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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Réseau de compétences IBMÀ propos de ce cours
21 110 consultations récentes
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Niveau intermédiaire
Approx. 7Â heures pour terminer
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Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
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. 7Â heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Offert par
Réseau de compétences 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 de cours : ce que vous apprendrez dans ce cours
2Â heures pour terminer
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
2Â heures pour terminer
5 vidéos (Total 28 min), 2 lectures, 5 quiz
1Â heure 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.
1Â heure pour terminer
3 vidéos (Total 22 min)
2Â 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.
2Â heures pour terminer
4 vidéos (Total 22 min)
1Â heure 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.
1Â heure pour terminer
2 vidéos (Total 10 min)
Avis
- 5 stars60,81Â %
- 4 stars24,17Â %
- 3 stars9,30Â %
- 2 stars2,85Â %
- 1 star2,85Â %
Meilleurs avis pour BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
par RB18 mai 2020
Excellent course to get started with tensorflow and deep learning.Really enjoyed the course.
par PC31 mars 2020
The coding part was hard to understand. If that part could also be covered in videos as a tutorial.
par SA3 févr. 2020
It helped me to understand how TensorFlow can be used to build the neural networks
par MO18 juil. 2020
I would like to thank the lecturer for this fruitful course. I have enjoyed it, Thanks a lot!
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