If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
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Spécialisation TensorFlow in Practicedeeplearning.aiÀ propos de ce cours
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10%
a obtenu une augmentation de salaire ou une promotion
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Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 4 dans le
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. 26 heures pour terminer
Anglais
Sous-titres : Anglais, Russe, Japonais
Ce que vous allez apprendre
Handle real-world image data
Plot loss and accuracy
Explore strategies to prevent overfitting, including augmentation and dropout
Learn transfer learning and how learned features can be extracted from models
Compétences que vous acquerrez
Inductive TransferAugmentationDropoutsMachine LearningTensorflow
Résultats de carrière des étudiants
13%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
10%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 4 dans le
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. 26 heures pour terminer
Anglais
Sous-titres : Anglais, Russe, Japonais
Offert par
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
Programme du cours : ce que vous apprendrez dans ce cours
7 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!
7 heures pour terminer
8 vidéos (Total 18 min), 5 lectures, 3 quiz
8 vidéos
A conversation with Andrew Ng1 min
Training with the cats vs. dogs dataset2 min
Working through the notebook4 min
Fixing through cropping49s
Visualizing the effect of the convolutions1 min
Looking at accuracy and loss1 min
Week 1 Wrap up33s
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
7 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 heures pour terminer
7 vidéos (Total 14 min), 6 lectures, 3 quiz
7 vidéos
Introducing augmentation2 min
Coding augmentation with ImageDataGenerator3 min
Demonstrating overfitting in cats vs. dogs1 min
Adding augmentation to cats vs. dogs1 min
Exploring augmentation with horses vs. humans1 min
Week 2 Wrap up37s
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
7 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 heures pour terminer
7 vidéos (Total 14 min), 5 lectures, 3 quiz
7 vidéos
Understanding transfer learning: the concepts2 min
Coding transfer learning from the inception mode1 min
Coding your own model with transferred features2 min
Exploring dropouts1 min
Exploring Transfer Learning with Inception1 min
Week 3 Wrap up36s
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
7 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!
7 heures pour terminer
6 vidéos (Total 12 min), 5 lectures, 3 quiz
6 vidéos
Moving from binary to multi-class classification44s
Explore multi-class with Rock Paper Scissors dataset2 min
Train a classifier with Rock Paper Scissors1 min
Test the Rock Paper Scissors classifier2 min
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
Wrap up10 min
1 exercice pour s'entraîner
Week 4 Quiz30 min
Avis
Meilleurs avis pour CONVOLUTIONAL NEURAL NETWORKS IN TENSORFLOW
par RB14 mars 2020
Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..
par JM11 sept. 2019
great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.
par SS8 déc. 2019
Very clear explanation on the concepts at the higher level and practical application of it is discussed, demonstrated and also the exercises are of the same way. You will just love learning this way
par PS13 sept. 2019
An excellent course by Laurence Moroney on explaining how ConvNets are prepared using Tensorflow. A really good strategy to have the programming exercises on Google Colab to speed up the processing.
À propos du 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.
Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
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