The Deep Learning Workshop
- Paperback: 474 pages
- Publisher: WOW! eBook (July 31, 2020)
- Language: English
- ISBN-10: 1839219858
- ISBN-13: 978-1839219856
The Deep Learning Workshop: Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text
Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout.
The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis.
- Understand how deep learning, machine learning, and artificial intelligence are different
- Develop multilayer deep neural networks with TensorFlow
- Implement deep neural networks for multiclass classification using Keras
- Train CNN models for image recognition
- Handle sequence data and use it in conjunction with RNNs
- Build a GAN to generate high-quality synthesized images
By the end of this The Deep Learning Workshop book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.