Deep Learning for Genomics
This book will help you learn how to build and tune state-of-the-art machine learning and deep learning models using Python and industry-standard libraries for deriving biological insights from large amounts of multimodal genomic datasets. You’ll also learn how to deploy these models on several cloud platforms such as AWS and Azure.
Offered by
Difficulty Level
Intermediate
Completion Time
9h
Language
English
About Book
Who Is This Book For?
This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.
Deep Learning for Genomics
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 9h total length
Introducing Machine Learning for Genomics
Genomics Data Analysis
Machine Learning Methods for Genomic Applications
Deep Learning for Genomics
Introducing Convolutional Neural Networks for Genomics
Recurrent Neural Networks in Genomics
Unsupervised Deep Learning with Autoencoders
GANs for Improving Models in Genomics
Building and Tuning Deep Learning Models
Model Interpretability in Genomics
Model Deployment and Monitoring
Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics
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