**TLDR at bottom
I have been searching for days and the chances are I just don't know what to google since I'm very new to machine learning. After doing research I've decided that tensorflow is a good starting point (I'm open to other suggestions). When I looked for examples of tensorflow they all required a large repository of data to feed into the program, but I'm more interested in creating AI that learns while it plays a game, such as Tic Tac Toe. I'm having difficulty figuring out how to do this. Any advice helps, thanks!
TLDR: Are there any good, simple examples of a machine learning program (preferably tensorflow) that can help me make a Tic-Tac-Toe AI.
I have also just started to learn more about machine learning, and it looks like the tutorial and library depend on the kind of machine learning you wish to pursue.
As you probably know there is supervised and unsupervised learning and reinforcement learning.
If you are curious about supervised and unsupervised learning, Tensorflow and SciKit Learn are the way to go.
If it's reinforcement learning, then openAI Gym would work best.
Here are some links to some tic-tac-toe repositories for each three.
Good Luck!
Supervised and Unsupervised Learning
https://github.com/3cky/tensorflow-rl-tictactoe (TensorFlow)
https://github.com/akapoorx00/tic-tac-toe-ml-project (SciKit Learn)
Reinforcement Learning
https://github.com/haje01/gym-tictactoe
https://gym.openai.com/docs/
A quick search brought these up:
https://github.com/jamesq9/Tic-Tac-Toe-Machine-Learning-Using-Reinforcement-Learning
https://github.com/3cky/tensorflow-rl-tictactoe
Related
I am trying to explore computer vision using deep learning techniques. I have gone through basic literature, made a NN of my own to classify digits using MNIST data(without using any library like TF,Keras etc, and in the process understood concepts like loss function, optimization, backward propagation etc), and then also explored Fashion MNIST using TF Keras.
I applied my knowledge gained so far to solve a Kaggle problem(identifying a plant type), but results are not very encouraging.
So, what should be my next step in progress? What should I do to improve my knowledge and models to solve more complex problems? What more books, literature etc should I read to move ahead of beginner stage?
You should try hyperparameter tuning, it will help improve your model performance. Feel free to surf around various articles, fine tuning your model will be the next step as you have fundamental knowledge regarding how model works.
I'm trying to control an actual robot manipulator using reinforcement learning. For reinforcement learning, I'm using Google tensorflow.
To control a robot manipulator, I need my controller to have real-time capability. However, as far as I know, python and thus tensorflow is not real-time friendly. I want to control the robot at about 100 ~ 1000 Hz.
I've considered implementing my own reinforcement learning algorithm in C++, but it would be too much work, and take too much time.
Is there anyway of using Tensorflow reinforcement learning algorithms in C++? Or, is there any other way of implementing reinforcement learning algorithm to a C++ real-time controller?
Any help would be appreciated.
Sincerely,
Steve
I don't see a reason why Tensorflow is not good for real-time control since a TF model is not subject to the limitations of the Python interpreter.
In case you find that standard TF is not fast enough you can also have a look at TF-lite: https://www.tensorflow.org/lite.
I'd like to make a machine learning with Tensorflow about sentiment analysis, I know it's possible to do machine learning with tensorflow but is it possible to make machine learning suited for sentiment analysis ? I know it's possible to do sentiment analysis with Convolutional Neuronal Network (Deep learning so) with Tensor flow but I'm looking for a solution that only uses Machine learning algorithm, not deep learning.
Would you know great tutorials to begin a GCP Machine Learning project ? Is it possible to begin a GCP Machine Learning Project without using google API or Tensorflow (just in "regular" python but which can be linked to other services like google big query, data prep) ?
Thank you very much for your answers :)
In general it wouldn't make much sense to use TensorFlow for non-deep learning solutions. You can always try scikit-learn for implementing other machine learning techniques with python, but I do not think that you could get far with sentiment analysis this way. For a nice introduction on sentiment analysis with TensorFlow I would suggest to go through this guide.
As for machine learning on google cloud platform: there is a number of APIs hosted in the platform which use pre-built models, including cloud natural language which has an analyzeSentiment method. Here you can find a python tutorial on how to do sentiment analysis using the python client library of the cloud natural language api.
If you still want to try and build your own model, you can train (with either scikit-learn or tensorflow), deploy and then use it for predictions on the cloud with the google cloud machine learning engine. As for tutorials, I would suggest that you visit the official documentations of any of the above products/services, you will find there comprehensive step-by-step guides for all levels.
The tensorflow.Estimator seems to gear toward supervised learning but seems to difficult to adopt for other task even if only small changes are needed for the model or training. For example, in reinforcement learning, I would need to feed a reward value which is not part of the features.
Estimators were meant for good-old supervised learning. So, adopting them to reinforcement learning will likely feel awkward. Here is an RL library that might be useful: https://github.com/tensorflow/agents.
I am able to do supervised learning for Facial Recognition with TensorFlow and especially after reading this great tutorial. I highly recommend the tutorial even if you don't intend on running the code.
Does anyone know of an open sourced library that does clustering of images based on facial recognition similar to Google Photos for instance? Or even a tutorial with sample code?
I found a good tutorial on autoencoders, so building my own is an option, but I wanted to check if there's something open sourced, or some tutorial that has code I maybe able to borrow?