Learn IT - Books tags model

Expert One-on-One Oracle

Apress, 2003

Tom Kyte is of a rare breed. To begin, he's technically expert in his subject (administration of and development of applications for Oracle database management systems). What's more (and what distinguishes him from the ranks of the super-competent), he is both able and willing to share his considerable store of wisdom with Oracle users via...

Machine Learning and Robot Perception (Studies in Computational Intelligence)

Springer, 2005

This book presents some of the most recent research results in the area of machine learning and robot perception. The book contains eight chapters.

Relevant progress has been done, within the Robotics field, in mechanical systems, actuators, control and planning. This fact, allows a wide application of industrial robots, where...

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch

Packt Publishing, 2018

Build neural network models in text, vision and advanced analytics using PyTorch

Key Features

Learn PyTorch for implementing cutting-edge deep learning algorithms.

Train your neural networks for higher speed and flexibility and learn how to implement them in various...

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...

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Packt Publishing, 2018

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras

Key Features

Implement machine learning algorithms to build, train, and validate algorithmic models

Create your own algorithmic design process to apply...

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...

Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Packt Publishing, 2018

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

Key Features

Explore deep reinforcement learning (RL), from the first principles to the latest algorithms

Evaluate high-profile RL methods, including value...

An Introduction to IMS(TM) : Your Complete Guide to IBM's Information Management System

IBM Press, 2004

The Only Up-to-the-Minute Guide to IMS Version 9--Straight from IBM IMS Experts

Introduces IMS, one of the world's premiere software products

Thoroughly covers key IMS functions, from security to Java support

For both new and experienced IMS...

Modelling and Reasoning with Vague Concepts

Springer, 2006

Vagueness is central to the flexibility and robustness of natural language descriptions. Vague concepts are robust to the imprecision of our perceptions, while still allowing us to convey useful, and sometimes vital, information. The study of vagueness in Artificial Intelligence (AI) is therefore motivated by the desire to incorporate this...

A Computational Model of Natural Language Communication: Interpretation, Inference, and Production in Database Semantics

Springer, 2006

Everyday life would be easier if we could simply talk with machines instead of having to program them. Before such talking robots can be built, however, there must be a theory of how communicating with natural language works. This requires not only a grammatical analysis of the language signs, but also a model of the cognitive agent, with...

Knowledge-Based Neurocomputing

MIT Press, 2000

Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based...

Policy-Based Network Management: Solutions for the Next Generation (The Morgan Kaufmann Series in Networking)

Morgan Kaufmann, 2003

Policy-Based Network Management (PBNM) systems enable business rules and procedures to be translated into policies that configure and control the network and its services. Those who manage network systems are aware that this approach can benefit both network management as well as the development of applications that use network services; however,...

Result Page: 274 273 272 271 270 269 268 267 266 265