Kessler Adrian M (6 results)

80 pp.; 22.9 x 15.2 cm.; glue bound; black-and-white; edition size unknown; unsigned and unnumbered; offset-printed Exhibition catalogue published in conjunction with show held May 31 - June 30, 1984. Organized by Linda Cathcart. Text by Linda Shearer, Susan Wyatt, and Linda Cathcart. Artists include Ericka Beckman, Gretchen Bender, Dara Birnbaum, Eric Bogosian, Jonathan Borofsky, Troy Brauntuch, Michael Brewster, Gary Burnley, Scott Burton, Michael Byron, Cynthia Carlson, James Casebere, Louisa Chase, Charles Clough, Arch Connelly, Marcia Dalby, Carroll Dunham, Nancy Dwyer, William Fares, R.M. Fischer, Hermine Ford, Stephen Frailey, Bobby G., Jack Goldstein, Don Gummer, David Haxton, Biff Henrich, Jenny Holzer, Rebecca Howland, Mel Kendrick, Jon Kessler, Jeff Koons, Barbara Kruger, Thomas Lanigan-Schmidt, Thomas Lawson, John Lees, Sherrie Levine, Robert Longo, Ree Morton, Matt Mullican, Nic Nicosia, Kevin Noble, Tom Otterness, Ken Pelka, Judy Pfaff, Ellen Phelan, Adrian Piper, James Pomeroy, Richard Prince, Walter Robinson, Tim Rollins, Ellen Rumm, Christy Rupp, David Salle, Cindy Sherman, Laurie Simmons, Charles Simonds, Michael Smith, Philip Smith, Ted Stamm, Donald Sultan, John Torreano, Roger Welch, Yunque, and Michael Zwack. Very Good / Fine. Light dusting to covers. Contents clean and unmarked.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Are you a developer, data scientist, or aspiring AI engineer looking to take your skills to the next level with PyTorch? Hands-On PyTorch for AI and Machine Learning 2026 is your ultimate guide to mastering modern deep learning using the most flexible and research-backed framework available today.This isn't just another machine learning book-it's a practical, project-driven blueprint that walks you through every critical step of designing, building, training, and deploying neural networks using Python and PyTorch. Whether you're transitioning from TensorFlow, starting from scratch, or seeking a real-world playbook for AI, this guide is for you.Inside, you'll learn how to: Understand the fundamentals of AI, machine learning, and deep learning in plain EnglishWork with tensors, autograd, and dynamic computation graphs like a proBuild your first neural network from scratch using torch.nn and SequentialTrain models with optimizers like SGD, Adam, and RMSProp, and fine-tune hyperparametersDevelop powerful CNNs for image classification and apply them to datasets like MNIST and CIFAR-10Dive into natural language processing with RNNs, GRUs, LSTMs, and Transformer architecturesUse pretrained models from torchvision.models and Hugging Face for transfer learningCreate custom datasets, implement data loaders, and write robust preprocessing pipelinesEvaluate your models with precision, recall, F1-score, and visualize performance using TensorBoard or Weights & BiasesDeploy models using Flask, FastAPI, or ONNX-and integrate them into mobile or web appsLeverage PyTorch Lightning to write cleaner, scalable, and production-ready codeStay ahead of the curve with future trends like AutoML, edge AI, quantization, and responsible AI practicesWhat sets this book apart: Future-focused for 2026 and beyond-updated tools, trends, and deployment practicesCode-first, no-fluff approach with real projects and clean architectureWritten for clarity-ideal for developers, ML engineers, and anyone transitioning into AIIncludes practical exercises and deployable templates for career-ready skillsApplicable across industries: healthcare, finance, cybersecurity, robotics, and moreWhether you're building a career in AI, optimizing your production pipelines, or simply want to stay relevant in the era of intelligent software, this book is your hands-on companion.Perfect for learners at any level ready to build deep learning models that actually work. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Are you a developer, data scientist, or aspiring AI engineer looking to take your skills to the next level with PyTorch? Hands-On PyTorch for AI and Machine Learning 2026 is your ultimate guide to mastering modern deep learning using the most flexible and research-backed framework available today.This isn't just another machine learning book-it's a practical, project-driven blueprint that walks you through every critical step of designing, building, training, and deploying neural networks using Python and PyTorch. Whether you're transitioning from TensorFlow, starting from scratch, or seeking a real-world playbook for AI, this guide is for you.Inside, you'll learn how to: Understand the fundamentals of AI, machine learning, and deep learning in plain EnglishWork with tensors, autograd, and dynamic computation graphs like a proBuild your first neural network from scratch using torch.nn and SequentialTrain models with optimizers like SGD, Adam, and RMSProp, and fine-tune hyperparametersDevelop powerful CNNs for image classification and apply them to datasets like MNIST and CIFAR-10Dive into natural language processing with RNNs, GRUs, LSTMs, and Transformer architecturesUse pretrained models from torchvision.models and Hugging Face for transfer learningCreate custom datasets, implement data loaders, and write robust preprocessing pipelinesEvaluate your models with precision, recall, F1-score, and visualize performance using TensorBoard or Weights & BiasesDeploy models using Flask, FastAPI, or ONNX-and integrate them into mobile or web appsLeverage PyTorch Lightning to write cleaner, scalable, and production-ready codeStay ahead of the curve with future trends like AutoML, edge AI, quantization, and responsible AI practicesWhat sets this book apart: Future-focused for 2026 and beyond-updated tools, trends, and deployment practicesCode-first, no-fluff approach with real projects and clean architectureWritten for clarity-ideal for developers, ML engineers, and anyone transitioning into AIIncludes practical exercises and deployable templates for career-ready skillsApplicable across industries: healthcare, finance, cybersecurity, robotics, and moreWhether you're building a career in AI, optimizing your production pipelines, or simply want to stay relevant in the era of intelligent software, this book is your hands-on companion.Perfect for learners at any level ready to build deep learning models that actually work. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.