Chevron Left
Voltar para Convolutional Neural Networks in TensorFlow

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

4.7
1,282 classificações
191 avaliações

Sobre o curso

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....

Melhores avaliações

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.

PS

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.

Filtrar por:

51 — 75 de {totalReviews} Avaliações para o Convolutional Neural Networks in TensorFlow

por Gogul I

Jun 22, 2019

Amazing course to learn concepts such as Dropouts, Augmentation and Transfer Learning to solve real world image problems.

por 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....!!!

por Adam

Jun 20, 2019

Clearly explain for CNN

por Leo

Jun 25, 2019

Great course for Computer Vision problems!

por SAHARSH A

Jun 26, 2019

compel the submission of ungraded tests

por Yuanzhe L

Jun 25, 2019

Great course!

por 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"

por Nazarii N

Jun 27, 2019

Easy and clear

por Pachi C

Jun 26, 2019

Great course and fantastic professors (Laurence and Andrew)

por Eagle Y

Jun 27, 2019

I really love this course! It is a lot of fun and I highly recommend this to other people.

por Abhishek P

Jun 10, 2019

Awesome Course!

I was quite familiar with CNNs before,but I gained few tricks and trips from great instructors!

I would highly recommend this course!

por Oleksandr M

Jun 11, 2019

It's very clear and useful! Thank you! :)

por Sergei A

Jul 02, 2019

All is clear and simple.

por 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.

por Aniruddha S

Jul 03, 2019

Nice Course but little tricky when making directories.

Learned so much.

por ravikiran

Jul 04, 2019

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

por Manuel R

Jul 07, 2019

The material was well presented and easy to follow. The instructor skillfully described the functionality in the code... to reinforce to training objectives for the lesson.

por 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

por Vidit G

Jul 07, 2019

This course helped me understand the concept behind CNN's and the I was able to implement them in the given assignments. Thanks Laurence Sir!

por 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

por Mats E

Jul 08, 2019

Very good high-level introduction course.

por Santosh P Y

Jul 10, 2019

Great opportunity to experiment and learn through the exercises!

por Scott C

Jul 10, 2019

Great for people who want to not delve too deep into theory and learn the latest tools to get going quickly. I had already done the Deep Learning specialization so I recommend that as a great complement for the theory part. I learned everything I needed to get going with a practical application in this course. My only complaint is that I felt that the quizzes were poorly designed - most questions emphasized whether you remembered a specific API's argument name, or some questions were a bit ambiguous. Otherwise, highly highly recommend the course.

por Magomet A

Jul 01, 2019

Great course! Learned a lot about CNNs

por 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.