This is the code repository for Neural Networks with Keras Cookbook, published by Packt.
Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.
This book covers the following exciting features:
- Build multiple advanced neural network architectures from scratch
- Explore transfer learning to perform object detection and classification
- Build self-driving car applications using instance and semantic segmentation
- Understand data encoding for image, text and recommender systems
- Implement text analysis using sequence-to-sequence learning
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
data['DebtRatio_newoutlier']=np.where(data['DebtRatio']>1,1,0)
data['DebtRatio']=np.where(data['DebtRatio']>1,1,data['DebtRatio'])
Following is what you need for this book: This intermediate-level book targets beginners and intermediate-level machine learning practitioners and data scientists who have just started their journey with neural networks. This book is for those who are looking for resources to help them navigate through the various neural network architectures; you'll build multiple architectures, with concomitant case studies ordered by the complexity of the problem. A basic understanding of Python programming and a familiarity with basic machine learning are all you need to get started with this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-16).
| Chapter | Software required | OS required |
|---|---|---|
| 1-16 | Jupyter Notebook, Python, TensorFlow, | Windows, macOS, and Ubuntu (Any) |
| Keras |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
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