Update README-es.md
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@ -1643,318 +1643,285 @@ Es probable que estos temas no aparezcan en una entrevista, pero los añadí par
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- [ ] [Avro](https://avro.apache.org/).
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- ### A*
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- [ ] [A Search Algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm)
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- [ ] [A* Pathfinding Tutorial (video)](https://www.youtube.com/watch?v=KNXfSOx4eEE)
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- [ ] [A* Pathfinding (E01: algorithm explanation) (video)](https://www.youtube.com/watch?v=-L-WgKMFuhE)
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- [ ] [A Search Algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm).
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- [ ] [A* Pathfinding Tutorial (video)](https://www.youtube.com/watch?v=KNXfSOx4eEE).
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- [ ] [A* Pathfinding (E01: algorithm explanation) (video)](https://www.youtube.com/watch?v=-L-WgKMFuhE).
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- ### Transformada rápida de Fourier
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- [ ] [An Interactive Guide To The Fourier Transform](https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/)
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- [ ] [What is a Fourier transform? What is it used for?](http://www.askamathematician.com/2012/09/q-what-is-a-fourier-transform-what-is-it-used-for/)
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- [ ] [What is the Fourier Transform? (video)](https://www.youtube.com/watch?v=Xxut2PN-V8Q)
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- [ ] [Divide & Conquer: FFT (video)](https://www.youtube.com/watch?v=iTMn0Kt18tg&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=4)
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- [ ] [Understanding The FFT](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/)
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- [ ] [An Interactive Guide To The Fourier Transform](https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/).
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- [ ] [What is a Fourier transform? What is it used for?](http://www.askamathematician.com/2012/09/q-what-is-a-fourier-transform-what-is-it-used-for/).
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- [ ] [What is the Fourier Transform? (video)](https://www.youtube.com/watch?v=Xxut2PN-V8Q).
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- [ ] [Divide & Conquer: FFT (video)](https://www.youtube.com/watch?v=iTMn0Kt18tg&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=4).
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- [ ] [Understanding The FFT](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/).
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- ### Filtro de Bloom
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- Dado un filtro de Bloom con m bits y k funciones de hashing, tanto las pruebas de inserción como de pertenencia son O(k)
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- [Bloom Filters](https://www.youtube.com/watch?v=-SuTGoFYjZs)
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- [Bloom Filters | Mining of Massive Datasets | Stanford University](https://www.youtube.com/watch?v=qBTdukbzc78)
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- [Tutorial](http://billmill.org/bloomfilter-tutorial/)
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- [How To Write A Bloom Filter App](http://blog.michaelschmatz.com/2016/04/11/how-to-write-a-bloom-filter-cpp/)
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- Dado un filtro de Bloom con m bits y k funciones de hashing, tanto las pruebas de inserción como de pertenencia son O(k).
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- [Bloom Filters](https://www.youtube.com/watch?v=-SuTGoFYjZs).
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- [Bloom Filters | Mining of Massive Datasets | Stanford University](https://www.youtube.com/watch?v=qBTdukbzc78).
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- [Tutorial](http://billmill.org/bloomfilter-tutorial/).
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- [How To Write A Bloom Filter App](http://blog.michaelschmatz.com/2016/04/11/how-to-write-a-bloom-filter-cpp/).
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- ### HyperLogLog
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- [How To Count A Billion Distinct Objects Using Only 1.5KB Of Memory](http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html)
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- [How To Count A Billion Distinct Objects Using Only 1.5KB Of Memory](http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html).
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- ### Hashing sensible a la localidad
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- Usado para determiner la similitude de documentos
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- Usado para determiner la similitude de documentos.
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- Lo opuesto de MD5 o SHA que son usados para determinar si dos documentos/cadenas son exactamente iguales.
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- [Simhashing (hopefully) made simple](http://ferd.ca/simhashing-hopefully-made-simple.html)
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- [Simhashing (hopefully) made simple](http://ferd.ca/simhashing-hopefully-made-simple.html).
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- ### Árboles van Emde Boa
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- [ ] [Divide & Conquer: van Emde Boas Trees (video)](https://www.youtube.com/watch?v=hmReJCupbNU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=6)
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- [ ] [MIT Lecture Notes](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2012/lecture-notes/MIT6_046JS12_lec15.pdf)
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- [ ] [Divide & Conquer: van Emde Boas Trees (video)](https://www.youtube.com/watch?v=hmReJCupbNU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=6).
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- [ ] [MIT Lecture Notes](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2012/lecture-notes/MIT6_046JS12_lec15.pdf).
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- ### Estructuras de datos aumentadas
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- [ ] [CS 61B Lecture 39: Augmenting Data Structures](https://youtu.be/zksIj9O8_jc?list=PL4BBB74C7D2A1049C&t=950)
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- [ ] [CS 61B Lecture 39: Augmenting Data Structures](https://youtu.be/zksIj9O8_jc?list=PL4BBB74C7D2A1049C&t=950).
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- ### Árboles de búsqueda equilibrada
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- Conocer por lo menos un tipo de árbol binario equilibrado (y saber cómo se implementa):
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- " Entre los árboles de búsqueda equilibrada, los árboles AVL y 2/3 están ahora pasados de moda, y los árboles rojo-negro parecen ser más populares.
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- "Entre los árboles de búsqueda equilibrada, los árboles AVL y 2/3 están ahora pasados de moda, y los árboles rojo-negro parecen ser más populares.
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Una estructura de datos auto-organizable particularmente interesante es el árbol biselados, que utilizan rotaciones para mover cualquier clave accedida a la raíz.." - Skiena
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- De estos, opté por implementar un árbol biselado. Por lo que he leído, no implementarás un árbol de búsqueda equilibrado en tu entrevista. Pero yo quería exponer la codificación de uno y bueno, los árboles son las rodillas de la abeja. He leído un montón de código de árbol rojo-negro.
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- Árboles biselados: Funciones insert, search, delete
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- Árboles biselados: Funciones insert, search, delete.
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Si terminas implementando un árbol rojo / negro, intenta lo siguiente:
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- Funciones de búsqueda e inserción, saltándose eliminar
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- Funciones de búsqueda e inserción, saltándose eliminar.
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- Quiero aprender más acerca de los Árboles-B ya que se utiliza tan ampliamente con conjuntos de datos muy grandes.
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- [ ] [Self-balancing binary search tree](https://en.wikipedia.org/wiki/Self-balancing_binary_search_tree)
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- [ ] [Self-balancing binary search tree](https://en.wikipedia.org/wiki/Self-balancing_binary_search_tree).
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- [ ] **Árboles AVL**
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- En práctica:
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Por lo que puedo decir, estos no se usan mucho en la práctica, pero pude ver dónde estarían:
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El árbol AVL es otra estructura que soporta la búsqueda, inserción y eliminación de O (log n). Es más rígidamente equilibrado que los árboles rojo-negro, lo que lleva a una inserción y eliminación más lenta, pero más rápido la recuperación. Esto lo hace atractivo para las estructuras de datos que se pueden construir una vez y se cargan sin reconstrucción, como diccionarios de idiomas (o diccionarios de programas, como los opcodes de un ensamblador o intérprete).
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- [ ] [MIT AVL Trees / AVL Sort (video)](https://www.youtube.com/watch?v=FNeL18KsWPc&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=6)
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- [ ] [AVL Trees (video)](https://www.coursera.org/learn/data-structures/lecture/Qq5E0/avl-trees)
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- [ ] [AVL Tree Implementation (video)](https://www.coursera.org/learn/data-structures/lecture/PKEBC/avl-tree-implementation)
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- [ ] [Split And Merge](https://www.coursera.org/learn/data-structures/lecture/22BgE/split-and-merge)
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- [ ] [MIT AVL Trees / AVL Sort (video)](https://www.youtube.com/watch?v=FNeL18KsWPc&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=6).
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- [ ] [AVL Trees (video)](https://www.coursera.org/learn/data-structures/lecture/Qq5E0/avl-trees).
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- [ ] [AVL Tree Implementation (video)](https://www.coursera.org/learn/data-structures/lecture/PKEBC/avl-tree-implementation).
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- [ ] [Split And Merge](https://www.coursera.org/learn/data-structures/lecture/22BgE/split-and-merge).
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- [ ] **Árboles biselados**
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- En práctica:
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Los árboles biselados son típicamente usados en la implementación de la memoria cache, ssignadores de memoria, enrutadores, recolectores de basura, compresión de datos, cuerdas (reemplazo de la cadena utilizada para cadenas de texto largas), en Windows NT (en la memoria virtual, en red y en el código del sistema de archivos) etc.
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- [ ] [CS 61B: Splay Trees (video)](https://www.youtube.com/watch?v=Najzh1rYQTo&index=23&list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd)
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- [ ] [CS 61B: Splay Trees (video)](https://www.youtube.com/watch?v=Najzh1rYQTo&index=23&list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd).
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- [ ] MIT Lecture: Splay Trees:
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- Se vuelve muy matemático, pero vea los 10 últimos minutos.
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- [Video](https://www.youtube.com/watch?v=QnPl_Y6EqMo)
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- [Video](https://www.youtube.com/watch?v=QnPl_Y6EqMo).
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- [ ] **Árboles rojos/negros**
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- Éstos son una traducción de un árbol 2-3 (véase abajo)
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- En la práctica:
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Los árboles rojo-negro ofrecen las peores garantías de tiempo de inserción, tiempo de borrado y tiempo de búsqueda.
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Esto no solo los hace valiosos en aplicaciones sensibles al tiempo como las aplicaciones en tiempo real, sino que las convierte en elementos valiosos en otras estructuras de datos que proporcionan las garantías más desfavorables; por ejemplo, muchas estructuras de datos utilizadas en la geometría computacional pueden basarse en árboles rojos y negros, y el Completely Fair Scheduler utilizado en los kernels Linux actuales usa árboles de color rojo-negro. En la versión 8 de Java, la Colección HashMap se ha modificado de manera que en lugar de usar una lista enlazada para almacenar elementos idénticos con códigos de hash pobres, se utiliza un árbol Rojo-Negro.
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- [ ] [Aduni - Algorithms - Lecture 4 (link jumps to starting point) (video)](https://youtu.be/1W3x0f_RmUo?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3871)
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- [ ] [Aduni - Algorithms - Lecture 5 (video)](https://www.youtube.com/watch?v=hm2GHwyKF1o&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=5)
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- [ ] [Black Tree](https://en.wikipedia.org/wiki/Red%E2%80%93black_tree)
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- [ ] [An Introduction To Binary Search And Red Black Tree](https://www.topcoder.com/community/data-science/data-science-tutorials/an-introduction-to-binary-search-and-red-black-trees/)
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- [ ] [Aduni - Algorithms - Lecture 4 (link jumps to starting point) (video)](https://youtu.be/1W3x0f_RmUo?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3871).
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- [ ] [Aduni - Algorithms - Lecture 5 (video)](https://www.youtube.com/watch?v=hm2GHwyKF1o&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=5).
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- [ ] [Black Tree](https://en.wikipedia.org/wiki/Red%E2%80%93black_tree).
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- [ ] [An Introduction To Binary Search And Red Black Tree](https://www.topcoder.com/community/data-science/data-science-tutorials/an-introduction-to-binary-search-and-red-black-trees/).
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- [ ] **Árboles de búsqueda 2-3**
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- En práctica:
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Los árboles 2-3 tienen inserciones más rápidas a expensas de búsquedas más lentas (ya que la altura es más comparada con árboles AVL).
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- Usará árboles 2-3 muy raramente porque su implementación implica diferentes tipos de nodos. En su lugar, las personas utilizan árboles de color rojo negro.
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- [ ] [23-Tree Intuition and Definition (video)](https://www.youtube.com/watch?v=C3SsdUqasD4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=2)
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- [ ] [Binary View of 23-Tree](https://www.youtube.com/watch?v=iYvBtGKsqSg&index=3&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
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- [ ] [2-3 Trees (student recitation) (video)](https://www.youtube.com/watch?v=TOb1tuEZ2X4&index=5&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
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- [ ] [23-Tree Intuition and Definition (video)](https://www.youtube.com/watch?v=C3SsdUqasD4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=2).
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- [ ] [Binary View of 23-Tree](https://www.youtube.com/watch?v=iYvBtGKsqSg&index=3&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6).
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- [ ] [2-3 Trees (student recitation) (video)](https://www.youtube.com/watch?v=TOb1tuEZ2X4&index=5&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp).
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- [ ] **Árboles 2-3-4 (aka Árboles 2-4)**
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- En práctica:
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Para cada árbol 2-4, hay árboles rojo-negro correspondientes con elementos de datos en el mismo orden. Las operaciones de inserción y supresión en árboles 2-4 también son equivalentes a la rotación de color en rojos y negros árboles. Esto hace que árboles 2-4 sean una herramienta importante para entender la lógica detrás de los árboles rojo-negros, y es por eso que muchos textos de algoritmo introductorios introducen árboles 2-4 justo antes de los árboles rojo-negro, aunque **Los árboles 2-4 no son a menudo utilizados en la práctica**.
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- [ ] [CS 61B Lecture 26: Balanced Search Trees (video)](https://www.youtube.com/watch?v=zqrqYXkth6Q&index=26&list=PL4BBB74C7D2A1049C)
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- [ ] [Bottom Up 234-Trees (video)](https://www.youtube.com/watch?v=DQdMYevEyE4&index=4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
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- [ ] [Top Down 234-Trees (video)](https://www.youtube.com/watch?v=2679VQ26Fp4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=5)
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- [ ] [CS 61B Lecture 26: Balanced Search Trees (video)](https://www.youtube.com/watch?v=zqrqYXkth6Q&index=26&list=PL4BBB74C7D2A1049C).
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- [ ] [Bottom Up 234-Trees (video)](https://www.youtube.com/watch?v=DQdMYevEyE4&index=4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6).
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- [ ] [Top Down 234-Trees (video)](https://www.youtube.com/watch?v=2679VQ26Fp4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=5).
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- [ ] **Árboles N-avo (K-avo, M-avo)**
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- Nota: La N o K es el factor de ramificación (ramas máximas)
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- Los árboles binarios son un árbol de 2 aros, con factor de ramificación = 2
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- Los árboles 2-3 son 3-avos
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- [ ] [K-Ary Tree](https://en.wikipedia.org/wiki/K-ary_tree)
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- Nota: La N o K es el factor de ramificación (ramas máximas).
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- Los árboles binarios son un árbol de 2 aros, con factor de ramificación = 2.
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- Los árboles 2-3 son 3-avos.
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- [ ] [K-Ary Tree](https://en.wikipedia.org/wiki/K-ary_tree).
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- [ ] **Árboles-B**
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- Dato curioso: Es un misterio, pero la B puede ser por Boeing, Balanceado, o Bayer (co-inventor)
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- Dato curioso: Es un misterio, pero la B puede ser por Boeing, Balanceado, o Bayer (co-inventor).
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- En práctica:
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Árboles-B son ampliamente utilizados en bases de datos. La mayoría de los sistemas de archivos modernos utilizan árboles B (o variantes). Además de su uso en bases de datos, el árbol B también se utiliza en sistemas de archivos para permitir el acceso rápido y aleatorio a un bloque arbitrario en un archivo en particular. El problema básico es convertir la dirección de bloque de archivos i en una dirección de bloque de disco (o tal vez a una dirección de cilindro-cabezal).
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- [ ] [B-Tree](https://en.wikipedia.org/wiki/B-tree)
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- [ ] [Introduction to B-Trees (video)](https://www.youtube.com/watch?v=I22wEC1tTGo&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=6)
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- [ ] [B-Tree Definition and Insertion (video)](https://www.youtube.com/watch?v=s3bCdZGrgpA&index=7&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
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- [ ] [B-Tree Deletion (video)](https://www.youtube.com/watch?v=svfnVhJOfMc&index=8&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
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- [ ] [MIT 6.851 - Memory Hierarchy Models (video)](https://www.youtube.com/watch?v=V3omVLzI0WE&index=7&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf)
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- Cubre árboles-B de cache inconsistente, estructuras de datos muy interesantes
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- Los primeros 37 minutos son muy técnicos, puede saltarlos (B es tamaño de bloque, tamaño de línea de caché)
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- [ ] [B-Tree](https://en.wikipedia.org/wiki/B-tree).
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- [ ] [Introduction to B-Trees (video)](https://www.youtube.com/watch?v=I22wEC1tTGo&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=6).
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- [ ] [B-Tree Definition and Insertion (video)](https://www.youtube.com/watch?v=s3bCdZGrgpA&index=7&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6).
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- [ ] [B-Tree Deletion (video)](https://www.youtube.com/watch?v=svfnVhJOfMc&index=8&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6).
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- [ ] [MIT 6.851 - Memory Hierarchy Models (video)](https://www.youtube.com/watch?v=V3omVLzI0WE&index=7&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf).
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- Cubre árboles-B de cache inconsistente, estructuras de datos muy interesantes.
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- Los primeros 37 minutos son muy técnicos, puede saltarlos (B es tamaño de bloque, tamaño de línea de caché).
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- ### Árboles k-D
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- Ideal para encontrar el número de puntos en un rectángulo o un objeto de dimensión superior
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- Un buen ajuste para k-vecinos más cercanos
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- [ ] [Kd Trees (video)](https://www.youtube.com/watch?v=W94M9D_yXKk)
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- [ ] [kNN K-d tree algorithm (video)](https://www.youtube.com/watch?v=Y4ZgLlDfKDg)
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- Ideal para encontrar el número de puntos en un rectángulo o un objeto de dimensión superior.
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- Un buen ajuste para k-vecinos más cercanos.
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- [ ] [Kd Trees (video)](https://www.youtube.com/watch?v=W94M9D_yXKk).
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- [ ] [kNN K-d tree algorithm (video)](https://www.youtube.com/watch?v=Y4ZgLlDfKDg).
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- ### Lista por saltos
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- " Éste es algo de una culta estructura de datos" - Skiena
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- [ ] [Randomization: Skip Lists (video)](https://www.youtube.com/watch?v=2g9OSRKJuzM&index=10&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
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- [ ] [For animations and a little more detail](https://en.wikipedia.org/wiki/Skip_list)
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- "Este es algo de una culta estructura de datos" - Skiena.
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- [ ] [Randomization: Skip Lists (video)](https://www.youtube.com/watch?v=2g9OSRKJuzM&index=10&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp).
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- [ ] [For animations and a little more detail](https://en.wikipedia.org/wiki/Skip_list).
|
||||
|
||||
- ### Flujos de red
|
||||
- [ ] [Ford-Fulkerson in 5 minutes (video)](https://www.youtube.com/watch?v=v1VgJmkEJW0)
|
||||
- [ ] [Ford-Fulkerson Algorithm (video)](https://www.youtube.com/watch?v=v1VgJmkEJW0)
|
||||
- [ ] [Network Flows (video)](https://www.youtube.com/watch?v=2vhN4Ice5jI)
|
||||
- [ ] [Ford-Fulkerson in 5 minutes (video)](https://www.youtube.com/watch?v=v1VgJmkEJW0).
|
||||
- [ ] [Ford-Fulkerson Algorithm (video)](https://www.youtube.com/watch?v=v1VgJmkEJW0).
|
||||
- [ ] [Network Flows (video)](https://www.youtube.com/watch?v=2vhN4Ice5jI).
|
||||
|
||||
- ### Conjuntos disjuntos & Encontrar unión
|
||||
- [ ] [UCB 61B - Disjoint Sets; Sorting & selection (video)](https://www.youtube.com/watch?v=MAEGXTwmUsI&list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd&index=21)
|
||||
- [ ] [Sedgewick Algorithms - Union-Find (6 videos)](https://www.youtube.com/watch?v=8mYfZeHtdNc&list=PLe-ggMe31CTexoNYnMhbHaWhQ0dvcy43t)
|
||||
- [ ] [UCB 61B - Disjoint Sets; Sorting & selection (video)](https://www.youtube.com/watch?v=MAEGXTwmUsI&list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd&index=21).
|
||||
- [ ] [Sedgewick Algorithms - Union-Find (6 videos)](https://www.youtube.com/watch?v=8mYfZeHtdNc&list=PLe-ggMe31CTexoNYnMhbHaWhQ0dvcy43t).
|
||||
|
||||
- ### Matemáticas para procesamiento rápido
|
||||
- [ ] [Integer Arithmetic, Karatsuba Multiplication (video)](https://www.youtube.com/watch?v=eCaXlAaN2uE&index=11&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
|
||||
- [ ] [The Chinese Remainder Theorem (used in cryptography) (video)](https://www.youtube.com/watch?v=ru7mWZJlRQg)
|
||||
- [ ] [Integer Arithmetic, Karatsuba Multiplication (video)](https://www.youtube.com/watch?v=eCaXlAaN2uE&index=11&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb).
|
||||
- [ ] [The Chinese Remainder Theorem (used in cryptography) (video)](https://www.youtube.com/watch?v=ru7mWZJlRQg).
|
||||
|
||||
- ### Treap
|
||||
- Combinación de un árbol de búsqueda binaria y un montículo
|
||||
- [ ] [Treap](https://en.wikipedia.org/wiki/Treap)
|
||||
- [ ] [Data Structures: Treaps explained (video)](https://www.youtube.com/watch?v=6podLUYinH8)
|
||||
- [ ] [Applications in set operations](https://www.cs.cmu.edu/~scandal/papers/treaps-spaa98.pdf)
|
||||
- Combinación de un árbol de búsqueda binaria y un montículo.
|
||||
- [ ] [Treap](https://en.wikipedia.org/wiki/Treap).
|
||||
- [ ] [Data Structures: Treaps explained (video)](https://www.youtube.com/watch?v=6podLUYinH8).
|
||||
- [ ] [Applications in set operations](https://www.cs.cmu.edu/~scandal/papers/treaps-spaa98.pdf).
|
||||
|
||||
- ### Programación Lineal (videos)
|
||||
- [ ] [Linear Programming](https://www.youtube.com/watch?v=M4K6HYLHREQ)
|
||||
- [ ] [Finding minimum cost](https://www.youtube.com/watch?v=2ACJ9ewUC6U)
|
||||
- [ ] [Finding maximum value](https://www.youtube.com/watch?v=8AA_81xI3ik)
|
||||
- [ ] [Solve Linear Equations with Python - Simplex Algorithm](https://www.youtube.com/watch?v=44pAWI7v5Zk)
|
||||
- [ ] [Linear Programming](https://www.youtube.com/watch?v=M4K6HYLHREQ).
|
||||
- [ ] [Finding minimum cost](https://www.youtube.com/watch?v=2ACJ9ewUC6U).
|
||||
- [ ] [Finding maximum value](https://www.youtube.com/watch?v=8AA_81xI3ik).
|
||||
- [ ] [Solve Linear Equations with Python - Simplex Algorithm](https://www.youtube.com/watch?v=44pAWI7v5Zk).
|
||||
|
||||
- ### Geometría, casco convexo (videos)
|
||||
- [ ] [Graph Alg. IV: Intro to geometric algorithms - Lecture 9](https://youtu.be/XIAQRlNkJAw?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3164)
|
||||
- [ ] [Geometric Algorithms: Graham & Jarvis - Lecture 10](https://www.youtube.com/watch?v=J5aJEcOr6Eo&index=10&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm)
|
||||
- [ ] [Divide & Conquer: Convex Hull, Median Finding](https://www.youtube.com/watch?v=EzeYI7p9MjU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=2)
|
||||
- [ ] [Graph Alg. IV: Intro to geometric algorithms - Lecture 9](https://youtu.be/XIAQRlNkJAw?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3164).
|
||||
- [ ] [Geometric Algorithms: Graham & Jarvis - Lecture 10](https://www.youtube.com/watch?v=J5aJEcOr6Eo&index=10&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm).
|
||||
- [ ] [Divide & Conquer: Convex Hull, Median Finding](https://www.youtube.com/watch?v=EzeYI7p9MjU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=2).
|
||||
|
||||
- ### Matemáticas discretas
|
||||
- Vea videos debajo
|
||||
- Vea videos debajo.
|
||||
|
||||
- ### Aprendizaje automático
|
||||
- [ ] ¿Por qué el aprendizaje automático?
|
||||
- [ ] [How Google Is Remaking Itself As A Machine Learning First Company](https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70)
|
||||
- [ ] [Large-Scale Deep Learning for Intelligent Computer Systems (video)](https://www.youtube.com/watch?v=QSaZGT4-6EY)
|
||||
- [ ] [Deep Learning and Understandability versus Software Engineering and Verification by Peter Norvig](https://www.youtube.com/watch?v=X769cyzBNVw)
|
||||
- [ ] [Google's Cloud Machine learning tools (video)](https://www.youtube.com/watch?v=Ja2hxBAwG_0)
|
||||
- [ ] [Google Developers' Machine Learning Recipes (Scikit Learn & Tensorflow) (video)](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
|
||||
- [ ] [Tensorflow (video)](https://www.youtube.com/watch?v=oZikw5k_2FM)
|
||||
- [ ] [Tensorflow Tutorials](https://www.tensorflow.org/versions/r0.11/tutorials/index.html)
|
||||
- [ ] [Practical Guide to implementing Neural Networks in Python (using Theano)](http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/)
|
||||
- [ ] [How Google Is Remaking Itself As A Machine Learning First Company](https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70).
|
||||
- [ ] [Large-Scale Deep Learning for Intelligent Computer Systems (video)](https://www.youtube.com/watch?v=QSaZGT4-6EY).
|
||||
- [ ] [Deep Learning and Understandability versus Software Engineering and Verification by Peter Norvig](https://www.youtube.com/watch?v=X769cyzBNVw).
|
||||
- [ ] [Google's Cloud Machine learning tools (video)](https://www.youtube.com/watch?v=Ja2hxBAwG_0).
|
||||
- [ ] [Google Developers' Machine Learning Recipes (Scikit Learn & Tensorflow) (video)](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal).
|
||||
- [ ] [Tensorflow (video)](https://www.youtube.com/watch?v=oZikw5k_2FM).
|
||||
- [ ] [Tensorflow Tutorials](https://www.tensorflow.org/versions/r0.11/tutorials/index.html).
|
||||
- [ ] [Practical Guide to implementing Neural Networks in Python (using Theano)](http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/).
|
||||
- Cursos:
|
||||
- [Great starter course: Machine Learning](https://www.coursera.org/learn/machine-learning)
|
||||
- [videos only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
|
||||
- Vea los videos 12-18 para un resumen de algebra lineal (14 y 15 son duplicados)
|
||||
- [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks)
|
||||
- [Google's Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning--ud730)
|
||||
- [Google/Kaggle Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009)
|
||||
- [Self-Driving Car Engineer Nanodegree](https://www.udacity.com/drive)
|
||||
- [Metis Online Course ($99 for 2 months)](http://www.thisismetis.com/explore-data-science)
|
||||
- [Great starter course: Machine Learning](https://www.coursera.org/learn/machine-learning).
|
||||
- [videos only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW).
|
||||
- Vea los videos 12-18 para un resumen de algebra lineal (14 y 15 son duplicados).
|
||||
- [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks).
|
||||
- [Google's Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning--ud730).
|
||||
- [Google/Kaggle Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009).
|
||||
- [Self-Driving Car Engineer Nanodegree](https://www.udacity.com/drive).
|
||||
- [Metis Online Course ($99 for 2 months)](http://www.thisismetis.com/explore-data-science).
|
||||
- Recursos:
|
||||
- Libros:
|
||||
- [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/)
|
||||
- [Data Science from Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X)
|
||||
- [Introduction to Machine Learning with Python](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/)
|
||||
- [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
|
||||
- Data School: http://www.dataschool.io/
|
||||
- [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/).
|
||||
- [Data Science from Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X).
|
||||
- [Introduction to Machine Learning with Python](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/).
|
||||
- [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers).
|
||||
- Data School: [http://www.dataschool.io/](http://www.dataschool.io/).
|
||||
|
||||
--
|
||||
|
||||
## Detalles adicionales de ciertos temas
|
||||
|
||||
Agregué estos para reforzar algunas ideas ya presentadas anteriormente, pero no quería incluirlas arriba porque es demasiado. Es fácil exagerar en un tema.
|
||||
¿Quieres ser contratado en este siglo, verdad?
|
||||
Agregué estos para reforzar algunas ideas ya presentadas anteriormente, pero no quería incluirlas arriba porque es demasiado. Es fácil exagerar en un tema. ¿Quieres ser contratado en este siglo, verdad?
|
||||
|
||||
- [ ] **Union-Find**
|
||||
- [ ] [Overview](https://www.coursera.org/learn/data-structures/lecture/JssSY/overview)
|
||||
- [ ] [Naive Implementation](https://www.coursera.org/learn/data-structures/lecture/EM5D0/naive-implementations)
|
||||
- [ ] [Trees](https://www.coursera.org/learn/data-structures/lecture/Mxu0w/trees)
|
||||
- [ ] [Union By Rank](https://www.coursera.org/learn/data-structures/lecture/qb4c2/union-by-rank)
|
||||
- [ ] [Path Compression](https://www.coursera.org/learn/data-structures/lecture/Q9CVI/path-compression)
|
||||
- [ ] [Analysis Options](https://www.coursera.org/learn/data-structures/lecture/GQQLN/analysis-optional)
|
||||
- [ ] [Overview](https://www.coursera.org/learn/data-structures/lecture/JssSY/overview).
|
||||
- [ ] [Naive Implementation](https://www.coursera.org/learn/data-structures/lecture/EM5D0/naive-implementations).
|
||||
- [ ] [Trees](https://www.coursera.org/learn/data-structures/lecture/Mxu0w/trees).
|
||||
- [ ] [Union By Rank](https://www.coursera.org/learn/data-structures/lecture/qb4c2/union-by-rank).
|
||||
- [ ] [Path Compression](https://www.coursera.org/learn/data-structures/lecture/Q9CVI/path-compression).
|
||||
- [ ] [Analysis Options](https://www.coursera.org/learn/data-structures/lecture/GQQLN/analysis-optional).
|
||||
|
||||
- [ ] **More Dynamic Programming** (videos)
|
||||
- [ ] [6.006: Dynamic Programming I: Fibonacci, Shortest Paths](https://www.youtube.com/watch?v=OQ5jsbhAv_M&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=19)
|
||||
- [ ] [6.006: Dynamic Programming II: Text Justification, Blackjack](https://www.youtube.com/watch?v=ENyox7kNKeY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=20)
|
||||
- [ ] [6.006: DP III: Parenthesization, Edit Distance, Knapsack](https://www.youtube.com/watch?v=ocZMDMZwhCY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=21)
|
||||
- [ ] [6.006: DP IV: Guitar Fingering, Tetris, Super Mario Bros.](https://www.youtube.com/watch?v=tp4_UXaVyx8&index=22&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
|
||||
- [ ] [6.046: Dynamic Programming & Advanced DP](https://www.youtube.com/watch?v=Tw1k46ywN6E&index=14&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
|
||||
- [ ] [6.046: Dynamic Programming: All-Pairs Shortest Paths](https://www.youtube.com/watch?v=NzgFUwOaoIw&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=15)
|
||||
- [ ] [6.046: Dynamic Programming (student recitation)](https://www.youtube.com/watch?v=krZI60lKPek&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=12)
|
||||
- [ ] [6.006: Dynamic Programming I: Fibonacci, Shortest Paths](https://www.youtube.com/watch?v=OQ5jsbhAv_M&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=19).
|
||||
- [ ] [6.006: Dynamic Programming II: Text Justification, Blackjack](https://www.youtube.com/watch?v=ENyox7kNKeY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=20).
|
||||
- [ ] [6.006: DP III: Parenthesization, Edit Distance, Knapsack](https://www.youtube.com/watch?v=ocZMDMZwhCY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=21).
|
||||
- [ ] [6.006: DP IV: Guitar Fingering, Tetris, Super Mario Bros.](https://www.youtube.com/watch?v=tp4_UXaVyx8&index=22&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb).
|
||||
- [ ] [6.046: Dynamic Programming & Advanced DP](https://www.youtube.com/watch?v=Tw1k46ywN6E&index=14&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp).
|
||||
- [ ] [6.046: Dynamic Programming: All-Pairs Shortest Paths](https://www.youtube.com/watch?v=NzgFUwOaoIw&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=15).
|
||||
- [ ] [6.046: Dynamic Programming (student recitation)](https://www.youtube.com/watch?v=krZI60lKPek&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=12).
|
||||
|
||||
- [ ] **Advanced Graph Processing** (videos)
|
||||
- [ ] [Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=mUBmcbbJNf4&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=27)
|
||||
- [ ] [Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=kQ-UQAzcnzA&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=28)
|
||||
- [ ] [Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=mUBmcbbJNf4&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=27).
|
||||
- [ ] [Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=kQ-UQAzcnzA&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=28).
|
||||
|
||||
- [ ] MIT **Probability** (Matemático y va lento, que es bueno para las cosas matemáticas) (videos):
|
||||
- [ ] [MIT 6.042J - Probability Introduction](https://www.youtube.com/watch?v=SmFwFdESMHI&index=18&list=PLB7540DEDD482705B)
|
||||
- [ ] [MIT 6.042J - Conditional Probability](https://www.youtube.com/watch?v=E6FbvM-FGZ8&index=19&list=PLB7540DEDD482705B)
|
||||
- [ ] [MIT 6.042J - Independence](https://www.youtube.com/watch?v=l1BCv3qqW4A&index=20&list=PLB7540DEDD482705B)
|
||||
- [ ] [MIT 6.042J - Random Variables](https://www.youtube.com/watch?v=MOfhhFaQdjw&list=PLB7540DEDD482705B&index=21)
|
||||
- [ ] [MIT 6.042J - Expectation I](https://www.youtube.com/watch?v=gGlMSe7uEkA&index=22&list=PLB7540DEDD482705B)
|
||||
- [ ] [MIT 6.042J - Expectation II](https://www.youtube.com/watch?v=oI9fMUqgfxY&index=23&list=PLB7540DEDD482705B)
|
||||
- [ ] [MIT 6.042J - Large Deviations](https://www.youtube.com/watch?v=q4mwO2qS2z4&index=24&list=PLB7540DEDD482705B)
|
||||
- [ ] [MIT 6.042J - Random Walks](https://www.youtube.com/watch?v=56iFMY8QW2k&list=PLB7540DEDD482705B&index=25)
|
||||
- [ ] [MIT 6.042J - Probability Introduction](https://www.youtube.com/watch?v=SmFwFdESMHI&index=18&list=PLB7540DEDD482705B).
|
||||
- [ ] [MIT 6.042J - Conditional Probability](https://www.youtube.com/watch?v=E6FbvM-FGZ8&index=19&list=PLB7540DEDD482705B).
|
||||
- [ ] [MIT 6.042J - Independence](https://www.youtube.com/watch?v=l1BCv3qqW4A&index=20&list=PLB7540DEDD482705B).
|
||||
- [ ] [MIT 6.042J - Random Variables](https://www.youtube.com/watch?v=MOfhhFaQdjw&list=PLB7540DEDD482705B&index=21).
|
||||
- [ ] [MIT 6.042J - Expectation I](https://www.youtube.com/watch?v=gGlMSe7uEkA&index=22&list=PLB7540DEDD482705B).
|
||||
- [ ] [MIT 6.042J - Expectation II](https://www.youtube.com/watch?v=oI9fMUqgfxY&index=23&list=PLB7540DEDD482705B).
|
||||
- [ ] [MIT 6.042J - Large Deviations](https://www.youtube.com/watch?v=q4mwO2qS2z4&index=24&list=PLB7540DEDD482705B).
|
||||
- [ ] [MIT 6.042J - Random Walks](https://www.youtube.com/watch?v=56iFMY8QW2k&list=PLB7540DEDD482705B&index=25).
|
||||
|
||||
- [ ] [Simonson: Approximation Algorithms (video)](https://www.youtube.com/watch?v=oDniZCmNmNw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=19)
|
||||
- [ ] [Simonson: Approximation Algorithms (video)](https://www.youtube.com/watch?v=oDniZCmNmNw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=19).
|
||||
|
||||
- [ ] **String Matching**
|
||||
- [ ] Rabin-Karp (videos):
|
||||
- [Rabin Karps Algorithm](https://www.coursera.org/learn/data-structures/lecture/c0Qkw/rabin-karps-algorithm)
|
||||
- [Precomputing](https://www.coursera.org/learn/data-structures/lecture/nYrc8/optimization-precomputation)
|
||||
- [Optimization: Implementation and Analysis](https://www.coursera.org/learn/data-structures/lecture/h4ZLc/optimization-implementation-and-analysis)
|
||||
- [Table Doubling, Karp-Rabin](https://www.youtube.com/watch?v=BRO7mVIFt08&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=9)
|
||||
- [Rolling Hashes, Amortized Analysis](https://www.youtube.com/watch?v=w6nuXg0BISo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=32)
|
||||
- [Rabin Karps Algorithm](https://www.coursera.org/learn/data-structures/lecture/c0Qkw/rabin-karps-algorithm).
|
||||
- [Precomputing](https://www.coursera.org/learn/data-structures/lecture/nYrc8/optimization-precomputation).
|
||||
- [Optimization: Implementation and Analysis](https://www.coursera.org/learn/data-structures/lecture/h4ZLc/optimization-implementation-and-analysis).
|
||||
- [Table Doubling, Karp-Rabin](https://www.youtube.com/watch?v=BRO7mVIFt08&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=9).
|
||||
- [Rolling Hashes, Amortized Analysis](https://www.youtube.com/watch?v=w6nuXg0BISo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=32).
|
||||
- [ ] Knuth-Morris-Pratt (KMP):
|
||||
- [TThe Knuth-Morris-Pratt (KMP) String Matching Algorithm](https://www.youtube.com/watch?v=5i7oKodCRJo)
|
||||
- [ ] Boyer–Moore string search algorithm
|
||||
- [Boyer-Moore String Search Algorithm](https://en.wikipedia.org/wiki/Boyer%E2%80%93Moore_string_search_algorithm)
|
||||
- [Advanced String Searching Boyer-Moore-Horspool Algorithms (video)](https://www.youtube.com/watch?v=QDZpzctPf10)
|
||||
- [ ] [Coursera: Algorithms on Strings](https://www.coursera.org/learn/algorithms-on-strings/home/week/1)
|
||||
- Inicia bien, pero en el momento en que pasa KMP se vuelve más complicado de lo que necesita ser
|
||||
- Buena explicación de los tries
|
||||
- Puede saltarse
|
||||
- [TThe Knuth-Morris-Pratt (KMP) String Matching Algorithm](https://www.youtube.com/watch?v=5i7oKodCRJo).
|
||||
- [ ] Boyer–Moore string search algorithm:
|
||||
- [Boyer-Moore String Search Algorithm](https://en.wikipedia.org/wiki/Boyer%E2%80%93Moore_string_search_algorithm).
|
||||
- [Advanced String Searching Boyer-Moore-Horspool Algorithms (video)](https://www.youtube.com/watch?v=QDZpzctPf10).
|
||||
- [ ] [Coursera: Algorithms on Strings](https://www.coursera.org/learn/algorithms-on-strings/home/week/1):
|
||||
- Inicia bien, pero en el momento en que pasa KMP se vuelve más complicado de lo que necesita ser.
|
||||
- Buena explicación de los tries.
|
||||
- Puede saltarse.
|
||||
|
||||
- [ ] **Sorting**
|
||||
|
||||
- [ ] Stanford lectures on sorting:
|
||||
- [ ] [Lecture 15 | Programming Abstractions (video)](https://www.youtube.com/watch?v=ENp00xylP7c&index=15&list=PLFE6E58F856038C69)
|
||||
- [ ] [Lecture 16 | Programming Abstractions (video)](https://www.youtube.com/watch?v=y4M9IVgrVKo&index=16&list=PLFE6E58F856038C69)
|
||||
- [ ] [Lecture 15 | Programming Abstractions (video)](https://www.youtube.com/watch?v=ENp00xylP7c&index=15&list=PLFE6E58F856038C69).
|
||||
- [ ] [Lecture 16 | Programming Abstractions (video)](https://www.youtube.com/watch?v=y4M9IVgrVKo&index=16&list=PLFE6E58F856038C69).
|
||||
- [ ] Shai Simonson, [Aduni.org](http://www.aduni.org/):
|
||||
- [ ] [Algorithms - Sorting - Lecture 2 (video)](https://www.youtube.com/watch?v=odNJmw5TOEE&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=2)
|
||||
- [ ] [Algorithms - Sorting II - Lecture 3 (video)](https://www.youtube.com/watch?v=hj8YKFTFKEE&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=3)
|
||||
- [ ] [Algorithms - Sorting - Lecture 2 (video)](https://www.youtube.com/watch?v=odNJmw5TOEE&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=2).
|
||||
- [ ] [Algorithms - Sorting II - Lecture 3 (video)](https://www.youtube.com/watch?v=hj8YKFTFKEE&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=3).
|
||||
- [ ] Steven Skiena lectures on sorting:
|
||||
- [ ] [lecture begins at 26:46 (video)](https://youtu.be/ute-pmMkyuk?list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&t=1600)
|
||||
- [ ] [lecture begins at 27:40 (video)](https://www.youtube.com/watch?v=yLvp-pB8mak&index=8&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b)
|
||||
- [ ] [lecture begins at 35:00 (video)](https://www.youtube.com/watch?v=q7K9otnzlfE&index=9&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b)
|
||||
- [ ] [lecture begins at 23:50 (video)](https://www.youtube.com/watch?v=TvqIGu9Iupw&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=10)
|
||||
- [ ] [lecture begins at 26:46 (video)](https://youtu.be/ute-pmMkyuk?list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&t=1600).
|
||||
- [ ] [lecture begins at 27:40 (video)](https://www.youtube.com/watch?v=yLvp-pB8mak&index=8&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b).
|
||||
- [ ] [lecture begins at 35:00 (video)](https://www.youtube.com/watch?v=q7K9otnzlfE&index=9&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b).
|
||||
- [ ] [lecture begins at 23:50 (video)](https://www.youtube.com/watch?v=TvqIGu9Iupw&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=10).
|
||||
|
||||
## Series de videos
|
||||
|
||||
Siéntese y disfrute. "Netflix and skill" :P
|
||||
|
||||
- [ ] [List of individual Dynamic Programming problems (each is short)](https://www.youtube.com/playlist?list=PLrmLmBdmIlpsHaNTPP_jHHDx_os9ItYXr)
|
||||
|
||||
- [ ] [x86 Architecture, Assembly, Applications (11 videos)](https://www.youtube.com/playlist?list=PL038BE01D3BAEFDB0)
|
||||
|
||||
- [ ] [MIT 18.06 Linear Algebra, Spring 2005 (35 videos)](https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8)
|
||||
|
||||
- [ ] [Excellent - MIT Calculus Revisited: Single Variable Calculus](https://www.youtube.com/playlist?list=PL3B08AE665AB9002A)
|
||||
|
||||
- [ ] [Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory](https://www.youtube.com/playlist?list=PL-XXv-cvA_iD8wQm8U0gG_Z1uHjImKXFy)
|
||||
|
||||
- [ ] [Discrete Mathematics by Shai Simonson (19 videos)](https://www.youtube.com/playlist?list=PLWX710qNZo_sNlSWRMVIh6kfTjolNaZ8t)
|
||||
|
||||
- [ ] [Discrete Mathematics Part 1 by Sarada Herke (5 videos)](https://www.youtube.com/playlist?list=PLGxuz-nmYlQPOc4w1Kp2MZrdqOOm4Jxeo)
|
||||
|
||||
- [ ] CSE373 - Analysis of Algorithms (25 videos)
|
||||
- [Skiena lectures from Algorithm Design Manual](https://www.youtube.com/watch?v=ZFjhkohHdAA&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=1)
|
||||
|
||||
- [ ] [UC Berkeley 61B (Spring 2014): Data Structures (25 videos)](https://www.youtube.com/watch?v=mFPmKGIrQs4&list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd)
|
||||
|
||||
- [ ] [UC Berkeley 61B (Fall 2006): Data Structures (39 videos)](https://www.youtube.com/playlist?list=PL4BBB74C7D2A1049C)
|
||||
|
||||
- [ ] [UC Berkeley 61C: Machine Structures (26 videos)](https://www.youtube.com/watch?v=gJJeUFyuvvg&list=PL-XXv-cvA_iCl2-D-FS5mk0jFF6cYSJs_)
|
||||
|
||||
- [ ] [OOSE: Software Dev Using UML and Java (21 videos)](https://www.youtube.com/playlist?list=PLJ9pm_Rc9HesnkwKlal_buSIHA-jTZMpO)
|
||||
|
||||
- [ ] [UC Berkeley CS 152: Computer Architecture and Engineering (20 videos)](https://www.youtube.com/watch?v=UH0QYvtP7Rk&index=20&list=PLkFD6_40KJIwEiwQx1dACXwh-2Fuo32qr)
|
||||
|
||||
- [ ] [MIT 6.004: Computation Structures (49 videos)](https://www.youtube.com/playlist?list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-)
|
||||
|
||||
- [ ] [Carnegie Mellon - Computer Architecture Lectures (39 videos)](https://www.youtube.com/playlist?list=PL5PHm2jkkXmi5CxxI7b3JCL1TWybTDtKq)
|
||||
|
||||
- [ ] [MIT 6.006: Intro to Algorithms (47 videos)](https://www.youtube.com/watch?v=HtSuA80QTyo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&nohtml5=False)
|
||||
|
||||
- [ ] [MIT 6.033: Computer System Engineering (22 videos)](https://www.youtube.com/watch?v=zm2VP0kHl1M&list=PL6535748F59DCA484)
|
||||
|
||||
- [ ] [MIT 6.034 Artificial Intelligence, Fall 2010 (30 videos)](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi)
|
||||
|
||||
- [ ] [MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)](https://www.youtube.com/watch?v=L3LMbpZIKhQ&list=PLB7540DEDD482705B)
|
||||
|
||||
- [ ] [MIT 6.046: Design and Analysis of Algorithms (34 videos)](https://www.youtube.com/watch?v=2P-yW7LQr08&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
|
||||
|
||||
- [ ] [MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)](https://www.youtube.com/watch?v=phxsQrZQupo&list=PL_2Bwul6T-A7OldmhGODImZL8KEVE38X7)
|
||||
|
||||
- [ ] [MIT 6.851: Advanced Data Structures (22 videos)](https://www.youtube.com/watch?v=T0yzrZL1py0&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf&index=1)
|
||||
|
||||
- [ ] [MIT 6.854: Advanced Algorithms, Spring 2016 (24 videos)](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c)
|
||||
|
||||
- [ ] [Harvard COMPSCI 224: Advanced Algorithms (25 videos)](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf)
|
||||
|
||||
- [ ] [MIT 6.858 Computer Systems Security, Fall 2014](https://www.youtube.com/watch?v=GqmQg-cszw4&index=1&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
|
||||
|
||||
- [ ] [Stanford: Programming Paradigms (27 videos)](https://www.youtube.com/playlist?list=PL9D558D49CA734A02)
|
||||
|
||||
- [ ] [Introduction to Cryptography by Christof Paar](https://www.youtube.com/playlist?list=PL6N5qY2nvvJE8X75VkXglSrVhLv1tVcfy)
|
||||
- [Course Website along with Slides and Problem Sets](http://www.crypto-textbook.com/)
|
||||
|
||||
- [ ] [Mining Massive Datasets - Stanford University (94 videos)](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV)
|
||||
|
||||
- [ ] [Graph Theory by Sarada Herke (67 videos)](https://www.youtube.com/user/DrSaradaHerke/playlists?shelf_id=5&view=50&sort=dd)
|
||||
- [ ] [List of individual Dynamic Programming problems (each is short)](https://www.youtube.com/playlist?list=PLrmLmBdmIlpsHaNTPP_jHHDx_os9ItYXr).
|
||||
- [ ] [x86 Architecture, Assembly, Applications (11 videos)](https://www.youtube.com/playlist?list=PL038BE01D3BAEFDB0).
|
||||
- [ ] [MIT 18.06 Linear Algebra, Spring 2005 (35 videos)](https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8).
|
||||
- [ ] [Excellent - MIT Calculus Revisited: Single Variable Calculus](https://www.youtube.com/playlist?list=PL3B08AE665AB9002A).
|
||||
- [ ] [Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory](https://www.youtube.com/playlist?list=PL-XXv-cvA_iD8wQm8U0gG_Z1uHjImKXFy).
|
||||
- [ ] [Discrete Mathematics by Shai Simonson (19 videos)](https://www.youtube.com/playlist?list=PLWX710qNZo_sNlSWRMVIh6kfTjolNaZ8t).
|
||||
- [ ] [Discrete Mathematics Part 1 by Sarada Herke (5 videos)](https://www.youtube.com/playlist?list=PLGxuz-nmYlQPOc4w1Kp2MZrdqOOm4Jxeo).
|
||||
- [ ] CSE373 - Analysis of Algorithms (25 videos):
|
||||
- [Skiena lectures from Algorithm Design Manual](https://www.youtube.com/watch?v=ZFjhkohHdAA&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=1).
|
||||
- [ ] [UC Berkeley 61B (Spring 2014): Data Structures (25 videos)](https://www.youtube.com/watch?v=mFPmKGIrQs4&list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd).
|
||||
- [ ] [UC Berkeley 61B (Fall 2006): Data Structures (39 videos)](https://www.youtube.com/playlist?list=PL4BBB74C7D2A1049C).
|
||||
- [ ] [UC Berkeley 61C: Machine Structures (26 videos)](https://www.youtube.com/watch?v=gJJeUFyuvvg&list=PL-XXv-cvA_iCl2-D-FS5mk0jFF6cYSJs_).
|
||||
- [ ] [OOSE: Software Dev Using UML and Java (21 videos)](https://www.youtube.com/playlist?list=PLJ9pm_Rc9HesnkwKlal_buSIHA-jTZMpO).
|
||||
- [ ] [UC Berkeley CS 152: Computer Architecture and Engineering (20 videos)](https://www.youtube.com/watch?v=UH0QYvtP7Rk&index=20&list=PLkFD6_40KJIwEiwQx1dACXwh-2Fuo32qr).
|
||||
- [ ] [MIT 6.004: Computation Structures (49 videos)](https://www.youtube.com/playlist?list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-).
|
||||
- [ ] [Carnegie Mellon - Computer Architecture Lectures (39 videos)](https://www.youtube.com/playlist?list=PL5PHm2jkkXmi5CxxI7b3JCL1TWybTDtKq).
|
||||
- [ ] [MIT 6.006: Intro to Algorithms (47 videos)](https://www.youtube.com/watch?v=HtSuA80QTyo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&nohtml5=False).
|
||||
- [ ] [MIT 6.033: Computer System Engineering (22 videos)](https://www.youtube.com/watch?v=zm2VP0kHl1M&list=PL6535748F59DCA484).
|
||||
- [ ] [MIT 6.034 Artificial Intelligence, Fall 2010 (30 videos)](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi).
|
||||
- [ ] [MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)](https://www.youtube.com/watch?v=L3LMbpZIKhQ&list=PLB7540DEDD482705B).
|
||||
- [ ] [MIT 6.046: Design and Analysis of Algorithms (34 videos)](https://www.youtube.com/watch?v=2P-yW7LQr08&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp).
|
||||
- [ ] [MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)](https://www.youtube.com/watch?v=phxsQrZQupo&list=PL_2Bwul6T-A7OldmhGODImZL8KEVE38X7).
|
||||
- [ ] [MIT 6.851: Advanced Data Structures (22 videos)](https://www.youtube.com/watch?v=T0yzrZL1py0&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf&index=1).
|
||||
- [ ] [MIT 6.854: Advanced Algorithms, Spring 2016 (24 videos)](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c).
|
||||
- [ ] [Harvard COMPSCI 224: Advanced Algorithms (25 videos)](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf).
|
||||
- [ ] [MIT 6.858 Computer Systems Security, Fall 2014](https://www.youtube.com/watch?v=GqmQg-cszw4&index=1&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh).
|
||||
- [ ] [Stanford: Programming Paradigms (27 videos)](https://www.youtube.com/playlist?list=PL9D558D49CA734A02).
|
||||
- [ ] [Introduction to Cryptography by Christof Paar](https://www.youtube.com/playlist?list=PL6N5qY2nvvJE8X75VkXglSrVhLv1tVcfy).
|
||||
- [Course Website along with Slides and Problem Sets](http://www.crypto-textbook.com/).
|
||||
- [ ] [Mining Massive Datasets - Stanford University (94 videos)](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV).
|
||||
- [ ] [Graph Theory by Sarada Herke (67 videos)](https://www.youtube.com/user/DrSaradaHerke/playlists?shelf_id=5&view=50&sort=dd).
|
||||
|
||||
## Cursos de Informática
|
||||
|
||||
- [Directory of Online CS Courses](https://github.com/open-source-society/computer-science)
|
||||
- [Directory of CS Courses (many with online lectures)](https://github.com/prakhar1989/awesome-courses)
|
||||
- [Directory of Online CS Courses](https://github.com/open-source-society/computer-science).
|
||||
- [Directory of CS Courses (many with online lectures)](https://github.com/prakhar1989/awesome-courses).
|
||||
|
|
|
|||
Loading…
Reference in New Issue