OOD Readers Paradise - Reading Challenge Day 112
Good morning fellow bookworms. We express our utmost gratitude to our readers and would love more responses. For today's challenge, we want you to express your creativity through the segment,' Show your talent!!' You can write stories, poems, or anything else you like. Have fun fellow readers!!!
So dear bookworms, bring those bright colors flying out!!!
Your beloved bookworms
Aishwaryaa and Divyalakshmi
“The strength of a nation derives from the integrity of the home.”
ReplyDeleteLove your nation as much as you can. You are from your nation, you are unique, and when someone asks you your nationality, it is your nation that is the answer of it.
No matter which country you love, which style you love, who you love or from where they are, never forget to love your nation.
It is because of your nation that you belong to a certain nationality.
For example- In the future, if you become a big business person, you will be representing your nation at an international level.
Example 2- When you go for international competitions, you are sometimes a team representing your nation. And if you win, your nation is proud of you, and you must respect that.
Example 3- Mukesh Ambani is an extrememly big businessman. He is from India. People all around the world who know him, also know that he is from India, which gives India some respect.
See? In so many situations of your life, you would be thankful to your nation. For giving you its citizenship, freedom, right to vote, and a place to live.
Of course you can live in foreign countries too, but no place is the same as your home country.
It does not matter from which nation you are. All that matters is that you respect it, no matter how its condition might be. Who knows? Maybe giving it love and respect might make it better.
Therefore, never forget to keep loving your nation.
Machine Learning — The Past, Present and the Future of A.I.
ReplyDeleteMachine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
Overview of Machine Learning
Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer’s part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.
History of Machine Learning
The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. A representative book of the machine learning research during the 1960s was the Nilsson’s book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.
Theory
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as a computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias-variance decomposition is one way to quantify generalization error.
Methods
Regression.
Classification.
Clustering.
Dimensionality Reduction.
Ensemble Methods.
Neural Nets and Deep Learning.
Transfer Learning.
Reinforcement Learning.
Last but not least, I would like to end with a quote by Nick Bostrom, “Machine intelligence is the last invention that humanity will ever need to make.”