Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process

Maicon Melo Alves, Lúcia Maria de Assumpçao Drummond

Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning

Margaux Masson-Forsythe

Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Rowel Atienza

Advanced Deep Learning with Python. Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

Ivan Vasilev

Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R

Bharatendra Rai

Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5

Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value

Bipin Chadha, Sylvester Juwe

Algorytmy Data Science. Siedmiodniowy przewodnik. Wydanie II

David Natingga

Algorytmy sztucznej inteligencji. Ilustrowany przewodnik

Rishal Hurbans

Algorytmy uczenia maszynowego. Zaawansowane techniki implementacji

Giuseppe Bonaccorso

Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym

Alex J. Gutman, Jordan Goldmeier

Angular and Machine Learning Pocket Primer. A Comprehensive Guide to Angular and Integrating Machine Learning

Mercury Learning and Information, Oswald Campesato

Apache Mahout Essentials. Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout

Jayani Withanawasam

Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark

Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

Ahmed Sherif, Amrith Ravindra

Apache Spark for Machine Learning. Build and deploy high-performance big data AI solutions for large-scale clusters

Deepak Gowda

Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots

Dr. Tania Moulik

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Sumit Ranjan, Dr. S. Senthamilarasu

Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures

Lakshya Khandelwal, Subhajoy Das

Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras

Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

David S. Jordan

Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Alok Malik, Bradford Tuckfield

Artificial Intelligence and Machine Learning Fundamentals. Develop real-world applications powered by the latest AI advances

Zsolt Nagy

Artificial Intelligence By Example. Develop machine intelligence from scratch using real artificial intelligence use cases

Denis Rothman