Samuel completed his first computer program to play that game within a few years. That information would become knowledge - and lead the computer to make smarter moves. Along the way, the computer turned bits of data into information. Then, it used those moves and strategies to play better the next time. During each game, the computer tracked which of its moves and strategies had worked best. He also taught the computer to play checkers against itself. Rather than program every possible move, he gave the computer advice from champion checkers players. This computer scientist would teach computers how to learn on their own. In the 1940s, while working as an engineer at the University of Illinois, Arthur Samuel decided to program computers differently. Then they described how they wanted the computer to respond to every decision it might be asked to make along the way. Those programmers had to consider every step a computer would or could encounter. The Children’s Museum of Indianapolis/Wikimedia ( CC BY-SA 3.0)įor computers to solve problems, people used to just write step-by-step instructions for the programs that operate a computer’s hardware. For example, you set too large learning rate or not enough neurons in second hidden-layer and your NN training will be stuck in a local minimum.The early developers of machine learning started with the task of teaching a computer how to win a game of checkers. You know what is funny? That each of the NN hyperparameters mentioned above can be critical. Not enough hyperparameters? You need to set a batch size as well (batch: how many samples to show for each weights update). Let's go with simple SGD: You need to set learning rate, momentum, decay. You can start with simple Stochastic Gradient Descent (SGD), but there are many others. Then you need to choose a training algorithm. How many neurons to use in each layer? What activation functions to use? What weights initialization to use?Īrchitecture ready. How many layers to use usually 2 or 3 layers should be enough. You need some magic skills to train NN well. For NN you have more steps for preprocessing, so more steps to implement in the production system as well.įor RF, you set the number of trees in the ensemble (which is quite easy because of the more trees in RF the better) and you can use default hyperparameters and it should work. Keep in mind that all preprocessing that is used for preparing training data should be used in production. Scale features into the same (or at least similar) range.Convert categorical data into numerical.To conclude, for NN training, you need to do the following preprocessing: What is more, the gradients values can explode and the neurons can saturate, which will make it impossible to train NN. If you don't scale features into the same ranges then features with larger values will be treated as more important in the training, which is not desired. In the case of different ranges of features, there will be problems with model training. What is more, there is a need for feature scaling. Categorical data has been converted into numericalĭata preprocessing for NN requires filling missing values and converting categorical data into numerical.To prepare data for RF (in Python and Sklearn package), you need to make sure that: However, the Sklearn implementation doesn't handle this. In theory, the RF should work with missing and categorical data. However, I would prefer RF over NN because they are easier to use. In the case of tabular data, you should check both algorithms and select the better one. OK, so now you have some intuition that, when you deal with images, audio, or text data, you should select NN. In theory, RF can work with such data as well, but, in real-life applications, after such preprocessing, data will become sparse and RF will be stuck. Text data-can be handled by NN after preprocessing, for example with bag-of-words.On the other hand, NN can work with many different data types: (What is tabular data? It is data in a table format). The last layer of neurons is making decisions. Usually, they are grouped in layers and process data in each layer and pass forward to the next layers. The neurons cannot operate without other neurons they are connected. The NN is a network of connected neurons. Decision trees in the ensemble are independent. Each decision tree, in the ensemble, processes the sample and predicts the output label (in case of classification). The RF is the ensemble of decision trees. I will try to show you when it is good to use Random Forest and when to use Neural Network.įirst of all, Random Forest (RF) and Neural Network (NN) are different types of algorithms. Which is better, Random Forest or Neural Network? This is a common question, with a very easy answer: It depends.
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