I want to make a AI gym Trainer app.the purpose of this app will be to correct the poses of the person performing the exercise, he/she will perform the exercise in front of the camera and the model will then evaluate it and provide guidance for correcting it. I have one issue that i am not fully aware what the Deep Learning Field has to offer.
My questions:
Are models capable of comparing the input pose with what it has learned through the dataset and provide feedback?
Can a model show the pose for correcting the user through any visual means, like by showing the skeleton structure to user performing that particular exercise?
Related
I have a cctv video where I want to identify a person. I tried both facial recognition and object tracking but both failed to produce high accuracy since the quality of the frame isn't great and the face disappears from the frame sometimes.
I have simplified the problem as much as I can and now thinking about training a YOLOV3 on the person and do object tracking or training on Resnet50 as a classification problem.
I have also looked into re-identification but not sure if it will work in this use case or not.
So the problem now is simplified to given an image of people and objects in hostile environment, how do you find and identify specific person?
thanks
It seems that deep learning is precisely the tool to use for identifying a specific person. And without facial recognition that seems impossible, unless the person wears the same clothes every time and that's your criteria for "specific person".
Consider using Face-API.js -- you provide several photos of the specific person and you can then detect whether they are in a particular image.
If you are still open to use video as input and not a specific frame you can look into person identification through gait.
One example of a deep learning implementation would be:
https://github.com/marian-margeta/gait-recognition
I'm new in the field of reinforcement learning. So I'm quite confused with "model based" or "model free" terms.
For example, in a video game, if I want to train an agent (a car) to drive on a racetrack.
If my input is a 256x256x3 first person image of the game, should I use a model free RL algorithm ?
And if I want to do the same, but with a 3rd person view above the racetrack, knowing coordinates, speed of the car and all obstacles, etc..., should I use model based RL ?
Thank you for your time.
In model-based you learn a model of the dynamics of your system and use it for planning or for generating "fake" samples. If you can learn the dynamics well, it can be extremely helpful, but if your model is wrong then it can me disastrous.
That said, there is no general rule for when to use model-free or model-based. Usually it depends on how much prior knowledge you have that can help you learning a good dynamics model.
Deep Reinforcement Learning can be very useful in applying it to real-world problems which have highly dynamic nature. Few examples can be listed as is finance, healthcare etc. But when it comes to these kinds of problems it is hard to have a simulated environment. So what are the possible things to do?
Let me first comment a couple concepts trying to give you future research directions according to your comments:
Probably the term "forecast" is not suitable to describe the kind of problems solved by Reinforcement Learning. In some sense, RL needs to do an internal forecast process to choose the best actions in the long term. But the problem solved is an agent choosing actions in an environment. So, if your problem is a forecast problem, maybe other techniques are more suitable than RL.
Between tabular methods and deep Q-learning there exist many other methods that maybe are more suitable to your problem. They are probably less powerful but easy to use (more stable, less parameter tuning, etc.) You can combine Q-learning with other function approximators (simpler than a deep neural network). In general, the best choice is the simplest one able to solve the problem.
I don't know how to simulate the problem of human activities with first-person vision. In fact, I don't fully understand the problem setup.
And regarding to the original question of applying RL without accessing a simulated environment, as I previously said in the comments, if you have enough data, you could probably apply an RL algorithm. I'm assuming that you can store data from your environment but you can not easily interact with it. This is typical, for example, in medical domains where there exist many data about [patient status, treatment, next patient status], but you can not interact with patients by applying random treatments. In this situation, there are some facts to take into account:
RL methods generally consume a very large quantity of data. This is specially true when combined with deep nets. How much data it is necessary depends totally of the problem, but be ready to store millions of tuples [state, action, next state] if your environment is complex.
The stored tuples should be collected with a policy which contains some exploratory actions. RL algorithm will try to find the best possible actions among the ones contained in the data. If the agent can interact with the environment, it should choose exploratory actions to find the best one. Similarly, if the agent cannot interact and instead the data is gathered in advance, this data also should contain exploratory actions. The papers Neural Fitted Q Iteration - First Experiences
with a Data Efficient Neural Reinforcement
Learning Method and Tree-Based Batch Mode Reinforcement Learning could be helpful to understand these concepts.
I wanted to learn to predict future events like......being able to predict number of plane crashes in 2018 using past two decades of plane crash data.....or.....predict how many tee-shirts with justin beibers face on it will be sold by 2018 depending upon fan base from previuos data..........or how many iphones 8's and samsungs s9's will be sold if they decide to launch on the same exact date....predicting somewhat accurate whole sale market.....stuff like that....please suggest a book...i really love head first series....is head first data analysis right for me? ....I dont lnow if i can ask questions other than programming here or not.....but here i am.....By the way does big data have anything to do with this?
it all falls in the category of data science (which is big data and data analysis). What you need for predictions and such stuff is some machine learning approach to data you have or can access about stuff you want to predict.
I'd recommend this, newest series of articles: https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
Apart from really nice intro, you'll find lots of resources for further learning there.
Also I highly recommend deeplearning.ai and machine learning course from Stanford you can find on Coursera.
Cheers!
I think most of the scenarios that you have asked are a case of Supervised Learning which is a type of machine learning, wherein you have previous data to train your machine learning model with the input and output values and once you have trained a model you feed new input values and it gives you the output which is the prediction.
I would highly recommend the following Machine Learning course by Andrew NG which on Coursera which covers all the basics of ML including Supervised and Unsupervised Learning.
https://www.coursera.org/learn/machine-learning
As for the books the following link from Analytics Vidya is a great place to start with, you can go through the books as they can give you some good basics of statistics and data sciences.
https://www.analyticsvidhya.com/blog/2015/10/read-books-for-beginners-machine-learning-artificial-intelligence/
As for the differences between Data Science, Data Analytics and Big Data. Data science and data analytics are similar in the sense that they both try to find patterns in data and based on those pattern you derive some insights.
Big data on the other hand is basically Data of huge size which is distributed across multiple machines, so you can store and compute large amount of data simultaneously and in parallel.
So you may ask how is big data and machine learning related? well the answer lies in the training of machine learning model, since the accuracy of prediction is to a certain extent depends on the amount of data you train it on. So more the training data better the predictions and in terms of quantity big data way ahead of others, hence the relation.
I've follwed recent DL's proceed from NIPS.
Although i haven't track what happens in Computational Neuroscience(CN)'s field.
But I wonder why so little work about general artificial intellgence(GAI)?for example, kinds of network of hebbian to build all supervised/unsupervised/Reinforcement learning.
Or Can anyone tell me what's the state of art about neural networks for GAI, from CN field?Or some review?
Thanks a lot!
All three fields, Deep Learning (e.g. what you read at NIPS), Artificial general intelligence (AGI) and Computational Neuroscience are separate fields with some overlap but not too much overlap.
A question like 'what is the state-of-the-art' cannot really be answered in such a general form and would also soon be outdated. You have to be more specific. But just check some sources in the specific field. Wikipedia might be a good start.