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.
Этот курс входит в специализацию ''Специализация TensorFlow in Practice'
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Специализация TensorFlow in Practicedeeplearning.aiОб этом курсе
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Карьерные результаты учащихся
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получил значимые преимущества в карьере благодаря этому курсу
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Курс 2 из 4 в программе
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Промежуточный уровень
Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.
Прибл. 26 часов на выполнение
Английский
Субтитры: Английский, Русский, Японский
Чему вы научитесь
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
Приобретаемые навыки
Inductive TransferAugmentationDropoutsMachine LearningTensorflow
Карьерные результаты учащихся
13%
получил значимые преимущества в карьере благодаря этому курсу
10%
стал больше зарабатывать или получил повышение
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 2 из 4 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Промежуточный уровень
Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.
Прибл. 26 часов на выполнение
Английский
Субтитры: Английский, Русский, Японский
от партнера
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.
Программа курса: что вы изучите
7 ч. на завершение
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 ч. на завершение
8 видео ((всего 18 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
8 видео
A conversation with Andrew Ng1мин
Training with the cats vs. dogs dataset2мин
Working through the notebook4мин
Fixing through cropping49
Visualizing the effect of the convolutions1мин
Looking at accuracy and loss1мин
Week 1 Wrap up33
5 материалов для самостоятельного изучения
Before you Begin: TensorFlow 2.0 and this Course10мин
The cats vs dogs dataset10мин
Looking at the notebook10мин
What you'll see next10мин
What have we seen so far?10мин
1 практическое упражнение
Week 1 Quiz30мин
7 ч. на завершение
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 ч. на завершение
7 видео ((всего 14 мин.)), 6 материалов для самостоятельного изучения, 3 тестов
7 видео
Introducing augmentation2мин
Coding augmentation with ImageDataGenerator3мин
Demonstrating overfitting in cats vs. dogs1мин
Adding augmentation to cats vs. dogs1мин
Exploring augmentation with horses vs. humans1мин
Week 2 Wrap up37
6 материалов для самостоятельного изучения
Image Augmentation10мин
Start Coding...10мин
Looking at the notebook10мин
The impact of augmentation on Cats vs. Dogs10мин
Try it for yourself!10мин
What have we seen so far?10мин
1 практическое упражнение
Week 2 Quiz30мин
7 ч. на завершение
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 ч. на завершение
7 видео ((всего 14 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
7 видео
Understanding transfer learning: the concepts2мин
Coding transfer learning from the inception mode1мин
Coding your own model with transferred features2мин
Exploring dropouts1мин
Exploring Transfer Learning with Inception1мин
Week 3 Wrap up36
5 материалов для самостоятельного изучения
Start coding!10мин
Adding your DNN10мин
Using dropouts!10мин
Applying Transfer Learning to Cats v Dogs10мин
What have we seen so far?10мин
1 практическое упражнение
Week 3 Quiz30мин
7 ч. на завершение
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 ч. на завершение
6 видео ((всего 12 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
6 видео
Moving from binary to multi-class classification44
Explore multi-class with Rock Paper Scissors dataset2мин
Train a classifier with Rock Paper Scissors1мин
Test the Rock Paper Scissors classifier2мин
A conversation with Andrew Ng1мин
5 материалов для самостоятельного изучения
Introducing the Rock-Paper-Scissors dataset10мин
Check out the code!10мин
Try testing the classifier10мин
What have we seen so far?10мин
Wrap up10мин
1 практическое упражнение
Week 4 Quiz30мин
Рецензии
Лучшие отзывы о курсе CONVOLUTIONAL NEURAL NETWORKS IN TENSORFLOW
от партнера RB14 мар. 2020 г.
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..
от партнера JM11 сент. 2019 г.
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.
от партнера SS8 дек. 2019 г.
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
от партнера MH23 мая 2019 г.
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.
Специализация TensorFlow in Practice: общие сведения
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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When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
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Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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