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In the web dev world, it is a common trend to use a programming language that optimizes for speed and memory management. However, there are certain benefits that can come from using slower languages such as C++ and Java. In this article, we will take a look at these benefits and how you can use them in your web development workflows. In order to demonstrate the functionality of a slow language in an environment where time is usually not an issue, let's set up our application on Python 3.6 with virtualenv and try to develop our application with C++ instead of Python 3.6's default language of python 2.7x . We will use Fastai that has built-in support for C++ dev by avoiding the need of having to compile code, write lengthy Makefiles , etc. Also, if you are interested in more Python related articles, check out this awesome list of awesome lists. Suppose we want to build a chatbot , which is somewhat similar to ELIZA (the first chatterbot ). The ELIZA bot was written in LISP and relied on pattern matching , substitution and rule-based inference . On the other hand, our chatbot will rely on deep learning with TensorFlow and an LSTM network . Let's get started with the code. The chatbot is set up to post back responses that are not too complicated, so that we can test out our code easily. We will be posting simple sentences to our bot and see if it replies back with an appropriate response . This will require us to utilize several packages, including numpy , tensorflow , fastai , requests , scikit-learn , matplotlib . Note: All code used in this article is available on GitHub for you to try out yourself. Here's the files we need to get started: 1. chatbot_data.py 2. model.py 3. dataset_creator.py 4. setup.py # -*- coding: utf-8 -*- # Chatbot data import warnings from warnings import ignore from collections import defaultdict from datetime import datetime def build_dataset (): """Returns a list of sentences that we will use for training.""" dataset = defaultdict () for sentence in open ( "../train/sentences/" ): line = sentence . rstrip () . split ( " " ) if len ( line ) == 0 : continue if not hasattr ( sentence , 'split' ): continue # Split and remove whitespace and punctuation if isinstance ( line , str ): words = re . split ( '[ \\s,;:?!' ]+' ) + line . split () # Remove the punctuation and whitespace separated words words = [ word for word in words if word and not re . cfa1e77820
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