40 Algorithms Every Programmer Should Know. Hone your problem-solving skills by learning different algorithms and their implementation in Python

Imran Ahmad

40 algorytmów, które powinien znać każdy programista. Nauka implementacji algorytmów w Pythonie

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A Developer's Guide to .NET in Azure. Build quick, scalable cloud-native applications and microservices with .NET 6.0 and Azure

Anuraj Parameswaran, Tamir Al Balkhi

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

Actionable Insights with Amazon QuickSight. Develop stunning data visualizations and machine learning-driven insights with Amazon QuickSight

Manos Samatas

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

Ivan Vasilev

Advanced Deep Learning with TensorFlow 2 and Keras. Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more - Second Edition

Rowel Atienza

Advanced Python Programming. Accelerate your Python programs using proven techniques and design patterns - Second Edition

Quan Nguyen

Advanced Python Programming. Build high performance, concurrent, and multi-threaded apps with Python using proven design patterns

Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

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

Bipin Chadha, Sylvester Juwe

Algorithmic Short Selling with Python. Refine your algorithmic trading edge, consistently generate investment ideas, and build a robust long/short product

Laurent Bernut, Michael Covel

Algorytmy Data Science. Siedmiodniowy przewodnik. Wydanie II

David Natingga

Algorytmy dla bystrzaków

John Paul Mueller, Luca Massaron

Algorytmy kryptograficzne w Pythonie. Wprowadzenie

Shannon W. Bray

Amazon Redshift Cookbook. Recipes for building modern data warehousing solutions

Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel, Eugene Kawamoto

Analiza danych behawioralnych przy użyciu języków R i Python

Florent Buisson

Ansible 2 Cloud Automation Cookbook. Write Ansible playbooks for AWS, Google Cloud, Microsoft Azure, and OpenStack

Aditya Patawari, Vikas Aggarwal

Apache Spark 2: Data Processing and Real-Time Analytics. Master complex big data processing, stream analytics, and machine learning with Apache Spark

Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, ...

Aplikacje internetowe z Django. Najlepsze receptury

Aidas Bendoraitis

Applied Computational Thinking with Python. Design algorithmic solutions for complex and challenging real-world problems

Sofía De Jesús, Dayrene Martinez

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 with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python

Benjamin Johnston, Aaron Jones, Christopher Kruger