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How To Learn Artificial Intelligence? (AI) – The Next Big Thing?
Artificial Intelligence is definitely the next big thing. However, programmers still find it hard to understand what A.I. really is and how they can use that bring their programming career to the next level.
AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future.
The primary goals of A.I. include deduction and reasoning, knowledge representation, planning, natural language processing (NLP), learning, perception, and the ability to manipulate and move objects. Long-term goals of AI research include achieving Creativity, Social Intelligence, and General (human level) Intelligence.
AI has heavily influenced different sectors that we may not recognize. Ray Kurzweil says “Many thousands of AI applications are deeply embedded in the infrastructure of every industry.” John McCarthy, one of the founders of AI, once said that “as soon as it works, no one calls it AI anymore.”
(Source: https://www.hackerearth.com/blog/developers/artificial-intelligence-101-how-to-get-started/)
But… Where to start learning A.I.? How you, as a programmer, can incorporate A.I. opportunities in your career?
Is there a better programming language to learn artificial intelligence? Is there a specific technology you should learn?
Watch this video and find out!
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Source
I did a project on Image classification using deep learning.
its just maths
Love you bro! <3
I really apreciate this channel, thanks Jhon
When thinking about what to learn, also think about the domain you're interested in, not just the technology stack. What type of industry do you want to work in and what are the common stacks used in that industry.
tanned John..
AWESOME LECTURE
I'm in Data Science
With all due respect for the great work you do, this time you seem to be a bit out of your area of expertise. The fundamentals are pretty consistent, no matter to which domain you're applying them. It's not much trouble for an expert to move between problem domains, so long as they have the background to understand that domain (for example, every sighted person has a background in facial recognition while most don't have the background for drug discovery).
What we should discuss is how can a software developer get involved with ML. The easiest way is to take pre-built models and make an application that uses them in a novel way. With a little knowledge, the software developer can also re-train public models on new datasets to solve new problems. At a deeper level, a software engineer can work on a team where their responsibility is to build pipelines to automatically collect and process data, train models (that are built by other team members) and evaluate them. Still deeper, and engineer can be responsible for writing the code to actually implement algorithms that they are tasked with, probably also optimizing their implementation for different use cases (training on TPUs, inference on edge devices, distributed training… lots of things here). This path requires increasing levels of software engineering skills and also of mathematical literacy.
Note that what I described above is distinct from a data scientist or a machine learning researcher, who is usually the one tasked with deciding which algorithms are appropriate for a given task, or developing new ones. That requires much less skill in software engineering, and much more knowledge of statistics and mathematics (usually at the masters or doctoral level).
I hope this helps.
(source: several years employed as a machine learning engineer and as a graduate student at a top-1 university just outside of Boston)
I want to cure diseases
You inspired me bro thanks.