À propos de ce cours
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Niveau intermédiaire

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

Approx. 7 heures pour terminer

Recommandé : 4 weeks of study, 4-5 hours/week...

Anglais

Sous-titres : Anglais

Ce que vous allez apprendre

  • Check

    Handle real-world image data

  • Check

    Plot loss and accuracy

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    Explore strategies to prevent overfitting, including augmentation and dropout

  • Check

    Learn transfer learning and how learned features can be extracted from models

Compétences que vous acquerrez

Inductive TransferAugmentationDropoutsMachine LearningTensorflow

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

Approx. 7 heures pour terminer

Recommandé : 4 weeks of study, 4-5 hours/week...

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
4 heures pour terminer

Exploring a Larger Dataset

In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, an you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification! In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!

...
8 vidéos (Total 18 min), 5 lectures, 3 quiz
8 vidéos
Working through the notebook4 min
Fixing through cropping49s
Visualizing the effect of the convolutions1 min
Looking at accuracy and loss1 min
Week 1 Outro33s
5 lectures
Before you Begin: TensorFlow 2.0 and this Course10 min
The cats vs dogs dataset10 min
Looking at the notebook10 min
What you'll see next10 min
What have we seen so far?10 min
1 exercice pour s'entraîner
Week 1 Quiz30 min
Semaine
2
4 heures pour terminer

Augmentation: A technique to avoid overfitting

You've heard the term overfitting a number of times to this point. Overfitting is simply the concept of being over specialized in training -- namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers. You'll learn all about that this week!

...
7 vidéos (Total 14 min), 6 lectures, 3 quiz
7 vidéos
Demonstrating overfitting in cats vs. dogs1 min
Adding augmentation to cats vs. dogs1 min
Exploring augmentation with horses vs. humans1 min
Week 2 Outro37s
6 lectures
Image Augmentation10 min
Start Coding...10 min
Looking at the notebook10 min
The impact of augmentation on Cats vs. Dogs10 min
Try it for yourself!10 min
What have we seen so far?10 min
1 exercice pour s'entraîner
Week 2 Quiz30 min
Semaine
3
4 heures pour terminer

Transfer Learning

Building models for yourself is great, and can be very powerful. But, as you've seen, you can be limited by the data you have on hand. Not everybody has access to massive datasets or the compute power that's needed to train them effectively. Transfer learning can help solve this -- where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario. This is Transfer learning, and you'll look into that this week!

...
7 vidéos (Total 14 min), 5 lectures, 3 quiz
7 vidéos
Coding your own model with transferred features2 min
Exploring dropouts1 min
Exploring Transfer Learning with Inception1 min
Week 3 Outro36s
5 lectures
Start coding!10 min
Adding your DNN10 min
Using dropouts!10 min
Applying Transfer Learning to Cats v Dogs10 min
What have we seen so far?10 min
1 exercice pour s'entraîner
Week 3 Quiz30 min
Semaine
4
4 heures pour terminer

Multiclass Classifications

You've come a long way, Congratulations! One more thing to do before we move off of ConvNets to the next module, and that's to go beyond binary classification. Each of the examples you've done so far involved classifying one thing or another -- horse or human, cat or dog. When moving beyond binary into Categorical classification there are some coding considerations you need to take into account. You'll look at them this week!

...
6 vidéos (Total 12 min), 5 lectures, 3 quiz
6 vidéos
Train a classifier with Rock Paper Scissors1 min
Test the Rock Paper Scissors classifier2 min
Outro, A conversation with Andrew Ng1 min
5 lectures
Introducing the Rock-Paper-Scissors dataset10 min
Check out the code!10 min
Try testing the classifier10 min
What have we seen so far?10 min
Outro10 min
1 exercice pour s'entraîner
Week 4 Quiz30 min
4.8
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Principaux examens pour Convolutional Neural Networks in TensorFlow

par MHMay 24th 2019

A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.

par CMMay 1st 2019

A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.

Enseignant

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Laurence Moroney

AI Advocate
Google Brain

À propos de deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

À propos de la Spécialisation TensorFlow in Practice

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. Courses 1-3 are available now, with Course 4 launching in July....
TensorFlow in Practice

Foire Aux Questions

  • 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.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, 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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