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  • Book Overview & Buying Python Deep Learning Projects
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Python Deep Learning Projects

Python Deep Learning Projects

By : Lamons, Kumar, Nagaraja
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Python Deep Learning Projects

Python Deep Learning Projects

3 (4)
By: Lamons, Kumar, Nagaraja

Overview of this book

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way
Table of Contents (17 chapters)
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8
Handwritten Digits Classification Using ConvNets

Serving chatbots

Up to now, we have seen how to build chatbots using the two methods of TF-IDF and Rasa NLU. Let's expose both of them as APIs. The architecture of this simple chatbot framework will look like this:

This chatbot pipeline illustrates that we can have any User Interface (Slack, Skype, and so on) integrated with the chatbot_api which we exposed . And under the hood we can setup any number of algorithms 'TFIDF' and 'RASA'

Refer to the Packt repository for this chapter (available at https://github.com/PacktPublishing/Python-Deep-Learning-Projects/tree/master/Chapter04) for the API code and look into the chatbot_api.py file. Here, we have implemented a common API that can load both versions of bot, and you can now build a whole framework on top of this.

To execute the serving of the APIs, follow these steps:

  1. Enter the chapter directory using...
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