The following list offers the Top 10 Deep Learning and Neural Networks books list I would recommend to you read. Once you’re done, you will have a VERY solid handle on the field.
What would you be able to anticipate from reading these books on this list?
The criteria I used to select books list are:
Well-Grounded, Very Practical, Generative, Affordable, Authorship, Highest Reviewed, Stimulates the mind & imagination, Practical Problems, Career Opportunities, Highest Rated, Can be read over & over, Best Sellers, Newest, Without Bias.
Without encourage due, Let’s Get Started!
Top 15 Best Books On Deep Learning And Neural Networks You Should Read
1.) Deep Learning: A Practitioner’s Approach
This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.
Through real-world examples, youíll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.
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2.) Deep Learning with Java
Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java
About This Book * Go beyond the theory and put Deep Learning into practice with Java * Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe *.
Whether you’re a data scientist or Java developer, dive in and find out how to tackle Deep Learning.
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3.) Deep Learning with TensorFlow
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide.
About This Book : * Learn how to implement advanced techniques in deep learning with Google’s brainchild, TensorFlow *
Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide * Real-world contextualization through some deep learning problems concerning research and application.
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4.) R Deep Learning Projects:
5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more.
Get to grips with R’s impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
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5.) Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
With the re-invigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field.
Large companies such as Google, Microsoft and Facebook have taken notice and are actively growing in-house deep learning teams.
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6.) Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks
Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions.
In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
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7.) Deep Learning with TensorFlow
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.
Key Features Learn how to implement advanced techniques in deep learning with Google’s brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide.
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8.) Learning TensorFlow: A Guide to Building Deep Learning Systems
Inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy.
This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
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9.) Hands-On Deep Learning with TensorFlow
This book is your guide to exploring the possibilities in the field of deep learning, making use of Google’s TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data.
If you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise in working with convoluted neural networks, then this book is for you.
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10.) TensorFlow 1.x Deep Learning Cookbook
This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to.
People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful.
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11.) Fundamentals of Artificial Neural Networks (MIT Press)
In Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers.
Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners.
Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics.
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12.) Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (MIT Press)
This book focuses on the subset of feed forward artificial neural networks called multilayer perceptrons (MLP).
These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition).
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12.) Neural Networks: A Comprehensive Foundation (2nd Edition)
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective.
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13.) Neural Networks for Pattern Recognition (Advanced Texts in Econometrics)
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition.
The book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models.
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14.) Neural Network Design (2nd Edition)
This book gives an introduction to basic neural network architectures and learning rules.
Emphasis is placed on the mathematical analysis of these networks, on methods of training them and on their application to practical engineering problems in such areas as pattern recognition signal processing and control system.
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15.) Make Your Own Neural Network
A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language.
Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.
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FINAL WORDS
Start Understanding these books One by One. Perhaps, a $5-10 book can change your Career for time everlasting.
Books changed my life in recent years. I was forced to peruse books in my formal education and avoided from perusing books in my corporate activity.
In any case, now my life relies upon books so to speak..
The above book list are some of most loved books that I would recommend you to enhance your career, a better person and a better individual.
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