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Today, 1th of June Google brain team committed new code in public.
There are some interesting points:
1) High level APIs will be presented as a separate SwiftPM package under github.com/tensorflow.

High level APIs were added earlier purely to explore the programming model, not to be usable by anyone. Having high level APIs be part of the stdlib module conveys a wrong message for beta testers, and it has been confusing ever since our open source release.

2) Supporting Python code is one of priority:

  • Improved Python diagnostics related to member access.
  • Improved Python C API functions for binary arithmetic operations.

3) Improved cross-device sends and receives support.

4) Lots of work done around supporting generic @dynamicCallable methods.

5) Deprecated a.dot(b) and to matmul(a, b).


Online demo of t-SNE visualization you can see here.
Machine learning algorithms have been put to good use in various areas for several years already. Analysis of various political events can become one of such areas. For instance, it can be used for predicting voting results, developing mechanisms for clustering the decisions made, analysis of political actors’ actions. In this article, I will try to describe the result of a research in this area.

Problem Definition  

Modern machine learning capabilities allow converting and visualizing huge amounts of data. Thereby it became possible to analyze political parties’ activities by converting voting instances that took place during 4 years into a self-organizing space of points that reflects actions of each elected official.

Each politician expressed themselves via 12 000 voting instances. Each voting instance can represent one of five possible actions (the person was absent, skipped the voting, voted approval, voted in negative, abstained).

The task is to convert the results of all voting instances into a point in the 3D Euclidean space that will reflect some considered attitude.

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Before reading this article, I recommend to have a look at my previous article on TensorFlowKit and like my repository.

When I started working in the field of machine learning, it was quite difficult to move to vectors and spaces from objects and their behavior. At first it was rather complicated to wrap my head around all that, and most processes did not seem obvious and clear at once. That’s the reason why I did my best to visualize everything I did in my groundwork:  I used to create 3D models, graphs, diagrams, figures, etc.

When speaking about efficient development of machine learning systems, usually such problems as learning speed control, learning process analysis, gathering various learning metrics, and others are mentioned. The major difficulty is that we (people) use 2D and 3D spaces to describe various processes that take place around us. However, processes within neural networks lay in multidimensional spaces, and that makes them rather difficult to understand. Engineers all around the world understand this problem and try to develop various approaches to the visualization or conversion of multidimensional data into simpler and more understandable forms.

There are separate communities dedicated to solving such problems, for example, Distill, Welch Labs, 3Blue1Brown.

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Please, read my previous post about Swift & TensorFlow

MNIST

I took “Hello World!”  in the universe of neural networks as an example, a task for systematization of MNIST images. MNIST dataset includes thousands of images of handwritten numbers, the size of each image is 28×28 pixels. So, we have ten classes that are neatly divided into 60 000 images for educating and 10 000 images for testing. Our task is to create a neural network that is able to classify an image and determine the class it belongs to (out of 10 classes).

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Kraken as height level API for TensorFlow.

Since today Kraken is high – level API and brain system for the most powerful deep – learning framework TensorFlow.

TensorFlow is the fastest growing solution for neural networks. Written on C++ language it shows huge performance on CPU and GPU hardware. Kraken could help us to build deep learning architecture at real time and test them in different ways and on different servers.

Using TensorFlow library as core of our Neural Network you can get lot’s of benefits as:

  • Many dimansion Pooling layer;
  • Many dimansion Normalization layer;
  • Many dimansion Convolution layer;
  • Densely-connected layer;
  • Many dimansion Pooling layer;
  • RNN and LSTM solutions;
  • Optimizer;

From today our tool is incredible powerful and strong.