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Learner Reviews & Feedback for Convolutional Neural Networks in TensorFlow by deeplearning.ai

4.7
stars
2,801 ratings
420 reviews

About the Course

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 deeplearning.ai 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 deeplearning.ai 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....

Top reviews

JM

Sep 12, 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.

RB

Mar 15, 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..

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51 - 75 of 420 Reviews for Convolutional Neural Networks in TensorFlow

By Aditya W

Jan 22, 2020

I mainly to learn the various constructs to do various things in TensorFlow, and this course is very well constructed for it. It doesn't explain the actual mathematics though, and I don't blame it for that. It is just designed to help people learn the framework. Overall, a very satisfying experience.

By anujeet

Dec 14, 2019

This course in tensorflow specialization is a must recommended. It builds knowledge from beginners to advance very smoothly, You will be able to get a experience of how to begin coding for tensorflow also be able to understand its core layers, And learning from Laurence is always fun.

By Sanjay M

Aug 13, 2019

Very well thought through course for Convolution Neural Networks using Tensorflow, covering some of advances topics like transfer learning, callback and review convolution layers. I already had understanding about CNN and these topics. This course shared scenarios when it is used.

By Simon Z

Sep 10, 2019

Excellent. I learned after a couple of years working with neural networks new topics and implementations. I think it would be a good idea to include also here an exercise that gets graded at the end such that we take our time and can try out if we can make things work.

By Abhinav S T

Jun 22, 2019

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....!!!

By saket p

Jul 01, 2019

This is very well structured course for geeks who want to start learning machine leaning and implement different neural networks are hiking the technology world.

I personally appreciate the course material and instructor for the immense work.

By Nebojsa D

Aug 15, 2019

This lectures are givin a very nice advices for practical implementation of ConvNets. 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.

By arnaud k

Jun 25, 2019

The practical aspect of this course is addicting. I can't stop myself from wanted to try the next technique. maybe because i have seen most of these before but i going had made it clear what i was doing wrong in some of my "failed kaggle"

By Jafed E

Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

By Carlos V

Jul 07, 2019

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

By Chirag G

Mar 08, 2020

This specialization is really helpful. I had done other specializations and Machine Learning Course of Andrew Ng. But this course helped me to revise those topics as well as implement them in the real world.

By Sharan S M

Oct 22, 2019

After finishing this course, I was able to build a neural network that could identify different types of boats with around 94% accuracy. I used many techniques learned in this course like image augmentation.

By Vincent H

Nov 26, 2019

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

It is very practical and you learn a lot of features about the Tensor Flow framework that you can reuse for other issues.

By Md. A K A

Mar 28, 2020

Really enjoyed the course. Thanks deeplearning.ai team. Except "Inception" every topic was clearly practiced. For "Inception", I am eager to learn how to lock a model & how he trained weight can be saved.

By Ravi P B

Mar 15, 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..

By Javier M

Sep 12, 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.

By Muhammad H

May 24, 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.

By SANDANAKISHNAN S

Dec 09, 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

By Parab N S

Sep 14, 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.

By Mohd S K

Dec 14, 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.

By maryam m

Oct 26, 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.

By clement l r

Jan 22, 2020

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.

By Md. S R

Jul 31, 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.

By Erling J

Jul 12, 2019

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

By Sai K K K

Jan 09, 2020

Fundamentals Concepts and Coding related to CNN-Classifications, Augmentation, Dropouts, Regularization and Transfer learning are well presented. Enjoyed a lot during the course..