checked yearly). Some say that the distance was corroborated by inquiring about the time that it took to travel from Syene to Alexandria by camel. Carl Sagan says that Eratosthenes paid a man to walk and measure the distance. Some claim Eratosthenes used the Olympic stade of 176.4 m, which would imply a circumference of 44,100 km, an error of 10%,^{[16]} but the 184.8 m Italian stade became (300 years later) the most commonly accepted value for the length of the stade,^{[16]} which implies a circumference of 46,100 km, an error of 15%.^{[16]} It was unlikely, even accounting for his extremely primitive measuring tools, that Eratosthenes could have calculated an accurate measurement for the circumference of the Earth. He made three important assumptions (none of which is perfectly accurate):

That the distance between Alexandria and Syene was 5000 stadia,

That the Earth is a perfect sphere.

That light rays emanating from the Sun are parallel.

Eratosthenes later rounded the result to a final value of 700 stadia per degree, which implies a circumference of 252,000 stadia, likely for reasons of calculation simplicity as the larger number is evenly divisible by 60.^{[16]} In 2012, Anthony Abreu Mora repeated Eratosthenes’ calculation with more accurate data; the result was 40,074 km, which is 66 km different (0.16%) from the currently accepted polar circumference of the Earth.

Seventeen hundred years after Eratosthenes’ death, while Christopher Columbus studied what Eratosthenes had written about the size of the Earth, he chose to believe, based on a map by Toscanelli, that the Earth’s circumference was one-third smaller. Had Columbus set sail knowing that Eratosthenes’ larger circumference value was more accurate, he would have known that the place that he made landfall was not Asia, but rather the New World.