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 Nombre de Développeur TensorFlow DeepLearning.AI Certificat Professionnel
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Cours 2 sur 4 dans le
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. 27 heures pour terminer
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Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol, 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
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.
Cours 2 sur 4 dans le
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. 27 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol, Japonais
Offert par
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programme du cours : ce que vous apprendrez dans ce cours
8 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!
8 heures pour terminer
8 vidéos (Total 18 min), 5 lectures, 4 quiz
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 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 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
Avis
Meilleurs avis pour CONVOLUTIONAL NEURAL NETWORKS IN TENSORFLOW
par MS12 nov. 2020
A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!
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 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 KA13 avr. 2020
Nice course. Even though I have previously done some projects using CNN and multi-class classification still this course let me to have an insight to how these APIs work. Keep Up The Good Work!!!!!!
À propos du Nombre de Développeur TensorFlow DeepLearning.AI Certificat Professionnel
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.
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