Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text
Key Features
- Understand how to implement deep learning with TensorFlow and Keras
- Learn the fundamentals of computer vision and image recognition
- Study the architecture of different neural networks
Book Description
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.
By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras.
What you will learn
- 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
Who this book is for
If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.
Table of Contents
- Building Blocks of Deep Learning
- Neural Networks
- Image Classification with Convolutional Neural Networks (CNNs)
- Deep Learning for Text - Embeddings
- Deep Learning for Sequences
- LSTMs, GRUs, and Advanced RNNs
- Generative Adversarial Networks
Mirza Rahim Baig is an avid problem solver who uses deep learning and AI to solve business problems and create impact. A BITS, Pilani graduate, Rahim is a lead at Flipkart, India's largest e-commerce platform. He has a decade of experience in creating value from data, harnessing the power of the latest in Machine learning and AI. Rahim is also a teacher - designing, creating, teaching data science for various learning platforms. He loves making the complex easy to understand.
Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning tool sets across multiple industry segments.
Nipun Sadvilkar is a senior data scientist at US healthcare company leading a team of data scientists and subject matter expertise to design and build the clinical NLP engine to revamp medical coding workflows, enhance coder efficiency, and accelerate revenue cycle. He has experience of more than 3 years in building NLP solutions and web-based data science platforms in the area of healthcare, finance, media, and psychology. His interests lie at the intersection of machine learning and software engineering with a fair understanding of the business domain. He is a member of the regional and national python community. He is author of pySBD - an NLP open-source python library for sentence segmentation which is recognized by ExplosionAI (spaCy) and AllenAI (scispaCy) organizations.
