What skills or experience do I need to already have, before starting to learn Keras?

Since Keras is based in Python, you'll need to have experience using this programming language before starting to learn Keras. You should also have a basic understanding of foundational machine learning concepts. It's also helpful to have an understanding of what deep learning is as well as strong math skills.

How do I know if learning Keras is right for me?

Learning Keras is likely right for you if you're pursuing a career in neural network framework and deep learning. Deep learning refers to methods of machine learning that are based on algorithms created in artificial neural networks that are modeled after the function and structure of the brain. You might be working toward a career or already in one as a software engineer, for example, or a data analyst, data engineer, research analyst, software developer, or bioinformatics analyst. If so, learning Keras may be right for you.

What topics can I study that are related to Keras?

TensorFlow, Theano, and CNTK are three topics you can study that are closely related to Keras because Keras runs on top of these libraries. You can also study the human brain to form a deeper understanding of the premise of neural networks. Any topic related to machine learning may also be of interest to you, such as supervised, unsupervised, and reinforcement learning; inductive, deductive, and transductive learning; or multi-task, active, online, transfer, and ensemble learning. You can also learn more about deep learning and neural networks.

What types of places hire people with a background in Keras?

Places that hire people with a background in Keras include companies and organizations that employ data and software analysts and developers. For example, you'll find people with a background in Keras working for cloud computing platforms like Amazon Web Services, professional services networks like Deloitte, telecommunications companies such as Verizon, and cybersecurity companies like Carbon Black. Other notable employers of people with a background in Keras include JP Morgan Chase Bank, Microsoft, Facebook, Ford Motor Company, and Lockheed Martin.

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207 resultados referentes a "keras"

IBM Skills Network
Introduction to Deep Learning & Neural Networks with Keras
Habilidades que você terá: Theoretical Computer Science, Statistical Programming, Artificial Neural Networks, Keras, Machine Learning, Convolutional Neural Network, Mathematics, Probability & Statistics, Algorithms, Deep Learning, Python Programming, Computer Programming
4.7
(1.1k avaliações)
Intermediate · Course · 1-3 Months
University of Colorado Boulder
Machine Learning: Theory and Hands-on Practice with Python
Habilidades que você terá: Applied Machine Learning, Artificial Neural Networks, Correlation And Dependence, Data Management, Data Structures, Deep Learning, Machine Learning, Probability & Statistics, Regression, Reinforcement Learning, Statistical Machine Learning, Theoretical Computer Science
3.4
(14 avaliações)
Intermediate · Specialization · 3-6 Months
Google Cloud
Intro to TensorFlow en Español
Habilidades que você terá: Computer Program, Python Programming, Computer Programming, Machine Learning, Full-Stack Web Development, Statistical Programming, Front-End Web Development, Web Development, Software Engineering, Applied Machine Learning, Agile Software Development, Tensorflow, Deep Learning
4.4
(122 avaliações)
Intermediate · Course · 1-3 Months

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Em resumo, aqui estão 10 dos nossos cursos mais populares keras

Introduction to Deep Learning & Neural Networks with Keras: IBM Skills Network
Traffic Sign Classification Using Deep Learning in Python/Keras: Coursera Project Network
Machine Learning: Theory and Hands-on Practice with Python: University of Colorado Boulder
Building a Keras Horse Zebra CycleGAN Webapp with Streamlit: Coursera Project Network
Intro to TensorFlow en Español: Google Cloud
Transfer Learning for NLP with TensorFlow Hub: Coursera Project Network
Creating Multi Task Models With Keras: Coursera Project Network
Facial Expression Recognition with Keras: Coursera Project Network
Classify Radio Signals from Space using Keras: Coursera Project Network
Understanding Deepfakes with Keras: Coursera Project Network

Perguntas frequentes sobre Keras

Learning Keras is likely right for you if you're pursuing a career in neural network framework and deep learning. Deep learning refers to methods of machine learning that are based on algorithms created in artificial neural networks that are modeled after the function and structure of the brain. You might be working toward a career or already in one as a software engineer, for example, or a data analyst, data engineer, research analyst, software developer, or bioinformatics analyst. If so, learning Keras may be right for you.‎
TensorFlow, Theano, and CNTK are three topics you can study that are closely related to Keras because Keras runs on top of these libraries. You can also study the human brain to form a deeper understanding of the premise of neural networks. Any topic related to machine learning may also be of interest to you, such as supervised, unsupervised, and reinforcement learning; inductive, deductive, and transductive learning; or multi-task, active, online, transfer, and ensemble learning. You can also learn more about deep learning and neural networks.‎
Places that hire people with a background in Keras include companies and organizations that employ data and software analysts and developers. For example, you'll find people with a background in Keras working for cloud computing platforms like Amazon Web Services, professional services networks like Deloitte, telecommunications companies such as Verizon, and cybersecurity companies like Carbon Black. Other notable employers of people with a background in Keras include JP Morgan Chase Bank, Microsoft, Facebook, Ford Motor Company, and Lockheed Martin.‎

Este conteúdo de Perguntas frequentes foi disponibilizado apenas para fins informativos. Os aprendizes são aconselhados a realizar pesquisas adicionais para garantir que os cursos e outras credenciais buscadas atendam a seus objetivos pessoais, profissionais e financeiros.