Comentários e feedback de alunos de Convolutional Neural Networks in TensorFlow da instituição deeplearning.ai

The good explanation about how to implement good design of machine learning to avoid overfitting etc. Very useful resources (dropout, augmentation, and transfer learning).

Very hands-on experience learning through this course. But only with a little of content each week comparing to the deep learning specialization by the same organization.

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.

Awesome Course. This course has enriched my concepts in Convolutional Neural Networks. This course also motivated me to do more courses on Coursera. Thank you Coursera!

Thanks to Instructor for this wonderful course.I learned various techniques including CNN,Data Augmentation and Transfer Learning along with Multi Class Classification

Deep, comprehensive and step-by-step introduction to CNN. Explanations are easily formulated and can be understood by non-expert users which seek to learn about CNNs.

Good Stuff! Learned about data augmentation with Tensorflow Data Generators. I also learned about transfer learning and how to retrain part of a pre-existing model.

The course met the expectation of having hands on assignments as well as explanation of the codes and obviously course instructor Mr. Laurance made the learning fun.

A great exploration of how CNN can be used with large datasets and how to deal with some of the issues that might arise when dealing with large and complex datasets

Excellent classes and materials, it make me feel image classification is so simple even though I still struggled in the assignment. Need to study and practice more.

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..

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

Great course. Laurence is a great instructor. This course gives you more confidence to deploy your own convolutional neural networks and machine learning models.

Wonderful Course!! stick to the basics slowly introducing methods to improve accuracy metric and in parallel taking care of overfitting. I thoroughly enjoyed it

I learned A LOT in this course just by using Tensor Flow to perform state-of-the-art image processing. My special thanks and gratitude to Laurence and Andrew.

I have learned Transfer Learning, Image augmentation. The Course is very interactive.

I hope will use this knowledge to develop meaningful work in my career.

This course has been very helpful in learning the implementation of CNN. I am very thankful to the course coordinator and Coursera for this awesome course.

Felt great working with computer vision on handling different types of obstacles with some smart solutions. Great help to boost my dl experience.

Thank You!

Excellent course. Enabled me to quickly get a grasp of this topic so I could go on to further learning and application. Perfectly designed course for this.

A brilliant hands-on course that really gets the student into ML with tensorflow / keras ... Also lots of kudos to our teacher Laurence for a great course.

Very practical and useful course. It really helps to build a few models yourself instead of just reading about it to fully understand potential of CNNs.

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

Very Detailed explanation on CNN! Transfer Learning and Augmentation are really good materials! Everything is well-structured and easy to work on with!

This class is awesome, unfortunately the notebooks in week 3 and 4 (if I'm not mistaken) have some errors that we need to cross the rules and debug it

Excellent course. Convolutions are great way of making machine learning effective

I would like to thank Laurence Sir

and a Special thanks to Andrew Sir