The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
oferecido por
IBM Skills NetworkInformações sobre o curso
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Prazos flexÃveis
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel intermediário
Aprox. 7 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel intermediário
Aprox. 7 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
IBM Skills Network
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Programa - O que você aprenderá com este curso
2 horas para concluir
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
2 horas para concluir
5 vÃdeos (Total 28 mÃn.), 2 leituras, 5 testes
1 hora para concluir
Supervised Learning Models
In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.
1 hora para concluir
3 vÃdeos (Total 22 mÃn.)
2 horas para concluir
Supervised Learning Models (Cont'd)
In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.
2 horas para concluir
4 vÃdeos (Total 22 mÃn.)
1 hora para concluir
Unsupervised Deep Learning Models
In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.
1 hora para concluir
2 vÃdeos (Total 10 mÃn.)
Avaliações
- 5 stars60,81%
- 4 stars24,20%
- 3 stars9,22%
- 2 stars2,87%
- 1 star2,87%
Principais avaliações do BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
por LD5 de nov de 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
por MT13 de nov de 2020
It is very good to explain concept of Deep Learning by Example , it is so clear, and better understand
por VV4 de mar de 2021
This course is the best out of all courses in the specialization, the pace of the speaker was perfect.
por XH29 de jun de 2020
The course concepts are not in-depth enough, and the server for Jupyter notebook running is way too slow...
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