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Deep Learning Models explored with help of Python Programming

Preface

This is the second book in the Deep Learning models series by the author. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are s Read More

This is the second book in the Deep Learning models series by the author. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models.
The book starts with the Introduction part which is adopted from Author’s Deep Learning Models and its application: An overview with the help of R software book and move on to the Python’s important data processing packages such Numpy, and Pandas.
Book then explores the Deep Learning models with the help of packages such as Pytorch, Tensor Flow and Keras and their applications in image processing, stock market prediction, recommender systems and natural language processing.

Table of Contents

Chapter 1 – Introduction to Deep Learning
Chapter 2 – Deep Learning Models
Chapter 3 – Introduction to Python Package
Chapter 4 – Data Processing in Python
Chapter 4 – PyTorch
Chapter 5 – Image Classification using PyTorch
Chapter 6 – Tensorflow
Chapter 7 - Stock market prediction with TensorFlow – Keras - LSTM
Chapter 8 – Sentiment Analysis using TensorFlow – Keras
Chapter 9 – Recommender System
Chapter 10 – Speech Recognition


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