Book Image

Machine Learning Solutions

Book Image

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Index

The best approach


We have covered the entire concept that can help us implement the DMN-based chatbot. In order to implement this approach, we will be using Keras with the TensorFlow backend. Without wasting any time, we will jump to the implementation section. You can refer to the code for this approach using this GitHub link: https://github.com/jalajthanaki/Chatbot_based_on_bAbI_dataset_using_Keras.

Implementing the best approach

Here, we will train our model on the given bAbI task 1 dataset. First of all, we need to parse the stories and build the vocabulary. You can refer to the code in the following figure:

Figure 8.34: Code snippet for parsing stories and build vocabulary

We can initialize our model and set its loss function as a categorical cross-entropy with stochastic gradient descent implementation using RMSprop in Keras. You can refer to the following screenshot:

Figure 8.35: Code snippet for building the model

Before training, we need to set a hyperparameter. With the help of the value...