magenta/README.md at master · tensorflow/magenta · GitHub

open sourced sketch-rnn.

  • For small to medium sized datasets, dropout and data augmentation is very useful technique to avoid overfitting.
  • This type of data augmentation is very powerful when used on small datasets, and is unique to vector drawings, since it is difficult to dropout random characters or notes in text or midi data, and also not possible to dropout random pixels without causing large visual differences in pixel image data.
  • If there is virtually no difference for a human audience when they compare an augmented example compared to a normal example, we apply both data augmentation techniques regardless of the size of the training dataset.
  • As mentioned before, recurrent dropout and data augmentation should be used when training models on small datasets to avoid overfitting.
  • If you want to create your own dataset, you must create three lists of examples for training/validation/test sets, to avoid overfitting to the training set.

magenta – Magenta: Music and Art Generation with Machine Intelligence
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At I/O, Android Takes Backseat to Machine Learning

At I/O, Android Takes Backseat to Machine Learning

  • Google I/O is ostensibly about the future of Android, but that changed in 2016 when CEO Sundar Pichai put the AI-powered Google Assistant and machine learning at the forefront, a trend that continued at this year’s show.
  • Google unveiled new tools to make interactions like purchases seamless, opening the door for the Assistant to become a money-making platform for developers.
  • It started when the Google Assistant debuted in the less-than-popular chat app Allo, which launched simultaneously on iPhone and Android.
  • Now that developers can write Actions for the Assistant, it effectively turns the iPhone into an extension of Google’s existing platforms.
  • Rather, Google is pushing hard to turn Android developers into Google Assistant developers, Google Home Actions developers and web developers using the latest tools available in Chrome.

As Google puts its machine learning at the forefront, Android is just another platform.
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Google made a site that shows how millions of people draw the same object

Google made a site that shows how millions of people draw the same object

  • Back in November, Google released artificial intelligence experiment that asks you to draw a random object and see if the neural network can identify your doodle.
  • was eventually turned into a tool that transformed drawings into clip art based on the best results it got, helping people add a visual icon to their work without requiring any particular artistic talent.
  • I wasn’t very confident in my frog (croak) so I felt that adding “Ribbit” to the drawing might provide context, even if the AI might not be able to read.
  • Face is a neat one too — I’d guess that depending on the artist, most of these drawings were interpreted as a self portrait.
  • While you can argue that most people see frog in mostly the same way, dragon produced a variety of results — from fire-breathing to horned.

Back in November, Google released artificial intelligence experiment that asks you to draw a random object and see if the neural network can identify your doodle. Quick, Draw! was eventually turned…
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Why Google’s CEO Is Excited About Automating Artificial Intelligence

Google’s CEO is excited about seeing #AI take over some work of his AI experts

  • Sundar Pichai at the company’s annual developer conference in Mountain View, experts are in short supply as companies in many industries rush to take advantage of recent strides in the power of artificial intelligence.
  • At Google’s annual developer conference today, Pichai introduced a project called AutoML coming out of the company’s Google Brain artificial intelligence research group.
  • The company is trying to lure new customers in the corporate cloud computing market, where it lags leader Amazon and second-place Microsoft (see “Google Reveals Powerful New AI Chip and is targeted at making it easier to use a technique called deep learning, which Google and others use to power speech and image recognition, translation, and robotics (see “10 Breakthrough Technologies 2013: Deep Learning”).
  • “We do it by intuition,” says Quoc Le, a machine-learning researcher at Google working on the AutoML project.Last month, Le and fellow researcher Barret Zoph presented results from experiments in which they tasked a machine-learning system with figuring out the best architecture to use to have software learn to solve language and image-recognition tasks.On the image task, their system rivaled the best architectures designed by human experts.
  • But like many ideas in the field of artificial intelligence, the power of deep learning is allowing new progress.

AI software that can help make AI software could accelerate progress on making computers smarter.
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Google brings 45 teraflops tensor flow processors to its compute cloud

Google brings 45 teraflops tensor flow processors to its compute cloud  #ai

  • Google has developed its second-generation tensor processor—four 45-teraflops chips packed onto a 180 TFLOPS tensor processor unit (TPU) module, to be used for machine learning and artificial intelligence—and the company is bringing it to the cloud.
  • The new TPUs are optimized for both workloads, allowing the same chips to be used for both training and making inferences.
  • Quite how floating point performance maps to these integer workloads isn’t clear, and the ability to use the new TPU for training suggests that Google may be using 16-bit floating point instead.
  • But as a couple of points of comparison: AMD’s forthcoming Vega GPU should offer 13 TFLOPS of single precision, 25 TFLOPS of half-precision performance, and the machine-learning accelerators that Nvidia announced recently—the Volta GPU-based Tesla V100—can offer 15 TFLOPS single precision and 120 TFLOPS for “deep learning” workloads.
  • Microsoft has been using FPGAs for similar workloads, though, again, a performance comparison is tricky; the company has performed demonstrations of more than 1 exa-operations per second (that is, 1018 operations), though it didn’t disclose how many chips that used or the nature of each operation.

Up to 256 chips can be joined together for 11.5 petaflops of machine-learning power.
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Google is giving a cluster of 1,000 Cloud TPUs to researchers for free

Google is giving a cluster of 1,000 Cloud TPUs to researchers for free

  • At the end of Google I/O, the company unveiled a new program to give researchers access to the company’s most advanced machine learning technologies for free.
  • The TensorFlow Research Cloud program, as it will be called, will be application based and open to anyone conducting research, rather than just members of academia.
  • If accepted, researchers will get access to a cluster of 1,000 Cloud TPUs for training and inference.
  • If that level of openness isn’t your cup of tea, Google is also planning to launch a Cloud TPU Alpha program for internal, private sector, work.
  • The application for the program isn’t open yet, but Google is directing interested parties to fill out a form indicating interest.

At the end of Google I/O, the company unveiled a new program to give researchers access to the company’s most advanced machine learning technologies for..
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Chapter 5: Random Forest Classifier – Machine Learning 101 – Medium

#MachineLearning 101 - Random Forest Classifier #ensemble #algorithm

  • In the code example concerned we perform following steps:To understand in detail, once again please refer to chapter 1 coding part here.Build dictionary of words from email documents from training set.Consider the most common 3000 words.For each document in training set, create a frequency matrix for these words in dictionary and corresponding labels.
  • The code snippet below does this:def make_Dictionary(root_dir): all_words = [] emails = [os.path.join(root_dir,f) for f in os.listdir(root_dir)] for mail in emails: with open(mail) as m: for line in m: words = line.split() all_words += words dictionary = Counter(all_words)# if you have python version 3.
  • if item.isalpha() == False: del dictionary[item] elif len(item) == 1: del dictionary[item] # consider only most 3000 common words in dictionary.dictionary = dictionarydef extract_features(mail_dir): files = [os.path.join(mail_dir,fi) for fi in os.listdir(mail_dir)] features_matrix = np.zeros((len(files),3000)) train_labels = np.zeros(len(files)) count = 0; docID = 0; for fil in files: with open(fil) as fi: for i,line in enumerate(fi): if i == 2: words = line.split() for word in words: wordID = 0 for i,d in enumerate(dictionary): if d[0] == word: wordID = i features_matrix[docID,wordID] = words.count(word) train_labels[docID] = 0; filepathTokens = fil.split(‘/’) lastToken = – 1] if lastToken.startswith(“spmsg”): train_labels[docID] = 1; count = count + 1 docID = docID + 1 return features_matrix, train_labels2.
  • Using Random Forest ClassifierThe code for using Random Forest Classifier is similar to previous classifiers.Import libraryCreate modelTrainPredictfrom sklearn.ensemble import = “.
  • /test-mails”dictionary = “reading and processing emails from file.

Random Forest Classifier is ensemble algorithm. In next one or two posts we shall explore such algorithms. Ensembled algorithms are those which combines more than one algorithms of same or different…
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