Deep learning has gotten a lot of public attention due to the growing interest in AI in the world. Deep learning algorithms are widely employed every day in numerous sectors.

Learn Python's Deep Learning ideas and algorithms to boost your machine learning abilities. Using examples from real-world use cases, this book will provide you with all the applicable practical information on the topic, including the best practices. In order to improve outcomes and increase forecast accuracy, you will learn to recognize and extract information.

The book will go right into deep learning principles using Sci-kit learn after a brief review of key machine learning ideas. You will eventually learn how to use modern open-source libraries like Google's TensorFlow, Theano, Keras, and H20. Discover the challenges of pattern recognition, scale data more precisely, and examine deep learning methods and strategies using this tutorial.

- Get a practical deep dive into deep learning algorithms
- Explore deep learning further with Theano, Caffe, Keras, and TensorFlow
- Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
- Dive into Deep Belief Nets and Deep Neural Networks