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.
Este curso forma parte de Certificado profesional de IBM AI Engineering
Building Deep Learning Models with TensorFlow
ofrecido por
Acerca de este Curso
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Certificado para compartir
Obtén un certificado al finalizar
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Curso 5 de 6 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 5 de 6 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Programa - Qué aprenderás en este curso
4 horas para completar
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
4 horas para completar
4 videos (Total 24 minutos), 1 lectura, 4 cuestionarios
1 lectura
Syllabus10m
1 ejercicio de práctica
Deep Neural Networks and TensorFlow30m
3 horas 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.
3 horas para completar
3 videos (Total 22 minutos)
3 videos
Convolutional Neural Networks (CNNs) for Classification4m
Convolutional Neural Networks (CNNs) Architecture13m
1 ejercicio de práctica
Convolutional Neural Networks30m
3 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.
3 horas para completar
4 videos (Total 22 minutos)
4 videos
Recurrent Neural Networks (RNNs)5m
The Long Short Term Memory (LSTM) Model5m
Language Modelling7m
1 ejercicio de práctica
Recurrent Neural Networks30m
3 horas 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.
3 horas para completar
2 videos (Total 10 minutos)
1 ejercicio de práctica
Restricted Boltzmann Machines30m
Revisiones
Principales revisiones sobre BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
por PDJan 4, 2020
Very clear explanation and well organized course. I give 4 stars because videos of Week 5 are missing the audio and subtitles.
por MKNov 21, 2019
Good content. A bit too fast on some complex concepts and missing audio for the last lecture but great lecturer.
por PCMar 31, 2020
The coding part was hard to understand. If that part could also be covered in videos as a tutorial.
por LDNov 5, 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
por SAFeb 3, 2020
It helped me to understand how TensorFlow can be used to build the neural networks
por NKDec 30, 2019
Week 5 lecture video no audio\n\nLab is not update for tensorflow 2
por JAJan 22, 2020
Teaches more on Deep Learning models but less in TensorFlow
por TSMar 1, 2020
Thank a lot for this course! It quick and really useful.
por TZMar 23, 2020
Very informative, could use some more room for practice.
Acerca de Certificado profesional de IBM AI Engineering
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Certificado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos del Certificado y te darán un certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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