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.
ofrecido por
Habilidades en redes de IBMAcerca de este Curso
21.413 vistas recientes
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 7 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 7 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Habilidades en redes de IBM
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 - Qué aprenderás en este curso
2 horas para completar
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
2 horas para completar
5 videos (Total 28 minutos), 2 lecturas, 5 cuestionarios
1 hora para completar
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 completar
3 videos (Total 22 minutos)
2 horas para completar
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 completar
4 videos (Total 22 minutos)
1 hora para completar
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 completar
2 videos (Total 10 minutos)
Reseñas
- 5 stars60,90Â %
- 4 stars24,21Â %
- 3 stars9,17Â %
- 2 stars2,85Â %
- 1 star2,85Â %
Principales reseñas sobre BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
por MB25 de mar. de 2022
The detail of prsenetation is awsome and make learning interesting. Thank you Corseara, Thank you IBM
por SM4 de jun. de 2022
Was a really fun course, but the final assignments were very lengthy.
por LY8 de feb. de 2020
Simple and easy-to-follow course for a hard-core python package
por LD5 de nov. de 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
¿Qué recibiré si me suscribo a este Certificado?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
