Artificial intelligence with Python / Prateek Joshi

No review available on this record. Title : Artificial intelligence with Python : build real-world artificial intelligence applications with Python to intelligently interact with the world around you Material Type: printed text Authors: Prateek Joshi, Author Publisher: Birmingham : Packt Publishing Publication Date: 2017 Pagination: vi, 430 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-7864-6439-2 General note: Inludes index Languages : English (eng) Original Language : English (eng) Descriptors: Algorithms Application software - Development Artificial intelligence - Data processing Computer programming COMPUTERS - Intelligence (AI) & Semantics; COMPUTERS - Programming Languages Python (Computer program language) Class number: 006.3 Abstract: Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you’ll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that’s based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Contents note: Introduction to artificial intelligence; Classification and regression using supervised learning; Predictive analytics with ensemble learning; Detecting patterns with unsupervised learning; Building recommended systems; Logic programming; Heuristic search techniques; Genetic algorithms; Building game with AI; Natural language processing; Probabilistic reasoning for sequential data; Building a speech recognizer; Object detection and tracking; Artificial Neural Networks; Reinforcement learning; Deep learning with convolutional neural networks; Index; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17511

Copies

Barcode	Call number	Media type	Location	Section	Status
1702-002416	006.3 Jos-Art 2017	General Collection	SEEU Library Skopje	English	Not for loan

Python machine learning / Sebastian Raschka

No review available on this record. Title : Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow Material Type: printed text Authors: Sebastian Raschka, Author ; Vahid Mirjalili, Author Edition statement: 2 nd Publisher: Birmingham : Packt Publishing Publication Date: 2017 Pagination: xviii, 595 p. Layout: ill. Size: 24 cm ISBN (or other code): 978-1-7871-2593-3 General note: Includes index Languages : English (eng) Original Language : English (eng) Descriptors: COMPUTERS - Intelligence (AI) & Semantics; Machine learning Python (Computer program language) Class number: 005.133 Abstract: "Python Machine learning" is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Contents note: Giving computers the ability to learn from data; Training simple machine learning algorithms from classification; A tour of machine learning classifiers using scikit-learn; Building good training sets; Compressing data via dimensionality reduction; Learning best practices for model evaluation and hyperparameter tuning; Combining different models for ensemble learning; Applying machine learning to sentiment analysis; Embedding a machine learning model into a web application; Predicting continuous target variables with regression analysis; Working with unlabeled data; Implementing a multilayer artificial neural network from scratch; Parallelizing neural network training with tensorflow; Going deeper; Classifying images with deep convolutional neural networks; Modeling sequential data recurrent neural networks; Index; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=17509

Copies

Barcode	Call number	Media type	Location	Section	Status
1702-002411	005.133 Ras-Pyt 2017	General Collection	SEEU Library Skopje	English	Not for loan