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.
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IBMÀ propos de ce cours
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Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
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Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Approx. 13 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
5 heures pour terminer
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
5 heures pour terminer
5 vidéos (Total 28 min), 1 lecture, 5 quiz
2 heures pour terminer
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.
2 heures pour terminer
3 vidéos (Total 22 min)
3 heures pour terminer
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 heures pour terminer
4 vidéos (Total 22 min)
2 heures pour terminer
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.
2 heures pour terminer
2 vidéos (Total 10 min)
Avis
Meilleurs avis pour BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
par RR25 juil. 2020
Nice course to introduce you to more advanced neural network algorithms, I wish the evaluations were more challenging and based on practical exercises... there is no final assignment either.
par HJ17 mai 2020
It would have been nice if the video tutorials would explain the code section as well, and if there would have been some in-depth teaching of the code part. But this course did benefit.
par RC14 mai 2020
Good an simple videos to understand the concept. The notebooks are very detailed and give a second layer of knowledge with practical example
par ZR2 juil. 2020
Deep Learning made me feel that there is a way to build models and classify data so easily and in a skillful way. Amazing course!
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