Hertha  Walsh

Hertha Walsh

1602774000

Simulated Annealing x SGD x Mini-batch | Machine Learning w TensorFlow & scikit-learn

This lecture is dedicated for variations of gradient descent algorithms. We talk about Stochastic & mini-batch Gradient Descent along with Simulated Annealing. Python implementations are done on Jupyter.

#machine learning

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Simulated Annealing x SGD x Mini-batch | Machine Learning w TensorFlow & scikit-learn

Simulated Annealing x SGD x Mini-batch | Machine Learning w TensorFlow & scikit-learn

This lecture is dedicated for variations of gradient descent algorithms. We talk about Stochastic & mini-batch Gradient Descent along with Simulated Annealing. Python implementations are done on Jupyter.

⏲Outline⏲
00:00​ Introduction
00:26​ Simulated Annealing
02:42​ Stochastic Gradient Descent with variable step size
10:12​ Mini-batch Gradient Descent

Subscribe: https://www.youtube.com/channel/UCgC1d4JZ1Fz4t8MWLJD464w

#machine-learning #tensorflow #scikit-learn

CellularAutomata.jl: Cellular Automata Simulation toolkit for Julia

Cellular Automata

A cellular automaton is a collection of "colored" cells on a grid of specified shape that evolves through a number of discrete time steps according to a set of rules based on the states of neighboring cells. The rules are then applied iteratively for as many time steps as desired.

mathworld.wolfram.com/CellularAutomaton

Elementary CA

To generate an elementary cellular automaton, use

ca = CellularAutomaton(rule, init, gen)

where rule is the Wolfram code (integer), init is a vector containing the initial starting condition and gen is the number of generations to be computed. For a single starting cell in the middle just omit the init vector.

To generate 15 generations of elementary cellular automaton of rule 90 use

using CellularAutomata

ca90 = CellularAutomaton(90, 16)
                            #                                    
                           # #                                   
                          #   #                                  
                         # # # #                                 
                        #       #                                
                       # #     # #                               
                      #   #   #   #                              
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                    #               #                            
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                 # # # #         # # # #                         
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              #   #   #   #   #   #   #   #                      
             # # # # # # # # # # # # # # # #                     

Totalistic CA

For a more complex cellular automaton you can change the number of states k the cell can be and the radius r of neighbors that can influence the states. If k is changed to be larger than 2, a totalistic CA is computed where only the average value of all neighbors count. This can be done like this

ca = CellularAutomaton(993, 15, k=3)
                        X                         
                       XXX                        
                      X# #X                       
                     X     X                      
                    XXX   XXX                     
                   X# #X X# #X                    
                  X     #     X                   
                 XXX   ###   XXX                  
                X# #X # X # X# #X                 
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            X     X### XXX ###X     X             
           XXX   X XX  # #  XX X   XXX            
          X# #X XX###X## ##X###XX X# #X           

2 dimensional CAs

Two dimensional cellular automaton (like Conway's Game of Life) can be created by

ca = CA2d(B, S, init, gen)

where B and S are vectors that have the numbers of neighboring cells that define when cell is born or survives, init (matrix) is the initial starting condition and gen is the number of generations the CA is to be computed.

Game of life is then run for 9 generations for e.g. a turbine pattern by typing

ca = CA2d([3], [2, 3], init, 9)

1st step

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2nd

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3rd

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4th

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5th

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6th

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7th

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Running Tests

To run tests, execute the following command from the root folder of the repository:

julia tests/run_tests.jl

Download Details:

Author: Natj
Source Code: https://github.com/natj/CellularAutomata.jl 
License: MIT license

#julia #math #toolkit 

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

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Visit Blog- https://www.xplace.com/article/8743

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Nora Joy

1604154094

Hire Machine Learning Developers in India

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Nora Joy

1607006620

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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
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  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
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**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
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Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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