Pro deep learning with TensorFlow 2.0 : a mathematical approach to advanced artificial intelligence in Python By Santanu Pattanayak

By:

Pattanayak, Santanu

Material type: Text

TextPublication details: Apress, c2023.Edition: 2Description: i-xx+652PISBN:

9781484289303

Subject(s):

Big Data COMPUTERS General Computer Science Computing Methodologies Databases Intelligence artificielle Machine learning Programming & scripting languages: general Python TensorFlow (Electronic resource) artificial intelligence

DDC classification:

2 006.31 PAT-P

Contents:

Chapter 1: Mathematical Foundations Chapter 2: Introduction to Deep Learning Concepts and TensorFlow Chapter 3: Convolutional Neural Networks Chapter 4: Natural Language Processing Using Recursive Neural Networks Chapter 5: Unsupervised Learning with Restricted Boltzmann Machines and Auto Encoders Chapter 6: Advanced Neural Networks

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Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics	Dept. of Computational Biology and Bioinformatics	006.31 PAT-P (Browse shelf(Opens below))	Checked out to SHEEBA K (DCBPDF20016)	14/08/2024	DCB4318

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Previous	006.31 OSI/D Deep learning cookbook	006.31 PAT-D Deep learning : a Practitioner's approach	006.31 PAT-I IoT, AI, and Blockchain for .NET: Building a Next-Generation Application from the Ground Up	006.31 PAT-P Pro deep learning with TensorFlow 2.0 : a mathematical approach to advanced artificial intelligence in Python	006.31 PRA-M Machine learning using Python	006.31 QUI-N Next -generation machine learning with spark: Covers XGBoost, lightGBM, spark NLP, distributed deep learning with Keras and more	006.31 RAV-H Hands-on reinforcement learning with Python : Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow	Next

Chapter 1: Mathematical Foundations
Chapter 2: Introduction to Deep Learning Concepts and TensorFlow
Chapter 3: Convolutional Neural Networks
Chapter 4: Natural Language Processing Using Recursive Neural Networks
Chapter 5: Unsupervised Learning with Restricted Boltzmann Machines and Auto Encoders
Chapter 6: Advanced Neural Networks

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