This is a test version of Biostars. For the public version, visit https://www.biostars.org.
News: Introduction to Deep Learning for Biologists - online course, 3–7 November

Course online: Introduction to Deep Learning for Biologists

  • Dates: 3–7 November

This course introduces the core concepts of deep learning, focusing on its application to biological data. Participants will learn how to build and evaluate deep learning models for classification, regression, and image segmentation tasks, with practical hands-on sessions using Python and Jupyter Notebooks.

Whether you’re new to deep learning or looking to deepen your understanding, this course will help you:

  • Understand key deep learning architectures (including CNNs)

  • Frame biological problems for predictive modeling

  • Build, evaluate, and improve deep learning models

  • Apply these tools to real-world biological datasets

This course also inspired the Deep Learning for Life Sciences book, authored by the instructors as part of our Decoding Evolution book series.

machinelearning python deeplearning convolutionalneuralnetwork
• 1,544 views • link
written by Physalia-courses Physalia-courses

1 answer

We are pleased to announce that the Springer Nature book Deep Learning for Life Scientists has now been officially published. Congratulations to Filippo Biscarini and Nelson Nazzicari.

Book (Springer Nature):

https://link.springer.com/book/10.1007/978-3-031-96852-5

This volume is the first title in the new textbook series Decoding Evolution, inspired by years of teaching undergraduate students and supporting researchers who need to carry out their own bioinformatics analyses.

Decoding Evolution series:

https://link.springer.com/series/16507

The Decoding Evolution series aims to bridge theory and practice, addressing real-world challenges in applying bioinformatics methods to evolutionary and biological questions. Each volume focuses on transferring emerging methods and techniques, making them accessible to students, educators, and researchers working with increasingly large and complex datasets.

All books in the series are based on lectures developed through Physalia Courses, which is committed to high-quality training in bioinformatics, genomics, and data science: https://www.physalia-courses.org/

About this first volume: Deep Learning for Life Scientists

Artificial intelligence is rapidly transforming the life sciences, from medical imaging and diagnostics to ecology, agriculture, and wildlife monitoring. Deep learning is becoming an essential tool for modern biologists.

This book introduces:

  • The theory and inner workings of neural networks
  • Core building blocks and mathematical foundations
  • Different network architectures and common pitfalls such as overfitting
  • Practical guidance for applying deep learning to real biological data
  • Successes and failures discussed through interviews with leading experts

The book is accompanied by hands-on Python notebooks, with clearly commented code to help readers move from concepts to practice.

This publication marks the first volume of the Decoding Evolution series, with additional titles planned.

Relevant for readers interested in AI, bioinformatics, computational biology, and modern life science training.

• 0 views • link
written by Physalia-courses Physalia-courses

Log in to answer this question.

More posts like this