GitHub

  • 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 lyng_95@zju.edu.cn).

deep-photo-styletransfer-tf – Tensorflow (Python API) implementation of Deep Photo Style Transfer
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Data Science As A Career Change – My Story as a Video Interview

MT @_data_mania: Video interview~
#DataScience as #career change
#WomenInTech #AI

  • If you’re like most people who work with data on a regular basis, you’re probably hearing about data science as a career change option and wondering “Is data science right for me?
  • Although I can’t answer those lingering questions for you – I can tell you my experience, as a person who approached data science as a career change.
  • In this exclusive premier interview for LinkedIn Learning, I discuss how I transitioned myself from an Environmental Engineer to a Data Scientist.
  • There’s a lot covered in this lively 30-minute session; And if you’re considering data science as a career change, watching it should help you get a better idea what to expect, and hopefully a little inspiration to ignite your passion.
  • If you liked this video and want to learn more about how to make the transition into data science as a career change, then be sure to check out my LinkedIn Learning / Lyndas training courses here.

If you’re like most people who work with data on a regular basis, you’re probably hearing about data science as a career change option and wondering…
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Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data @kojouharov

  • To make things more interesting and give context, I added descriptions and/or excerpts for each major topic.This is the most complete list and the Big-O is at the very end, enjoy…If you like this list, you can let me know here.Neural NetworksNeural Networks Cheat SheetNeural Networks GraphsNeural Networks Graphs Cheat SheetNeural Network Cheat SheetMachine Learning OverviewMachine Learning Cheat SheetMachine Learning: Scikit-learn algorithmThis machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part.
  • The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.Machine Learning Cheat SheetMACHINE LEARNING : ALGORITHM CHEAT SHEETThis machine learning cheat sheet from Microsoft Azure will help you choose the appropriate machine learning algorithms for your predictive analytics solution.
  • First, the cheat sheet will asks you about the data nature and then suggests the best algorithm for the job.MACHINE LEARNING ALGORITHM CHEAT SHEET If you like this list, you can let me know here.
  • Data Wrangling Cheat SheetPandas Data Wrangling Cheat SheetData Wrangling with dplyr and tidyrData Wrangling with dplyr and tidyr Cheat SheetData Wrangling with dplyr and tidyr Cheat SheetScipySciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries.
  • Data VisualizationData Visualization Cheat Sheetggplot cheat sheetPySparkBig-OBig-O Algorithm Cheat SheetBig-O Algorithm Complexity ChartBIG-O Algorithm Data Structure OperationsBig-O Array Sorting AlgorithmsAbout StefanStefan is the founder of Chatbot’s Life, a Chatbot media and consulting firm.

Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and…
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/home/kv/Backpropagation

  • The actual function \(f: \mathbb{R}^{N} \rightarrow \mathbb{R}^{N}\) is as follows:

    Computing softmax activation is as follows:

    This occurs due to the overflow which is encountered in .

  • This is done by subtracting element wise the max element from the input vector \(x\).
  • called the “logits” and computes the softmax activations which are:

    We need to now compute the Jacobian matrix for the \(i\)th output w.r.t \(j\)th input:

    For \(i = j\), we have:

    And for the case \(i

    eq j\), we have:

    Let there be \(l\) layers in the network.

  • Comparing these values to the corresponding ground truth values , which is a one hot vector we can define the cross entropy loss function as follows.
  • Considering just one training example, the loss \(L\) is given by:

    \(y\) is one hot vector representing the ground truth, \(f\) is the corresponding predictions which are obtained after applying softmax activation.

Inspired from training Deep Neural Networks up till now, I wanted to write about
the working of backpropagation and the flow of gradients during backpropagation.

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50 Companies Leading the Artificial Intelligence Revolution

Here are 50 companies leading the AI revolution

  • We know that artificial intelligence will soon reshape our world.
  • But which companies will lead the way?
  • To help ­answer that question, research firm CB Insights recently selected the “AI 100,” a list of the 100 most promising artificial intelligence startups ­globally.
  • The private companies were chosen (from a pool of over 1,650 candidates) by CB Insights’ Mosaic algorithm, based on factors like financing history, investor quality, business category, and momentum.
  • A look at the 50 largest startups on the list, ranked by total funds raised, shows that investment in AI is surging worldwide.

A look at the most promising global startups working with artificial intelligence.
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China’s Dissident Chatbots

#China’s dissident #chatbots
#ai #tencent
by @WSJOpinion

  • Beijing’s system of internet censorship relies on tens of thousands of workers to remove comments critical of the Communist Party.
  • A couple of artificial-intelligence programs run by a Chinese internet company suggest resentment of the country’s rulers is running high.
  • The programs were designed to learn how to make conversation by…

Tencent provides a glimpse of what Chinese think of their leaders.
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Artificial intelligence proves that craft beer names are total nonsense

Artificial intelligence proves that craft beer names are total nonsense

  • Artificial intelligence proves that craft beer names are total nonsense

    Let’s call it “Shock State.

  • “Image: NEARMY/EPA/REX/Shutterstock
    By Stan Schroeder2017-08-04 09:11:11 UTC

    If you’re a craft beer connoisseur — or even just an occasional drinker — you’ve likely noticed that names for new brews are getting out of hand.

  • Likely in order to distance themselves from traditional, European beer names such as Franziskaner Royal, the new breed of craft beer makers are going with increasingly odd names such as Stone Ruination IPA.
  • So what happens when you employ a neural network to create new craft beer names?
  • Researcher Janelle Shane did it by feeding a neural network a bunch of beer names from BeerAdvocate’s database — the project was initiated by Gizmodo’s Ryan Mandelbaum — and the AI came up with a bunch of names which mostly sound very odd, just like real craft beer names.

An AI came up with beer names and the result was pretty much the same as when humans do it.
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