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.
Ce cours fait partie de la Spécialisation TensorFlow in Practice
Convolutional Neural Networks in TensorFlow
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À propos de ce cours
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Résultats de carrière des étudiants
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10%
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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
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. 8 heures pour terminer
Recommandé : 4 weeks of study, 4-5 hours/week
Anglais
Sous-titres : Anglais, Russe, Japonais
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 exercices 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 exercices 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 exercices 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 exercices pour s'entraîner
Week 4 Quiz30 min
Avis
4.7
Meilleurs avis pour CONVOLUTIONAL NEURAL NETWORKS IN TENSORFLOW
par RBMar 14, 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 JMSep 11, 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 SSDec 8, 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 PSSep 13, 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.
par MHMay 23, 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 CMApr 30, 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.
par HROct 1, 2019
The course is really nice. But would be better if the convolutional layers were a bit more detailed. It was a bit difficult for me to understand all the parameters e.g: input/output filter size.
par RCMay 14, 2019
Excellent material superbly presented by world-class experts.\n\nSorry if this sounds sycophantic, but this series contains some of the best courses I've encountered in50+ years of learning.
par MKDec 13, 2019
a very nice course on ConvNets. Image journey through convnets and logic behind using specific type of layers. you can very wellunderstand the keras structure to build convnets through this.
par MMOct 25, 2019
Well structures course. No matter your level of expertise you can learn from this course and implement models more professionally and improve answers accuracy using this course techniques.
par MRJul 30, 2019
This course is so best for the new practitioners! Because when you are learning deep learning theoritically, this course will help you at such level to make you practice these highly.
par SKJan 8, 2020
Fundamentals Concepts and Coding related to CNN-Classifications, Augmentation, Dropouts, Regularization and Transfer learning are well presented. Enjoyed a lot during the course..
par RGJan 16, 2020
I think this was a good course but the standard of exercises and quizzes was too easy. More conceptual questions especially in quizzes would help in understanding the topic better
par EGMay 10, 2019
It's great to learn about data augmentation techniques and how to implement this. This is a great complement for the deeplearning.ai's course on Convolutional Neural Networks.
par RPFeb 6, 2020
this is a great course,teaching is outstanding. this course has covered all basics needed for deep learning and CNN.this course also give us good understanding of tensorflow.
par GSSep 26, 2019
the last exercise needed a big upload, made it imposible (for me) to do. This was a problem not related to the subject, should use data downloadable directly from internet.
par MRJul 6, 2019
The material was well presented and easy to follow. The instructor skillfully described the functionality in the code... to reinforce to training objectives for the lesson.
par SDMay 19, 2019
Very brief and precisely taught implementing various techniques in Convolution Neural Networks by using Tensorflow. Quite time saving and a good one to boost your skills.
par UDNov 17, 2019
The course is straight to the point. The instructor provides his code structure too, which can be a great reference to people are keen on implementing the TF right away.
par AKJun 13, 2019
Hands on practice and I love the videos after each lab that explains all we encountered during the lab. I highly recommend this course to anyone interested in CNNs
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deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
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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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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