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Convolutional Neural Networks in TensorFlow

DeepLearning.AI TensorFlow Developer,

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. In Course 2 of the TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

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Rating 4.4 based on 355 ratings
Length 5 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Jun 26 (35 weeks ago)
Cost $49
From via Coursera
Instructor Laurence Moroney
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Software Development Machine Learning

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What people are saying

transfer learning

I especially enjoyed the parts about image augmentation with the use of ImageDataGenerator and the transfer learning addition wit huse of the Inception network.

Short videos and a small number of examples, for example, Transfer learning could be more in-depth.

The week 1 is a bit casual but where as the remaining one's are just awesome learnt a lot like how to implement a model without overfiting and learnt how to implement transfer learning and multi-class classification problem, really worthy taking up this course....!!!

Didn't provide a real understanding for transfer learning The Way this has been driven, I never felt disconnected.

Nice course Fundamentals Concepts and Coding related to CNN-Classifications, Augmentation, Dropouts, Regularization and Transfer learning are well presented.

It's a lot of fun building a ConvNet using my dog's pictures and reduce the overfitness through augmentation, dropouts and transfer learning!

This Course Covers All The Topics Like Data Augmentation, Transfer Learning, Drop-Out, And Multi-Class-Classification Problem.

a brief intro to transfer learning First of all, the course was amazing!

Transfer learning is also easy to do if there is Keras model there already.

You can see how convolution works, image processing, transfer learning and so on.

Complex topic such as transfer learning, and image augmentation have been beautifully covered, along with easy to follow implementations.

Great course digging into convnets, augmentation, transfer learning and multiclass classification Very well designed course on TensorFlow.

I found the transfer learning lessons a bit unclear and I struggle generalizing this to other cases.

Very good course that teaches you basics of convolutions, augmentation, transfer learning.

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deep learning

I had already done the Deep Learning specialization so I recommend that as a great complement for the theory part.

But only with a little of content each week comparing to the deep learning specialization by the same organization.

That makes it really convenient to learn and experiment with Machine Learning and Deep Learning.

I did take the first course in the Deep Learning Specialization early last year, but didn't get a chance to do this until now.

this course has covered all basics needed for deep learning and CNN.this course also give us good understanding of tensorflow.

Exceptional If you have taken Andrew's courses in ML or deep learning, you will be disappointed.

The instructor does an OK job of showing you how to use TF, but he doesn't always explain things very clearly, and doesn't always have an accurate understanding of how ML or deep learning works.

It is better to discuss the code in the video before moving to the notebook not the opposite.Thank you I would like to see examples with videos, yolo, etc I am grateful for this course This course is a great addition to the deep learning courses by Prof. Andrew Ng.

I got to learn the fundamentals of deep learning from Andrew Ng's courses and learned to programme from here.

IT is a great course about Deep Learning and above all, how to code it with Python.

Amazing course helps in gathering all the required skills needed for deep learning Very effective notebooks and the suggested resources along the way were helpful.

This is a great course but I will advise taking Andrew's "Deep Learning Specialization" before this.

I love topics like Computer Vision, Deep Learning, and this is an excellent step to make great things about that.

Great way to get started with deep learning and perform transfer learning.

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neural networks

And It is great pleasure to thank Coursera platform for providing me Financial aid to take up these course.ThanksRishiganesh.V this course is very useful for beginners The course is marvelous explain and with clear, concise & straight forward concepts alike the practice project.Take your time to understand the concepts, so you can move on.I'll recommend to watch the specialization of Neural Network from Andrew Ng, to deeply understand the "magic" ( linear regression, matrices, derivatives) of Neural Networks.

Excellent course, in particular, all explanations to work with the Image Augmentation libraries, I enjoined the transfer learning part, highly recommended for anyone looking to improve their knowledge of Convolutional Neural Networks Could have dived more into the details and inner workings of Convolutional layers but overall awesome course.

This course worked as a great reference for my project on Neural Networks.

this class is not difficult ,it is suitable for benginers of DL Very brief and precisely taught implementing various techniques in Convolution Neural Networks by using Tensorflow.

This is a great complement for the's course on Convolutional Neural Networks.

This course explores the topics of the first course for image classification with neural networks.

After ending this course, I believe I would enrolled on the other specialization, to gain a better mathematical understanding of convolutional neural networks but I'm pretty happy to learn the practical stuff, this make possible a lot of projects!

Practice with various data sets, and learn the tools to use for convolutional neural networks with tensor flow More exercises should be available for students to practice and test their skills.

Great intro to CNN I am already familiar with machine learing and convolutional neural networks, and before starting using the TensorFlow framework I wanted to develop my own know-how in order to really have control and knowledge on what am I doing.

I learned after a couple of years working with neural networks new topics and implementations.

Great course to learn about Convolutional Neural Networks in Keras The best!

Though in tandem with the deep learning specialization gives a good view on convolutional neural networks as well as its implementation in tensorflow.

Very well thought through course for Convolution Neural Networks using Tensorflow, covering some of advances topics like transfer learning, callback and review convolution layers.

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andrew ng

You will not gain any insights into the challenges that someone might face using CNNs on Tensorflow in a real-world scenario.This course does not compare to the kind of insights that you learn from the other courses taught by Andrew Ng.There are no graded programming assignments to validate what you have learned.

Many thanks to Andrew Ng and team for the great balance of theoretical background, practical references and hands-on programming exercises.

BTW, I really like Andrew NG's courses, but this one really disappointed me.

The homework assignments should be required and more challenging What could improve it: Not enough depth in the practicals if you have already done Andrew Ng's course on Conv nets.

A well taught course with interesting coursework and projects a little bit too easy compared to Andrew Ng's deep learning course.

combining it with prof.Andrew Ng's lecture exercises in this course will allow you much more practi implementation of knowledge you have acquired before.

If it has been purposely done to keep the course open to even newbies in Machine Learning, then there should have been a course focussed for those who have done Andrew Ng's ML/DL specialization.

Excellent course to take after completing Deep Learning Specialization need to watch Andrew Ng's course on deep learning before watching this one The course seems to be getting more loose than the first course.

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too easy

No se puede hacer sin haber hecho previamente el otro curso Too easy.

I think this was a good course but the standard of exercises and quizzes was too easy.

too easy!

Too easy.

The course was fine sometimes I feel too easy.

It's too easy for an intermediate machine learning leaner, and it's little about naive TensorFlow.

Useful but too easy The content could have been covered in 15 min.

The Course is too easy but it was fun and to the point It was good, but similar to other learners I feel a little light in content.

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convolutional neural

Great introductory course to learn the application of TensorFlow with Keras in the field of Convolutional Neural Network.

Most of the question in the discussion, there is not somehow replies to it Wonderful Course on Convolutional Neural Network Using TF.

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easy to understand

Very good slides which are well formulated and easy to understand This is the second course of the specialization and still I feel like I haven't been introduced to anything beyond the free tutorials available on tensorflow website.

It's a perfect course to learn TensorFlow for CNN, and it is extremely easy to understand.

I found it great for the following reasons:- Laurence Moroney (Instructor) was very professional and clear while delivering the knowledge- The introductions by Andrew NG were really nice- Easy to understand codes and understanding of thr underlying principles- Varied topics such as CNN, NLP & Time Series- Very insightful by providing expert opinions about different ways of model optimizationI really enjoyed the course and I thank the instructor for the same :) I liked a lot, was really straid to the head on the basics to practice CNN.

It's pretty clear and notebooks is also very easy to understand.

Best Explanation easy to understand The course is well structured and explained from trainer I feel that I have more information and get knowledge in tensorflow practices Thanks for this Amazing course

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laurence moroney

It is really an amazing course, My heartfelt thanks to Mr.Laurence Moroney, for his great teaching and Mr. Andrew Ng for giving these great platform.

Thanks for the lectures by Laurence Moroney.

Specially, a huge thank to Laurence Moroney for this wonderful course series.

An excellent course by Laurence Moroney on explaining how ConvNets are prepared using Tensorflow.

Laurence Moroney presents another superb primer on the mechanics of tensor flow.

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using tensorflow

It's a perfect fit for beginners or those who want to have a practical review before starting using Tensorflow 2.0 with keras implemetations.

So easy but okey Exellent tutorial for using Tensorflow and convolutional networks.

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more advanced

I would like to have a little more advanced topics A very nice course to finish understand well convolutio, data augmentation, overfitting in neural network, as well as transfer learning and making classifier for binary and multiclass.

needs to be more advanced too basic not much of insights or details, and it's too easy!

A more advanced course would be highly appreciated.

Very interesting course that starts to explore some more advanced topics of machine learning, in particular, Image Augmentation and Transfer Learning.

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Rating 4.4 based on 355 ratings
Length 5 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Jun 26 (35 weeks ago)
Cost $49
From via Coursera
Instructor Laurence Moroney
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Software Development Machine Learning

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