The book concentrates on an end-to-end method for learning, comprehending, and employing deep neural networks with the aid of Python and Keras, . You will study a practical business issue that deep neural networks and supervised learning algorithms can resolve. Utilizing well-known Kaggle datasets, you’ll take on two regression use cases and two classification use cases. You will have a strong understanding of deep learning concepts after completing Learn Keras for Deep Neural Networks, as well as actual hands-on experience creating enterprise-grade deep learning solutions in Keras. Finally, you’ll improve your deep learning abilities even more and learn about current research and progress in the field.
Overview:
The Keras API was created with people in mind, not with computers. Keras adheres to recommended procedures for lowering cognitive stress. In the event of user error, it offers transparent and useful feedback. It provides clear & consistent APIs. Since Keras tightly interfaces with low-level TensorFlow capabilities, you may create highly hackable workflows with completely customizable functionality. Because you are more productive as a Keras user, you can test more ideas faster than your rivals, which ultimately helps you win machine learning competitions. Scalability of Keras. You may easily execute your models on massive GPU clusters or a complete TPU pod, representing more than one exaFLOPs of processing power, by using the TensorFlow Distribution Strategy API, which is supported natively by Keras.
Topics covered by this book:
- We will briefly introduce the topic of deep learning (DL) in chapter 1 before moving on to look at the most well-liked options for existing frameworks for DL development. In order to comprehend why the Keras ecosystem is unique, we will also look at it more closely.
- We will examine the Keras framework in this chapter 2 and begin with practical tasks to master the fundamentals of Keras, a little bit of Python, and the relevant DL subjects.
- We will examine a DL use case for regression in Chapter 3. We’ll investigate the complete problem-solving methodology using a business-forward perspective.
- In this chapter 4, we will take our learning one step further and create a network for a categorization use case. Additionally, we will advance our knowledge from this chapter with sophisticated DNN designs.
- In this chapter 5, we’ll talk about moving forward after creating the initial model by examining the strategies you should use if the model you created doesn’t live up to your expectations
- In chapter 6 will provide a quick overview of the future. What other subjects are crucial for a data scientist to master in order to succeed in the DL journey? is the question we will attempt to address.
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