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Deep Learning with Keras 2

Jerry Kurata

Deep Learning lies at the heart of many leading machine learning and artificial intelligence applications. This course, Deep Learning with Keras, shows you how to use Keras to quickly create powerful deep neural networks.

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Deep Learning lies at the heart of many leading machine learning and artificial intelligence applications. This course, Deep Learning with Keras, shows you how to use Keras to quickly create powerful deep neural networks.

There has been a revolution in artificial intelligence (AI) and machine learning, and deep learning-based solutions are leading the charge. Implementing these solutions can be tedious to create and require you to write many lines of complex code. Keras is a library that makes it much easier for you to create these deep learning solutions. In a few lines of code, you can create a model that could require hundreds of lines of conventional code.

This course, Deep Learning with Keras, will get you up to speed with both the theory and practice of using Keras to implement deep neural networks.

First, you will dive deep into learning how Keras implements various layers of neurons quickly and easily, with each layer defining the specific functionality needed to implement parts of your solution.

Next, you will discover how to use Keras’ various methods for interconnecting these layers to form the structure of your deep neural networks. Finally, you will learn how you use Keras to implement several state-of-the-art neural networks, such as the widely used Convolutional and Recurrent Neural Networks, to make these concepts come to life.

By the end of this course, you will gain the skills and experience required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.

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What's inside

Syllabus

Course Overview
Introducing Keras
Creating Your First Neural Network with Keras
Constructing Models in Keras
Read more
Employing Layers in Keras Models
Building Convolutional NN with Keras
Implementing Recurrent Neural Nets with Keras
Using Specialty Layers and Functions
Last Words

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops foundational skills in building and implementing Deep Neural Networks with Keras
Teaches practical skills in Deep Learning with Keras

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Career center

Learners who complete Deep Learning with Keras 2 will develop knowledge and skills that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineers design and develop deep learning models. They use these models to solve complex problems in computer vision, natural language processing, and other fields. Deep Learning Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Deep Learning Engineer.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop artificial intelligence systems. These systems can be used to solve a wide range of problems, including image recognition, natural language processing, and speech recognition. Artificial Intelligence Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Artificial Intelligence Engineer.
Machine Learning Engineer
A Machine Learning Engineer uses data science and machine learning to solve real-world problems. Machine Learning Engineers develop and implement machine learning models, which are used to make predictions and decisions based on data. This course will help you build a strong foundation in machine learning, and it will teach you how to use Keras to create powerful deep neural networks. This knowledge and these skills will be essential for success as a Machine Learning Engineer.
Data Scientist
Data Scientists use data to solve business problems. They use machine learning and other techniques to analyze data, identify trends, and make predictions. This course will help you build a strong foundation in machine learning, and it will teach you how to use Keras to create powerful deep neural networks. This knowledge and these skills will be essential for success as a Data Scientist.
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems. These systems can be used to solve a wide range of problems, including object detection, facial recognition, and medical imaging. Computer Vision Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Computer Vision Engineer.
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop natural language processing systems. These systems can be used to solve a wide range of problems, including machine translation, text summarization, and sentiment analysis. Natural Language Processing Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Natural Language Processing Engineer.
Speech Recognition Engineer
Speech Recognition Engineers design and develop speech recognition systems. These systems can be used to solve a wide range of problems, including voice control, dictation, and customer service. Speech Recognition Engineers need a strong foundation in machine learning, and they need to be able to use Keras and other deep learning frameworks. This course will help you build the skills you need to be a successful Speech Recognition Engineer.
Software Engineer
Software Engineers design, develop, and maintain software systems. They use a variety of programming languages and technologies to create software that meets the needs of users. Software Engineers need a strong foundation in computer science, and they need to be able to learn new technologies quickly. This course will help you build the skills you need to be a successful Software Engineer.
Data Analyst
Data Analysts use data to solve business problems. They use a variety of techniques to analyze data, identify trends, and make predictions. Data Analysts need a strong foundation in statistics and mathematics, and they need to be able to communicate their findings effectively. This course may help you build the skills you need to be a successful Data Analyst.
Business Analyst
Business Analysts use data to solve business problems. They work with stakeholders to understand their needs and then develop solutions that meet those needs. Business Analysts need a strong foundation in business and technology, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Business Analyst.
Operations Manager
Operations Managers plan and execute operations. They work with stakeholders to define the operations strategy, develop an operations plan, and track progress. Operations Managers need a strong foundation in operations management, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Operations Manager.
Product Manager
Product Managers develop and manage products. They work with stakeholders to define the product vision, develop a product roadmap, and track progress. Product Managers need a strong foundation in product management, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Product Manager.
Marketing Manager
Marketing Managers plan and execute marketing campaigns. They work with stakeholders to define the marketing strategy, develop a marketing plan, and track progress. Marketing Managers need a strong foundation in marketing, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Marketing Manager.
Sales Manager
Sales Managers plan and execute sales campaigns. They work with stakeholders to define the sales strategy, develop a sales plan, and track progress. Sales Managers need a strong foundation in sales, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Sales Manager.
Project Manager
Project Managers plan and execute projects. They work with stakeholders to define the project scope, develop a project plan, and track progress. Project Managers need a strong foundation in project management, and they need to be able to communicate effectively. This course may help you build the skills you need to be a successful Project Manager.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Deep Learning with Keras 2.
Provides a comprehensive overview of deep learning, covering fundamental concepts such as neural networks, convolutional neural networks, and recurrent neural networks. It also provides practical guidance on how to implement deep learning models using Python and Keras.
Provides a hands-on introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including word embeddings, sequence models, and attention mechanisms.
Provides a comprehensive overview of generative adversarial networks (GANs). It covers the theoretical foundations of GANs, as well as practical guidance on how to implement GANs using Keras.
Provides a comprehensive overview of autoencoders. It covers the theoretical foundations of autoencoders, as well as practical guidance on how to implement autoencoders using Keras.
Provides a comprehensive overview of deep reinforcement learning. It covers the theoretical foundations of deep reinforcement learning, as well as practical guidance on how to implement deep reinforcement learning algorithms using Keras.
Provides a comprehensive overview of multi-agent deep reinforcement learning. It covers the theoretical foundations of multi-agent deep reinforcement learning, as well as practical guidance on how to implement multi-agent deep reinforcement learning algorithms using Keras.
Provides a comprehensive overview of machine learning for time series. It covers a wide range of topics, including time series forecasting, anomaly detection, and time series classification.
Provides a comprehensive overview of deep learning for medical image analysis. It covers a wide range of topics, including medical image segmentation, medical image classification, and medical image retrieval.
Provides a hands-on introduction to natural language processing using PyTorch. It covers a wide range of topics, including text classification, sentiment analysis, and machine translation.

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