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A Practical Guide to Deep Learning with Keras

Implement AI with Keras for building complex Deep Learning neural networks with fewer lines of coding in Python

A Practical Guide to Deep Learning with Keras

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Udemy Course Offer Description

Keras is an Open source Neural Network library written in Python. It is a Deep Learning library for fast, efficient training of Deep Learning models. It is a minimal, highly modular framework that runs on both CPUs and GPUs and allows you to put your ideas into action in the shortest possible time. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time.

This comprehensive 3-in-1 course takes a step-by-step practical approach to implement fast and efficient Deep Learning models: Projects on Image Processing and Reinforcement Learning. Initially, you’ll learn backpropagation, install and configure Keras to understand callbacks and customize the process. You’ll develop a deep learning network from scratch with Keras using Python to solve a practical problem of classifying the traffic signs on the road. Finally, you’ll get to grips with Keras to implement fast and efficient deep-learning models with ease.

Towards the end of this course, you'll use AI with Keras for building complex Deep Learning networks with fewer lines of coding in Python.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Deep Learning with Keras, covers implementing deep learning neural networks with Python. Keras is a high-level neural network library written in Python and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPUs and GPUs and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner.

The second course, Hands-On Artificial Intelligence with Keras and Python, covers how to use AI with Keras for building complex Deep Learning networks with fewer lines of coding in Python. This course will help you learn by doing an industry relevant problem in image processing domain, develop and understand automation and AI techniques. You will learn how to harness the power of algorithms by creating apps which intelligently interact with the world around you, addressing common challenges faced in AI ecosystem. By the end of the course, you will be able to build real-world artificial intelligence applications using Keras and Python.

Towards the end of this course, you'll use AI with Keras for building complex Deep Learning networks with fewer lines of coding in Python.

About the Authors

  • Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and has managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields ranging from publishing (Elsevier) to consumer internet (Ask .com and Tiscali) and high-tech R&D (Microsoft and Google).

  • Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.

  • Sandipan Das is working as a senior software engineer in the field of perception within the Autonomous vehicles industry in Sweden. He has more than 8 years of experience in developing and architecting various software components. He understands the industry needs and the gaps in between a traditional university degree and the job requirements in the industry. He has worked extensively on various neural network architectures and deployed them in real vehicles for various perception tasks in real-time.

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