Learning Deep Learning | By Magnus Ekman | First Edition | Pearson Publication ( English Medium )

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Learning Deep Learning | By Magnus Ekman | First Edition | Pearson Publication ( English Medium ).

"Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow" by Magnus Ekman is a comprehensive guide designed to introduce readers to the fundamentals and advanced concepts of deep learning. Published by Pearson Education in 2021, this book serves as an invaluable resource for students, developers, data scientists, and professionals aiming to understand and apply deep learning techniques effectively.

Key Features

Foundational Concepts: Begins with the basics of neural networks, including perceptrons, gradient-based learning, sigmoid neurons, and backpropagation.

Advanced Architectures: Explores complex structures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and the Transformer architecture.

Practical Applications: Demonstrates the application of deep learning in computer vision and natural language processing, covering models such as Mask R-CNN, GPT, and BERT.

Hands-on Coding: Provides well-annotated code examples using TensorFlow with Keras, facilitating practical understanding and implementation.

Comprehensive Coverage: Addresses topics like neural architecture search (NAS), ethical considerations in AI, and offers resources for further learning.

Table of Contents

Introduction to Deep Learning

Artificial Neurons and Neural Networks

Training Deep Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks and LSTM

Natural Language Processing with Deep Learning

Transformers and Attention Mechanisms

Advanced Applications in Computer Vision and NLP

Neural Architecture Search

Ethical Implications and Future Directions in AI

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Product Details

SKU / BOOK Code:	Pearson-Lear-Deep-Learning-(E)
Publisher:	Pearson Publication
Author:	Magnus Ekman
Binding Type:	Paperback
No. of Pages:	760
ISBN-10:	9356063974
ISBN-13:	978-9356063976
Edition:	First Edition
Language:	English Medium
Publish Year:	2025-05
Weight (g):	2500
Product Condition:	New
Reading Age:	Above 10 Years
Country of Origin:	India
Genre:	Competitive Books
Manufacturer:	Pearson Publication
Importer:	Pearson Publication
Packer:	Fullfilled by Supplier

Product Description

Learning Deep Learning | By Magnus Ekman | First Edition | Pearson Publication ( English Medium ).

"Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow" by Magnus Ekman is a comprehensive guide designed to introduce readers to the fundamentals and advanced concepts of deep learning. Published by Pearson Education in 2021, this book serves as an invaluable resource for students, developers, data scientists, and professionals aiming to understand and apply deep learning techniques effectively.

Key Features

Foundational Concepts: Begins with the basics of neural networks, including perceptrons, gradient-based learning, sigmoid neurons, and backpropagation.

Advanced Architectures: Explores complex structures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and the Transformer architecture.

Practical Applications: Demonstrates the application of deep learning in computer vision and natural language processing, covering models such as Mask R-CNN, GPT, and BERT.

Hands-on Coding: Provides well-annotated code examples using TensorFlow with Keras, facilitating practical understanding and implementation.

Comprehensive Coverage: Addresses topics like neural architecture search (NAS), ethical considerations in AI, and offers resources for further learning.

Table of Contents

Introduction to Deep Learning

Artificial Neurons and Neural Networks

Training Deep Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks and LSTM

Natural Language Processing with Deep Learning

Transformers and Attention Mechanisms

Advanced Applications in Computer Vision and NLP

Neural Architecture Search

Ethical Implications and Future Directions in AI

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