Python w uczeniu maszynowym

Matthew Kirk

PyTorch Deep Learning Hands-On. Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily

Sherin Thomas, Sudhanshu Passi

Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage

Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos López de Prado

R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet

PKS Prakash, Achyutuni Sri Krishna Rao

R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition

Mark Hodnett, Joshua F. Wiley

R Deep Learning Projects. Master the techniques to design and develop neural network models in R

Yuxi (Hayden) Liu, Pablo Maldonado

R Machine Learning Essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment

Michele Usuelli

R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

R Programming Fundamentals. Deal with data using various modeling techniques

Kaelen Medeiros

Recurrent Neural Networks with Python Quick Start Guide. Sequential learning and language modeling with TensorFlow

Simeon Kostadinov

Reinforcement Learning with TensorFlow. A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

Sayon Dutta

Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0

Svetlana Karslioglu

Scala for Machine Learning. Leverage Scala and Machine Learning to construct and study systems that can learn from data

Patrick R. Nicolas

Scala Machine Learning Projects. Build real-world machine learning and deep learning projects with Scala

Md. Rezaul Karim

Simplifying Android Development with Coroutines and Flows. Learn how to use Kotlin coroutines and the flow API to handle data streams asynchronously in your Android app

Jomar Tigcal, Aileen Apolo-de Jesus

Spark. Rozproszone uczenie maszynowe na dużą skalę. Jak korzystać z MLlib, TensorFlow i PyTorch

Adi Polak

Splunk 7 Essentials. Demystify machine data by leveraging datasets, building reports, and sharing powerful insights - Third Edition

J-P Contreras, Steven Koelpin, Erickson Delgado, Betsy Page Sigman

Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

James D. Miller

Supervised Machine Learning with Python. Develop rich Python coding practices while exploring supervised machine learning

Taylor Smith

Szeregi czasowe. Praktyczna analiza i predykcja z wykorzystaniem statystyki i uczenia maszynowego

Aileen Nielsen

Sztuczna inteligencja. Błyskawiczne wprowadzenie do uczenia maszynowego, uczenia ze wzmocnieniem i uczenia głębokiego

Hadelin de Ponteves

Sztuczna inteligencja i uczenie maszynowe dla programistów. Praktyczny przewodnik po sztucznej inteligencji

Laurence Moroney

Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 1

Stuart Russell, Peter Norvig

Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 2

Stuart Russell, Peter Norvig