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.
Ce cours fait partie de la Nombre de IBM AI Engineering Certificat Professionnel
Building Deep Learning Models with TensorFlow
Offert par
À propos de ce cours
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Niveau intermédiaire
Approx. 8 heures pour terminer
Recommandé : 4-5 hours/week
Anglais
Sous-titres : Anglais
Programme du cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
4 heures pour terminer
4 vidéos (Total 24 min), 1 lecture, 4 quiz
4 vidéos
Welcome2 min
Introduction to TensorFlow7 min
Introduction to Deep Learning2 min
Deep Neural Networks11 min
1 lecture
Syllabus10 min
1 exercice pour s'entraîner
Deep Neural Networks and TensorFlow30 min
3 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.
3 heures pour terminer
3 vidéos (Total 22 min)
3 vidéos
Convolutional Neural Networks (CNNs) for Classification4 min
Convolutional Neural Networks (CNNs) Architecture13 min
1 exercice pour s'entraîner
Convolutional Neural Networks30 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)
4 vidéos
Recurrent Neural Networks (RNNs)5 min
The Long Short Term Memory (LSTM) Model5 min
Language Modelling7 min
1 exercice pour s'entraîner
Recurrent Neural Networks30 min
3 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.
3 heures pour terminer
2 vidéos (Total 10 min)
1 exercice pour s'entraîner
Restricted Boltzmann Machines30 min
Avis
4.3
Meilleurs avis pour BUILDING DEEP LEARNING MODELS WITH TENSORFLOW
par 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.
par MKNov 21, 2019
Good content. A bit too fast on some complex concepts and missing audio for the last lecture but great lecturer.
par LDNov 5, 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
par SAFeb 3, 2020
It helped me to understand how TensorFlow can be used to build the neural networks
par NKDec 30, 2019
Week 5 lecture video no audio\n\nLab is not update for tensorflow 2
par JAJan 22, 2020
Teaches more on Deep Learning models but less in TensorFlow
par TSMar 1, 2020
Thank a lot for this course! It quick and really useful.
par TZMar 23, 2020
Very informative, could use some more room for practice.
Offert par
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.
À propos du Nombre de IBM AI Engineering Certificat Professionnel
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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à ce Certificat ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours du Certificat, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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