by
Richert, Willi.
Call Number
006.76
Publication Date
2013
Summary
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them. This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.
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Electronic Resources
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0.0696
by
Seneque, Gareth, author.
Call Number
006.31 23
Publication Date
2019
Summary
The Go ecosystem comprises some really powerful Deep Learning tools. This book shows you how to use these tools to train and deploy scalable Deep Learning models. You will explore a number of modern Neural Network architectures such as CNNs, RNNs, and more. By the end, you will be able to train your own Deep Learning models from scratch, using ...
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Electronic Resources
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0.0469
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by
Amr, Tarek, author.
Call Number
006.31 23
Publication Date
2020
Summary
This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.
Format:
Electronic Resources
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0.0442
by
Reddy Bokka, Karthiek.
Call Number
006.35 23
Publication Date
2019
Summary
Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues.
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Electronic Resources
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0.0442
by
Saleh, Hyatt.
Call Number
006.31 23
Publication Date
2020
Summary
With this hands-on, self-paced guide, you'll explore crucial deep learning topics and discover the structure and syntax of PyTorch. Challenging activities and interactive exercises will keep you motivated and encourage you to build intelligent applications effectively.
Format:
Electronic Resources
Relevance:
0.0430
by
Dey, Sandipan, author.
Call Number
005.133 23
Publication Date
2020
Format:
Electronic Resources
Relevance:
0.0430
by
Galeone, Paolo, author.
Call Number
006.32 23
Publication Date
2019
Format:
Electronic Resources
Relevance:
0.0430
by
Cheong, Soon Yau, author.
Call Number
006.37 OCOLC 23ENG20230216
Publication Date
2020
Summary
This book is a step-by-step guide to show you how to implement generative models in TensorFlow 2.x from scratch. You'll get to grips with the image generative technology by covering autoencoders, style transfer, and GANs as well as fundamental and state-of-the-art models.
Format:
Electronic Resources
Relevance:
0.0419
by
Auffarth, Ben.
Call Number
005.133 23
Publication Date
2020
Summary
If you are looking to build next-generation AI solutions for work or even for your pet projects, you'll find this cookbook useful. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build smart models for problem-solving.
Format:
Electronic Resources
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0.0409
by
Valle, Rafael, 1985- author.
Call Number
006.31 23
Publication Date
2019
Summary
This book will explore deep learning and generative models, and their applications in artificial intelligence. You will learn to evaluate and improve your GAN models by eliminating challenges that are encountered in real-world applications. You will implement GAN architectures in various domains such as computer vision, NLP, and audio processing.
Format:
Electronic Resources
Relevance:
0.0409
by
Hany, John.
Call Number
005.133 23
Publication Date
2019
Summary
This book will help you understand how GANs architecture works using PyTorch. You will get familiar with the most flexible deep learning toolkit and use it to transform ideas into actual working codes. You will apply GAN models to areas like computer vision, multimedia and natural language processing using a sample-generation perspective.
Format:
Electronic Resources
Relevance:
0.0400
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