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Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects
Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python
Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook DescriptionNeural 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.
What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long Short-Term MemoryBuild and train a highly accurate facial recognition security systemWho this book is forThis 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.
Table of ContentsMachine Learning and Neural Networks 101Predicting Diabetes with Multilayer PerceptronsPredicting Taxi Fares with Deep Feedforward NetworksCats Versus Dogs - Image Classification Using CNNsRemoving Noise from Images Using AutoencodersSentiment Analysis of Movie Reviews Using LSTMImplementing a Facial Recognition System with Neural NetworksWhat’s Next?
Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook DescriptionNeural 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.
What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long Short-Term MemoryBuild and train a highly accurate facial recognition security systemWho this book is forThis 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.
Table of ContentsMachine Learning and Neural Networks 101Predicting Diabetes with Multilayer PerceptronsPredicting Taxi Fares with Deep Feedforward NetworksCats Versus Dogs - Image Classification Using CNNsRemoving Noise from Images Using AutoencodersSentiment Analysis of Movie Reviews Using LSTMImplementing a Facial Recognition System with Neural NetworksWhat’s Next?
580 pages, Kindle Edition
Published February 28, 2019
48 people are currently reading
56 people want to read
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Displaying 1 - 5 of 5 reviews
December 29, 2020
"Machine learning and artificial intelligence (AI) have become ubiquitous in our everyday lives. Wherever we go, whatever we do, we are constantly interacting with AI in one way or another. And neural networks and deep learning are driving these AI advances. Powered by neural networks, AI systems are now able to achieve human-like performance in many areas."
Two days ago I posted an enthusiastic review of Hands-On Machine Learning with Scikit-Learn & TensorFlow (I am "borrowing" a portion of this paragraph and the entire second paragraph from that review). This is the other book that was important to me and my students in 2020, one that helped me return to the field of neural networks and machine learning in general and helped my outstanding research student complete her challenging and advanced research project with extraordinary success.
I worked with neural networks (NN) in the late 1980s and early 1990s and even taught a course on neural network learning. However, in the 1990s it had become clear that the limits of what the then traditional NN architecture can achieve had been reached and the scientific community basically abandoned NNs as the preferred approach to machine learning. Yet beginning in the first decade of the 21st century we witnessed the rebirth of the NN idea, primarily via various multi-level NN models, such as convolutional neural networks (CNNs), developed by Le Cun, Hinton, and others. Currently, CNNs achieve truly spectacular (one can say 'superhuman' without exaggeration) results in various areas of artificial intelligence (AI) and machine learning (ML).
James Loy's Neural Network Projects with Python is a modest yet a very good text on developing NNs in Python with the Keras, pandas, NumPy, and TensorFlow libraries. The publisher's blurb on the cover, "The ultimate guide to using Python to explore the true power of neural networks through six projects," accurately characterizes the text, if we remove the hype word "ultimate." This is a perfect text for a serious student. The projects are well selected and clearly explained; the book comes with complete and meticulously checked set of instructions which help the reader - in case the reader is a Python beginner - with installing Anaconda, the free and open-source distribution of Python and its libraries. Then, it guides the reader through setting up the Python virtual environment, including all needed libraries. Setting up the environment took me only about 15 minutes and proceeded without any hitch.
The book consists of eight chapters. The first chapter, Machine Learning and Neural Networks 101 provides a nice introduction to the topic and presents the toolkits/libraries used in the projects. Then come six chapters each covering a specific project: beginning with the multilayer perceptrons, through deep feedforward networks, convolutional NNs, autoencoders, recurrent NNs (in particular, LSTM, long short-term memory networks), to Siamese NNs. The practical applications of the projects include: predicting diabetes, predicting taxi fares in New York City, image classification (the "cats versus dogs" problem), removing noise from images, sentiment analysis of movie reviews, and facial recognition system.
The last chapter, What's Next?, summarizes the projects, presents some newest methods, for instance, the fascinating GANs (generative adversarial networks) that can generate images of fake human faces indistinguishable from photographs of real people, and discusses the possible future directions of ML and AI.
I am highly recommending Neural Network Projects. A small, modest text fully delivers on its promise and gives great samples of code.
Three-and-three-quarter stars.
Two days ago I posted an enthusiastic review of Hands-On Machine Learning with Scikit-Learn & TensorFlow (I am "borrowing" a portion of this paragraph and the entire second paragraph from that review). This is the other book that was important to me and my students in 2020, one that helped me return to the field of neural networks and machine learning in general and helped my outstanding research student complete her challenging and advanced research project with extraordinary success.
I worked with neural networks (NN) in the late 1980s and early 1990s and even taught a course on neural network learning. However, in the 1990s it had become clear that the limits of what the then traditional NN architecture can achieve had been reached and the scientific community basically abandoned NNs as the preferred approach to machine learning. Yet beginning in the first decade of the 21st century we witnessed the rebirth of the NN idea, primarily via various multi-level NN models, such as convolutional neural networks (CNNs), developed by Le Cun, Hinton, and others. Currently, CNNs achieve truly spectacular (one can say 'superhuman' without exaggeration) results in various areas of artificial intelligence (AI) and machine learning (ML).
James Loy's Neural Network Projects with Python is a modest yet a very good text on developing NNs in Python with the Keras, pandas, NumPy, and TensorFlow libraries. The publisher's blurb on the cover, "The ultimate guide to using Python to explore the true power of neural networks through six projects," accurately characterizes the text, if we remove the hype word "ultimate." This is a perfect text for a serious student. The projects are well selected and clearly explained; the book comes with complete and meticulously checked set of instructions which help the reader - in case the reader is a Python beginner - with installing Anaconda, the free and open-source distribution of Python and its libraries. Then, it guides the reader through setting up the Python virtual environment, including all needed libraries. Setting up the environment took me only about 15 minutes and proceeded without any hitch.
The book consists of eight chapters. The first chapter, Machine Learning and Neural Networks 101 provides a nice introduction to the topic and presents the toolkits/libraries used in the projects. Then come six chapters each covering a specific project: beginning with the multilayer perceptrons, through deep feedforward networks, convolutional NNs, autoencoders, recurrent NNs (in particular, LSTM, long short-term memory networks), to Siamese NNs. The practical applications of the projects include: predicting diabetes, predicting taxi fares in New York City, image classification (the "cats versus dogs" problem), removing noise from images, sentiment analysis of movie reviews, and facial recognition system.
The last chapter, What's Next?, summarizes the projects, presents some newest methods, for instance, the fascinating GANs (generative adversarial networks) that can generate images of fake human faces indistinguishable from photographs of real people, and discusses the possible future directions of ML and AI.
I am highly recommending Neural Network Projects. A small, modest text fully delivers on its promise and gives great samples of code.
Three-and-three-quarter stars.
October 9, 2025
واقعا برای قدم اول و راه افتادن عالیه. پروژههاش واقعا کاربردی و جدی هستن و صرفا جنبه آموزشی ندارن. با کمک چت جی پی تی، میشه حتی بهره بیشتری هم ازش برد.
پ.ن.: این کتاب اصلا به ریاضی و تئوری پشت الگوریتمها نمیپردازه و چیزی هست که باید جداگانه پیگیر شد و یاد گرفت.
پ.ن.2: پیشفرض اینه که خواننده با پایتون آشنا هست.
پ.ن.: این کتاب اصلا به ریاضی و تئوری پشت الگوریتمها نمیپردازه و چیزی هست که باید جداگانه پیگیر شد و یاد گرفت.
پ.ن.2: پیشفرض اینه که خواننده با پایتون آشنا هست.
March 12, 2022
quite good for someone with relatively little knowledge of deep learning
September 26, 2023
Awesome book
Well paced
Excellent descriptions
Excellent place to get started with machine learning
While older now, it gives you a great start
Well paced
Excellent descriptions
Excellent place to get started with machine learning
While older now, it gives you a great start
November 5, 2021
This book is fantastic. Projects in book is actual projects similar to real life so after completing doing that you will be able like an experienced Deep learning practitioner. I recommend this book to everybody who is starting on the same field
Displaying 1 - 5 of 5 reviews