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      "body": "#### Input data\n\nThe neural network need some data to learn on. Input data is very important part of machine learning. If you have a huge amount of data, you can achieve great results even if an architecture of your network is not good. That’s why companies like Google are trying to gain all information they can get from their users (of course not because they have bad architectures but because really big data is precious).\n\nSo we need to generate some data. You can sit and play as many games as you can, but it is always good when you can generate data automatically (from scratch or modifying data that you have). In our case it is easy to create data just randomly choosing direction and observing if the snake is still alive after the turn.\n\nAfter 100 games I’ve got 5504 training examples. It is enough for training to survive\n\n#### Architecture of neural network\n\nChoosing the right architecture or your neural network is always hard. You can choose number of neurons in layers, number of layers and types of neurons. It always depends on task that you trying to solve. It’s better to try different variations and choose the one that fits more than others.\n\nOur task is very simple therefore we will use only input and output layers. No hidden layers are needed.\n\n![](https://cdn-images-1.medium.com/max/800/1*vpcmHGcYrRu-mSufZI39NA.png)\n\nIn TensorFlow it will look like(I’m using [TFLearn](http://tflearn.org/)):\n\nnetwork = input_data(shape=\\[None, 4, 1\\], name='input')  \nnetwork = fully_connected(network, 1, activation='linear')  \nnetwork = regression(network, optimizer='adam', learning\\_rate=1e-2, loss='mean\\_square', name='target')  \nmodel = tflearn.DNN(network)\n\nYou can find the full code [here](https://github.com/korolvs/snake_nn/blob/master/nn_1.py)\n\n#### Results\n\nEach turn we give to the network three arrays with possible actions and choose one with better output. After training the snake chose the easiest way to survive:\n\n![](https://cdn-images-1.medium.com/max/800/1*WN6N9bxZWGA8SHBVfCV5gA.gif)\n\nwebsite: https://towardsdatascience.com/today-im-going-to-talk-about-a-small-practical-example-of-using-neural-networks-training-one-to-6b2cbd6efdb3",
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      "body": "Hi! I am a robot. I just upvoted you! I found similar content that readers might be interested in:\nhttps://towardsdatascience.com/today-im-going-to-talk-about-a-small-practical-example-of-using-neural-networks-training-one-to-6b2cbd6efdb3",
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      "body": "something about machine learning, neural networks, and TensorFlow but there is no problem otherwise.\n\nAnd finally, obviously, there are better approaches to write a logic for a snake game but let’s pretend that it is a real task and we need to solve it this way.\n\n### Part 0: The game\n\nFirstly we need to write a game itself. It will have a 20x20 field, a snake of 3 pieces at the start, one randomly generated apple at each moment in time and API to use with our network. You can find a code of the game [here](https://github.com/korolvs/snake_nn/blob/master/snake_game.py).\n\n![](https://cdn-images-1.medium.com/max/800/1*2VKWGN2p1NyGY7i0KedvOg.gif)\n\nNow let’s start with a neural network.\n\n### Part 1: Survive\n\n#### Features\n\nTo make the snake “smart” we need to give some knowledge to it — we need to create **features **to teach it. Always try to choose features which will be most useful. If you add not enough features, a network will not get enough information to be good. From the other side, if there are too many features. it will be hard for a network to decide which are more important and learning will be longer.\n\nAt the first step, we will learn the snake how to survive and will not think about apples. To choose a right direction it should know if there are any obstacles around it. Considering these obstacles and suggested direction the network will decide is it a good action or not.\n\nSo on the input of our neural network we will give an array of 4 numbers:\n\n-   Is there an obstacle to the left of the snake (1 — yes, 0 — no)\n-   Is there an obstacle in front of the snake (1 — yes, 0 — no)\n-   Is there an obstacle to the right of the snake (1 — yes, 0 — no)\n-   Suggested direction (-1 — left, 0 — forward, 1 — right)\n\nAnd as the output we want to receive a decision. 1 — we should go in the selected direction, 0 — we should choose another one.\nTo be continued.\nUPVOTE PLS!\nwebsite: https://towardsdatascience.com/",
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      "body": "Hi! I am a robot. I just upvoted you! I found similar content that readers might be interested in:\nhttp://news.psu.edu/story/506997/2018/02/28/research/hail-technology-deep-learning-may-help-predict-when-people-need",
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      "body": "Computers may better predict taxi and ride sharing service demand, paving the way toward smarter, safer and more sustainable cities, according to an international team of researchers.\nIn a study, the researchers used two types of neural networks -- computational systems modeled on the human brain -- that analyzed patterns of taxi demand. This deep learning approach, which lets computers learn on their own, was then able to predict the demand patterns significantly better than current technology.\n\n\"Ride sharing companies, like Uber in the United States, and Didi Chuxing in China, are becoming more and more popular and have really changed the way people approach transportation,\" said Jessie Li, associate professor of information sciences and technology, Penn State. \"And you can imagine how important it would be to predict the taxi demand because the taxi company could dispatch the cars even before the need arises.\"\n\nBetter predictions could lessen the time taxis idle waiting for rides, making cities cleaner, the researchers added. Because accidents tend to happen more often in congested areas, better ride prediction technology could also improve safety.\n\nThe researchers analyzed a large dataset of ride requests to Didi Chuxing, one of the largest car-hailing companies in China, according to Huaxiu Yao, doctoral student in information sciences and technology and lead author of the paper.\n\nWhen users need a ride they first make a request through a computer application -- for example, a mobile phone app. Using these requests for rides, rather than relying solely on ride data, better reflect overall demand, according to the researchers.\n\n\"This is really good data because it's based on demand,\" said Yao. \"If you just know how many people took a ride, that doesn't really tell you the demand because it could be that people didn't get a ride, or others just gave up trying.\"\n\nWith the historical data, which includes the time and location of the request, the computer can then predict how the demand will change over time. When visualized on the map, the researchers could see that evolving demand.\n\n\"In the morning, for example, you can see that in a residential section there are more pickups, and there are more drop-offs in the downtown area,\" said Li. \"In the evening, it's reversed. What we are doing is using historical pickup data to predict how this map changes 30 minutes from now, one hour from now, and so on.\"\n\nThe researchers, who presented their findings at the recent AAAI Conference on Artificial Intelligence, one of the biggest conferences in the AI research field, used data on taxi requests in Guangzhou, China, from Feb. 1 to March 26, 2017. Guangzhou residents make about 300,000 ride requests each day. By comparison, there are about 500,000 rides per day in New York City.\n\nWhile technology uses one type of neural network, the researchers combined two neural networks -- the convolutional neural network, or CNN, and Long Short Term Memory network, or LSTM -- to help guide the complex sequences of predictions. CNNs can better model complex spatial correlations and LSTMs can better handle sequential modeling.\n\n\"Basically, we used a very complicated neural net to simulate how people digest information, in this case, the image of the traffic patterns,\" said Li.\n\nLi said access to larger data sets -- Big Data -- and advances in computer technology that can process this large amount of data have helped this project and enabled other deep learning developments.\n\n\"In traditional computer programming, people need to tell the computer what aspects -- or features -- it needs to look at and then they have to model it, which takes a huge effort,\" said Li. \"Why deep learning is revolutionary is now we can skip that step. You can just give the computer the images, for example. You don't need to tell the computer what it needs to look at.\"\n\nUPVOTING PLS!!!\nwebsite: https://www.sciencedaily.com/releases/2018/03/180301103621.htm",
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