Learn IT - Books tags neural

Computational Methods for Deep Learning: Theoretic, Practice and Applications (Texts in Computer Science)

Springer, 2020

Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical...

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Packt Publishing, 2019

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features

Build a strong foundation in neural networks and deep learning with Python libraries

Explore advanced deep learning techniques and their applications...

C++ Neural Networks and Fuzzy Logic

M & T Books, 1995

The number of models available in neural network literature is quite large. Very often the treatment is mathematical and complex. This book provides illustrative examples in C++ that the reader can use as a basis for further experimentation. A key to learning about neural networks to appreciate their inner workings is to experiment. Neural...

Deep Learning with Swift for TensorFlow: Differentiable Programming with Swift

Apress, 2021

About this book

Discover more insight about deep learning algorithms with Swift for TensorFlow. The Swift language was designed by Apple for optimized performance and development whereas TensorFlow library was designed by Google for advanced machine learning research. Swift for TensorFlow is a combination of both...

Clustering (IEEE Press Series on Computational Intelligence)

John Wiley & Sons, 2008

Clustering has become an increasingly important topic in recent years, caused by the glut of data from a wide variety of disciplines. However, due to the lack of good communication among these communities, similar theories or algorithms are redeveloped many times, causing unnecessary waste of time and resources. Furthermore, different terminologies...

Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

Packt Publishing, 2018

Be an adaptive thinker that leads the way to Artificial Intelligence

Key Features

AI-based examples to guide you in designing and implementing machine intelligence

Develop your own method for future AI solutions

Acquire advanced AI, machine learning, and...

Artificial Intelligence: A Modern Approach (3rd Edition)

Prentice Hall, 2009

Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is...

Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA

Packt Publishing, 2018

Build GPU-accelerated high performing applications with Python 2.7, CUDA 9, and open source libraries such as PyCUDA and scikit-cuda. We recommend the use of Python 2.7 as this version has stable support across all libraries used in this book.

Key Features

Get to grips with GPU programming...

Advanced Data Mining Techniques

Springer, 2008

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a...

Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

Packt Publishing, 2021

Take your NLP knowledge to the next level and become an AI language understanding expert by mastering the quantum leap of Transformer neural network models

Key Features

Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using...

Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

Apress, 2018

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...

Intelligent Projects Using Python: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras

Packt Publishing, 2019

Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python

Key Features

A go-to guide to help you master AI algorithms and concepts

8 real-world projects tackling different challenges in healthcare, e-commerce, and...

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