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Neural Network Projects with Python
Neural Network Projects with Python

Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects

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Profile Icon James Loy
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (15 Ratings)
Paperback Feb 2019 308 pages 1st Edition
eBook
Mex$721.99
Paperback
Mex$902.99
Subscription
Free Trial
Arrow left icon
Profile Icon James Loy
Arrow right icon
Free Trial
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6 (15 Ratings)
Paperback Feb 2019 308 pages 1st Edition
eBook
Mex$721.99
Paperback
Mex$902.99
Subscription
Free Trial
eBook
Mex$721.99
Paperback
Mex$902.99
Subscription
Free Trial

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Neural Network Projects with Python

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Key benefits

  • Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI
  • Build expert neural networks in Python using popular libraries such as Keras
  • Includes projects such as object detection, face identification, sentiment analysis, and more

Description

Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.

Who is this book for?

This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.

What you will learn

  • Learn various neural network architectures and its advancements in AI
  • Master deep learning in Python by building and training neural network
  • Master neural networks for regression and classification
  • Discover convolutional neural networks for image recognition
  • Learn sentiment analysis on textual data using Long Short-Term Memory
  • Build and train a highly accurate facial recognition security system

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 28, 2019
Length: 308 pages
Edition : 1st
Language : English
ISBN-13 : 9781789138900
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Product Details

Publication date : Feb 28, 2019
Length: 308 pages
Edition : 1st
Language : English
ISBN-13 : 9781789138900
Category :
Languages :
Tools :

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Table of Contents

8 Chapters
Machine Learning and Neural Networks 101 Chevron down icon Chevron up icon
Predicting Diabetes with Multilayer Perceptrons Chevron down icon Chevron up icon
Predicting Taxi Fares with Deep Feedforward Networks Chevron down icon Chevron up icon
Cats Versus Dogs - Image Classification Using CNNs Chevron down icon Chevron up icon
Removing Noise from Images Using Autoencoders Chevron down icon Chevron up icon
Sentiment Analysis of Movie Reviews Using LSTM Chevron down icon Chevron up icon
Implementing a Facial Recognition System with Neural Networks Chevron down icon Chevron up icon
What's Next? Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(15 Ratings)
5 star 80%
4 star 6.7%
3 star 6.7%
2 star 6.7%
1 star 0%
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Top Reviews

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sekhar Jun 28, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is very good for beginners in tensorflow and neural network and cnn
Amazon Verified review Amazon
Cuauhtemoc Olmedo-Bustillo Nov 12, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excelent examples of increasing difficulty with very detailed explanations.
Amazon Verified review Amazon
C Abhiram May 23, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very Good Book for people who want to go deeper into the concept.But, you need to have prior programming skills in python, just the basics as the book explains each line of code pretty well.Dont pay any money to learn python, just learn it on youtube it will be sufficient.
Amazon Verified review Amazon
paulbright Mar 18, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I thoroughly enjoyed this book. It starts with a simple raw NN network written in python without using any libraries. From here it proceeds to develop six end to end NN models complete with exploratory data analysis, visualization, feature engineering, actual NN creation and then a very helpful summary. It covers MLP, CNN, RNN, NLP, Siamese Neural Network etc. I highly recommend this to any student of Deep Learning who want to understand end to end development of a ML models.
Amazon Verified review Amazon
raj Sep 08, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very good text book on neural net. The book supplements with some of the real life problems and its implementation with detailed explanation. Most of the case studies are taken from Kaggle. Highly recommended
Amazon Verified review Amazon
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