Book Image

Intelligent Projects Using Python

By : Santanu Pattanayak
Book Image

Intelligent Projects Using Python

By: Santanu Pattanayak

Overview of this book

This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.
Table of Contents (12 chapters)

Inference using the trained RBM

Inference for the RBM is pretty straightforward given that we have already generated the file pred_all_recs.csv with all the predictions during training. All we need to do is just extract the test records from the pred_all_recs.csv based on the provided test file. Also, we resort to the original userid and movieid by adding 1 to their current values. The purpose of going back to the original ID is to be able to add the user and movie information from the u.user and u.item files.

The inference block is as follows:

    def inference(self):

self.df_result = self.test_df.merge(self.train_df,on=['userid','movieid'])
# in order to get the original ids we just need to add 1
self.df_result['userid'] = self.df_result['userid'] + 1
self.df_result['movieid'] = self.df_result[&apos...