Hands-on Deep Learning
Building Models from Scratch
- Publisher's listprice EUR 74.89
-
31 060 Ft (29 581 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 6 212 Ft off)
- Discounted price 24 848 Ft (23 665 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
31 060 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 23 December 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783032004871
- Binding Hardback
- No. of pages245 pages
- Size 235x155 mm
- Language English
- Illustrations XIV, 245 p. 56 illus., 44 illus. in color. 700
Categories
Long description:
This book is designed for data scientists and machine learning engineers who are keen to dive deep into the complexities of deep learning. The book is particularly useful for professionals in industries where machine learning is applied. It serves as a comprehensive guide for those eager to explore and expand their knowledge in this domain. The book caters to aspirational practitioners, those who are enthusiastic about the field of deep learning in general, being also suitable for engineers and data scientists who are preparing for machine learning interviews. Furthermore, undergraduate and graduate students who possess a basic understanding of machine learning will find this book to be a valuable resource.
Learning to create deep learning algorithms from scratch provides a deeper understanding of the underlying principles and mechanics, which can be beneficial in customizing and optimizing models for specific tasks. As such, this book will allow the readers to innovate, creating new architectures or techniques beyond what existing libraries offer. Moreover, it fosters a problem-solving mindset, as the learner navigates through the challenges of implementing complex algorithms. This knowledge will help readers and learners to debug and improve models using pre-built libraries.
The author goes beyond just explaining the theory of deep learning, connecting theoretical ideas to their real-world implementations, and dives into how the theoretical aspects of deep learning can be applied in real-world scenarios. Through hands-on examples and case studies, the author demonstrates the application of deep learning principles in solving problems across diverse domains like computer vision, natural language processing, and business analytics.
MoreTable of Contents:
"
""1-Introduction"".- ""2-Implementing the gradient descent algorithm"".- ""3-Training deep neural networks"".- ""4-Dealing with bias and variance"".- ""5-Leveraging advanced optimization techniques"".- ""6- Applying convolutional neural networks"".- ""7-Creating recurrent neural networks"".- ""8-Crafting long short-term memory networks"".- ""9-Using embeddings in language models"".- ""10-Assembling attention mechanisms and transformers"".
" More
