Title: Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python, Author: Sebastian Raschka
Title: Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks, Author: Keith L. Downing
Title: Practical C++ Machine Learning: Hands-on strategies for developing simple machine learning models using C++ data structures and libraries, Author: Anais Sutherland
Title: The Syntellect Hypothesis: Five Paradigms of the Mind's Evolution, Author: Alex M Vikoulov
Title: Deep Learning and AI Superhero: An in-depth guide to mastering TensorFlow, Keras, PyTorch, and advanced AI techniques, Author: Cuantum Technologies LLC
Title: Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia, Author: Shalom Lappin
Title: Statistics with Rust, Second Edition: Explore rust programming and its powerful crates across data science, machine learning and NLP projects, Author: Keiko Nakamura
Title: Designing Deep Learning Systems: A software engineer's guide, Author: Chi Wang
Title: PyTorch Cookbook, Author: Matthew Rosch
Title: Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures, Author: Lakshya Khandelwal
Title: Neural Networks with R: Uncover the power of artificial neural networks by implementing them through R code., Author: Giuseppe Ciaburro
Title: Elements of Causal Inference: Foundations and Learning Algorithms, Author: Jonas Peters
Title: Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem, Author: Ankur Ankan
Title: Go Machine Learning Projects: Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go, Author: Xuanyi Chew
Title: Managing Machine Learning Projects: From design to deployment, Author: Simon Thompson
Title: Machine Learning for Business: Using Amazon SageMaker and Jupyter, Author: Doug Hudgeon
Title: Data Augmentation with Python: Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data, Author: Duc Haba
Title: Deep Learning with JAX, Author: Grigory Sapunov
Title: Neural Networks with Python, Author: Mei Wong
Title: Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications, Author: Joseph Babcock

Pagination Links