The general approach to building deep learning algorithms is gonna be first knowing what data you have and in which format. Then figuring out what your goal is with the data. This step is what we call the** Input and Output Step**. At this stage, we should also partition the input examples into Training Set and Testing Test.
At this 30’000 feet above view, we know in general terms if we have a classification or regression problem. With this information we figure out how to get our input data transformed into a viable input to the neural network. This stage, we should call the Encode and Decode Step, also in the real world called Preprocessing Step.
We know the rough architecture of our neural network. But in the Architecture Step we will commit to the layer architecture specifically, based on how complicated the input data and how complicated the task is, you will use more or less layers.
The next step is the Components Step, when you will choose the loss function, the metrics and the optimizer. The metrics are what you will use to regularize.
And finally the most important step of training. In the Training Step, we will choose a rough number of epochs to iterate. As well as the batch size. This step may also be called the Fitting Step, because you are fitting the model to the data.
In the last step, Evaluation Step, you evaluate all the metrics and stop the fitting process right before it over-fits. The model will freeze the weights at this stage and theoretically it is optimally positioned to give high accuracy on unseen data.
These steps are quite simple, but if it seems still a bit abstract. The following is a simple example to solidify you understanding.
We will be using the IMDB movie data set to do binary classification. The data set contains 50’000 movie reviews that are pretty polar. There are no movie reviews that neutral or hard to guess. Some are very positive review and the other are very negative. The entire set is divided equally into the 2 types, 25’000 positive reviews and 25’000 negative.
Now we partition the data into the 2 sets: Training Set and Testing Set.
The Training Set consists of 50% positive reviews and 50% negative reviews, totaling 25’000. While the Testing Set consists of 50% positive reviews and 50% negative reviews, totaling 25’000, exactly mirroring the other set.
So let’s import our data set from
keras.datasets so that we can put it into training (examples + labels) and testing (examples + labels) while taking only the top 10’000 most frequently occurring words in the entire review data set.
The stack for this project is Jupyter Notebook, Keras, Tensorflow Backend.
This is a simple natural language processing project. Given a bunch of text, we are trying to have our computer understand whether its a positive review or negative review. But our model doesn’t understand the meanings of the word well in “well directed” or bad in “bad acting”. It simply learns to associate the words with labels. As such we don’t give the words as strings to the model, we encode it as vector that can be manipulated with linear transformations. This kind of encoding is called one-hot encoding. We find the highest frequency words in the entire set and rank it by the frequency, and take only the top x number of words, in this case 10’000.
Generally we encode a word as [0 0 0 1 …. 0 0], this may be the word “Bad”. While the word “good” might be another unique one-hot configuration of let’s say [0 1 0 0 … 0 0]. We generally wanna keep the size of this vector small, because our computers have processing limitations and we don’t want it to take too long to train.
I recommend you set up a TensorFlow Jupyter docker container on your machine for your projects. Another option if you want to prototype quickly is to use Google Colaboratory. Its a ready to use, no config notebook that runs on a Compute Engine with a very generous RAM and Storage.
The next thing to encode of course are the labels. Our deep learning model isn’t gonna magically produce numbers into strings like “positive review” or “negative review”. It’s encoded into 1 for positive and 0 for negative as well.
There’s an array of dictionary items in the IMDB data set, that maps the actual strings to the one-hot vectors. You can use that to decode the vectors back into English.
Based on the task and the data we now know that we will be classifying text into 2 groups, a classification problem. We roughly know that this means our input vectors will go into our neural network and come out either a 1 or 0.
A typical review looks something like this, in **text **form:
? this film was just brilliant casting location scenery story direction everyone’s really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy’s that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don’t you think the whole story was so lovely because it was true and was someone’s life after all that was shared with us all
#programming #artificial-intelligence #deep-learning #movies #deep learning
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Searching for an element’s presence in a list is usually done using linear search and binary search. Linear search is time-consuming and memory expensive but is the simplest way to search for an element. On the other hand, Binary search is effective mainly due to the reduction of list dimension with each recursive function call or iteration. A practical implementation of binary search is autocompletion.
The objective of this project is to create a simple python program to implement binary search. It can be implemented in two ways: recursive (function calls) and iterative.
The project uses loops and functions to implement the search function. Hence good knowledge of python loops and function calls is sufficient to understand the code flow.
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In my previous article (Model Selection in Text Classification), I presented a way to select a model by making a comparison between classical machine learning and deep learning for the binary text classification problem.
The notebook is structured to run automatically with cross-validation all the algorithms and shows the results for the different metrics leaving the user free to select the algorithm according to his needs.
#tutorial #metrics #binary #multiclass #classification #testing
Recently I did a project wherein the target was multi-class. It was a simple prediction task and the dataset involved both categorical as well as numerical features.
For those of you who are wondering what multi-class classification is: If you want to answer in ‘0 vs 1’, ‘clicked vs not-clicked’ or ‘cat vs dog’, your classification problem is binary; if you want to answer in ‘red vs green vs blue vs yellow’ or ‘sedan vs hatch vs SUV’, then the problem is multi-class.
Therefore, I was researching suitable ways to encode the categorical features. No points for guessing, I was taken to medium articles enumerating benefits of mean target encoding and how it outperforms other methods and how you can use category_encoders library to do the task in just 2 lines of code. However, to my surprise, I found that no article demonstrated this on multi-class target. I went to the documentation of category_encoders and found that it does not say anything about supporting multi-class targets. I dug deeper, scouring through the source code and realized that the library only works for binary or continuous targets.
So I thought: “Inside of every problem lies an opportunity.” — Robert Kiposaki
Going deep, I went straight for the original paper by _Daniele Micci-Barreca _that introduced mean target encoding. Not only for regression problem, the paper gives the solution for both binary classification as well as multi-class classification. This is the same paper that category_encoders cites for target encoding as well.
While there are several articles explaining target encoding for regression and binary classification problems, my aim is to implement target encoding for multi-class variables. However, before that, we need to understand how it’s done for binary targets. In this article, I cover an overview of the paper that introduced target encoding, and show by example how target encoding works for binary problems.
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Binary Classification is a type of classification model that have two label of classes. For example an email spam detection model contains two label of classes as spam or not spam.
Most of the times the tasks of binary classification includes one label in a normal state, and another label in an abnormal state.
#machine-learning #python #deep-learning #binary-classification #data-science #deep learning