Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 3 years ago.
Improve this question
Im developing android app fro groceries shop.
How can include or what way i can use ML, AI, Deep learning in my app.
Im just beginner to ML , AI , DL. IM developing app for engineering mini project. So that parallely i can learn both things.
AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
In addition to Zunaib's answer:
Machine Learning is a subset of AI which consists of types of algorithms that are able to autonomously calculate conclusions based on the given data. Where as Deep Learning is a subset of machine learning where we use different types of Neural Networks as algorithms.
Related
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
From my understanding, artificial intelligence can be divided into two subsets, deep learning and machine learning. Which one of those categories does the minimax algorithm fall into when developing an AI to play chess?
1- AI is much wider than Machine Learning. ML is a subset of Learning, Learning is a subset of AI
2- Deep Learning is a platform for ML( if not a subset of ML), to help do the automatic feature selection at the same time as training.
3- There is not really such a boundary of which algorithm belongs to which part of AI
4- A major part of AI named "Problem Solving" in the AI modern approach book. MinMax has been discussed there if I am not mistaken
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 3 years ago.
Improve this question
I am trying to make a binary classifier using machine learning and I am trying to develop other features for my data using correlated features (numerical attributes) I have. I searched much but could not get a block of code that will work with me.
What should i do?
I've searched in dimenshionality reduction and found library (Multivariate Statistics) but actually i did not understand and i felt lost :D
No one will make a choice for you what exact method to choose. They are many, many different ways of doing a binary classification and to do feature extraction. If you feel overwhelmed by all these names that libraries such as Multivariate Statistics offer, then take a look at a textbook on statistics and machine learning, understanding the methods is independent from the programming language.
Start with some simple methods such as principal compenent analysis (PCA), (MultivariateStats.jl provides that), then test others as you gain more knowledge on your data and the methods.
Some Julia libraries to take a look at: JuliaStats (https://github.com/JuliaStats) with its parts
StatsBase for the most basic stuff
MultivariateStats for methods like PCA
StatsModels (and DataFrames) for statistical models
many more ....
For Neural Networks there are Flux.jl and KNet.jl
For Clustering there is Clustering.jl
Then, there are also bindings to the python libraries Tensorflow (Neural Networks & more) and Scikit-Learn (all kinds of ML algorithms)
There are many more projects, but these are some that I think are important.
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 4 years ago.
Improve this question
Thanks for making it this far on my post!
I am studying engineering, yet have a passion for programming and wish to implement computer science knowledge into my own research.
My question is pertaining to any resources that this community has available and any advice you all are willing to give regarding getting started in this broad field.
I’m mainly confused about ‘neural networks’ in relation to Deep Learning as well as implementation of algorithms.
I have slight Python and R knowledge.
Note: one of the subfora of StackExchange is probably a better fit for this question.
In any case, for ML you can do just fine with basic Python/R. Most of the research and work done on ML is based on TensorFlow and similar frameworks currently (2018). To use the frameworks you don't really need a strong programming background to setup and train models on them (although it certainly helps). Actually, math/statistics will help you more, specially if you want to get to the bottom of it (i.e. reading the latest articles/papers, etc.).
Mainly I’m confused about ‘neural networks’ in relation to Deep Learning
"Deep Learning" is basically taking advantage of modern computing capabilities to train complex models (e.g. neural networks with many hidden layers) which a few years ago (e.g. 10 years ago) were unfeasible. Informally speaking, the more complex your network is, the more interesting are the things that it can learn.
as well as implementation of algorithms.
Typically, you will use an existing framework -- you won't implement the algorithms yourself. Although, of course, implementing a MultiLayer Perceptron by yourself is always a good and fun learning exercise.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 4 years ago.
Improve this question
I have just completed Machine learning course from Andrew ng and would like to proceed further.
I also want the python implementation of Machine Learning from beginning so that i can practice on Kaggle.
Also, is there any better book or tutorial or some resource like that so that i can proceed further without wasting any time searching such resources.
The best book unequivocally that has implementation of Machine Learning algorithms in Python is the "Introduction to Machine Learning with Python: A Guide for Data Scientists" by Andreas C. Müller. Machine Learning algorithms in Python can be used from a package called scikit-learn. This package has everything you need for Machine Learning. All the algorithms, scaling, cross validation. And that book is written by the chief developer of scikit-learn itself.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 8 years ago.
Improve this question
(homework problem)
Which of the following problems are best suited for the learning approach?
Classifying numbers into primes and non-primes.
Detecting potential fraud in credit card charges.
Determining the time it would take a falling object to hit the ground.
Determining the optimal cycle for trafic lights in a busy intersection
I'm trying to answer your question without doing your homework.
Basically you can think of machine learning as a way to extract patterns from data where all other approaches fail.
So first clue here: If there is an analytic way to solve the problem then don't use machine learning! The analytic algorithm will likely be faster, more efficient, and 100% correct.
Second clue is: There has to be a pattern in the data. If you as a human see a pattern, machine learning can find it too. If lots of smart humans who are experts of the respective domain don't see a pattern then machine learning will most likely fail. Chaos can not be learned, i.e. classified/predicted.
That should answer your question. Make sure to also read the summary on wikipedia to get an idea whether a problem can be solved using supervised, unsupervised, or reinforcement learning.