- This is a pure Tensorflow implementation of Deep Photo Styletransfer, the torch implementation could be found here
This implementation support L-BFGS-B (which is what the original authors used) and Adam in case the ScipyOptimizerInterface incompatible when Tensorflow upgrades to higher version.
- is to generate segmented intermediate result like torch file neuralstyle_seg.
- uses this intermediate result to generate final result like torch file deepmatting_seg.
- Run to see a list of all options
This repository doesn’t offer image segmentation script and simply use the segmentation image from the torch version.
- Here are more results from tensorflow algorithm (from left to right are input, style, torch results and tensorflow results)
If you find this code useful for your research, please cite:
Feel free to contact me if there is any question (Yang Liu email@example.com).
deep-photo-styletransfer-tf – Tensorflow (Python API) implementation of Deep Photo Style Transfer
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- @drsimonj here to introduce pipelearner – a package I’m developing to make it easy to create machine learning pipelines in R – and to spread the word in the hope that some readers may be interested in contributing or testing it.
- This post will demonstrate some examples of what pipeleaner can currently do.
- Fitting all of these models takes about four lines of code in pipelearner.
- Head to the pipelearner Github page to learn more and contact me if you have a chance to test it yourself or are interested in contributing (my contact details are at the end of this post).
- For updates of recent blog posts, follow @drsimonj on Twitter, or email me at firstname.lastname@example.org to get in touch.
@drsimonj here to introduce pipelearner – a package I’m developing to make it easy to create machine learning pipelines in R – and to spread the word in the hope that some readers may be interested in contributing or testing it. This post will… | blogR | R tips and tricks from a scientist. All R Markdown docs with full R code can be found at my GitHub: https://github.com/drsimonj/blogR
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- They’ve developed an evolution strategy (no, it doesn’t relate much to biological evolution) that promises more powerful AI systems.
- Rather than use standard reinforcement training, they create a “black box” where they forget that the environment and neural networks are even involved.
- The technique eliminates a lot of the traditional cruft in training neural networks, making the code both easier to implement and roughly two to three times faster.
- In tests, a large supercomputer with 1,440 cores could train a humanoid to walk in 10 minutes versus 10 hours for a typical setup, and even a “lowly” 720-core system could do in 1 hour what a 32-core system would take a full day to accomplish.
- However, the practical implications are clear: neural network operators could spend more time actually using their systems instead of training them.
OpenAI researchers have developed an evolution strategy that promises more powerful AI systems. Rather than use standard reinforcement training, they create a
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- People figured that if they could find a way to codify instructions to a machine to tell it what steps to take, any manual operation could be eliminated saving any business time and money.
- Algorithms, on the other hand, are a series of steps that describe a way of solving a problem that meets the criteria of both being correct and ability to be terminated if need be.
- Instead of writing code to search our data given a set of parameters of the certain pattern as traditional coding focuses on, with big data we look for the pattern that matches the data.
- Now another step’s been added to the equation that finds patterns humans don’t see, such as the certain wavelength of light, or data over a certain volume.
- So, this new algorithmic step now successfully searches for patterns and will also create the code needed to do it.
We are all now in what’s called the “big data era,” and we’ve been here for quite some time. Once upon a time we were only just starting to piece together
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- The course is fun and exciting, but at the same time we dive deep into Machine Learning.
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 10 – Model Selection & Boosting: k-fold Cross Validation, XGBoost
- We will walk you step-by-step into the World of Machine Learning.
- The course is packed with practical exercises which are based on live examples.
Coupon 100 10 15 75 Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
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- The repository provides code, exercises and solutions for popular Reinforcement Learning algorithms.
- All code is written in Python 3 and uses RL environments from OpenAI Gym .
- Exercises and Solutions to accompany Sutton’s Book and David Silver’s course.
- Latest commit f117e5d Nov 27, 2016 dennybritz committed on GitHub Merge pull request #36 from alvarosg/bug-epsilons-total-t
- In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings.
reinforcement-learning – Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton’s Book and David Silver’s course.
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- Google is letting users peek into some of its most experimental artificial intelligence projects.
- Play a few notes on a computer-connected keyboard and the algorithm plays a few notes of its own based on what you played.
- Google is also posting the code for all the projects on A.I. Experiments on Github so developers can tinker around with tools themselves and is taking submissions from developers who have used Google’s tech to make similar applications.
- Another experiment, called A.I. Duet, shows how artificial intelligence can be applied to music.
- The site offers hands-on demos that allow you to interact with projects created by Google researchers that show off their AI technology.
Take a peek into Google’s experimental artificial intelligence research.
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- All algorithms are implemented in Python, using numpy, scipy and autograd.
- The project is targeting people who wants to learn internals of ml algorithms or implement them from scratch.
- Failed to load latest commit information.
- We recommend upgrading to the latest Internet Explorer , Google Chrome , or Firefox .
MLAlgorithms – Minimal and clean examples of machine learning algorithms
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- So we want to train a neural net to write some Python code.
- We can now write our code to train a LSTM network on Python code.
- Teaching an AI to write Python code with Python code
- The network takes a few hours to train.
- You will be able to write code directly in your browser and have it run on your instance.
OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.
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- I’ve reproduced Sharon’s code and charts below.
- ) I also modified the data download code to make it work more easily on Windows.
- Sharon Machlis is a journalist with Computerworld, and to show other journalists how great R is for data visualization she shows them these five data visualizations , each of which can be created in 5 lines of R code or less.
- As a final step before posting your comment, enter the letters and numbers you see in the image below.
- I did make a couple of tweaks to the code, though.
Sharon Machlis is a journalist with Computerworld, and to show other journalists how great R is for data visualization she shows them these five data visualizations, each of which can be created in 5 lines of R code or less. I’ve reproduced Sharon’s code and charts below. I did make a couple of tweaks to the code, though. I added a call to checkpoint(“2016-08-22”) which, if you’ve saved the code to a file, will install all the necessary packages for you. (I also verified that the code runs with package versions as of today’s date, and if you’re trying out…
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