18 апр. 2020 г.
Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course
5 дек. 2019 г.
I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse
автор: Saimur R A•
2 авг. 2020 г.
This course trully go deeper into the deep learning and I learned a lot of things which improve my concept about NN network. Andrew gave an excellent lesson like the first course and simplify everything and the quote from Andrew "if you dont understand anythink don't worry too much about it" really make sense and over the time the concept will get clearer.
автор: J A•
8 сент. 2017 г.
Very clear, straight to the point, explanations with very well guided programming assignments in Python to hammer the concepts. A lot of knowledge and experience condensed in just a few hours and materials. I recommend previous exposure to Python and Machine Learning to make the most of this course (Ng's Coursera's course provides a very solid foundation)
автор: Amaranath B•
13 окт. 2019 г.
This is an amazing course , the way they had designed the transition from numpy to tensorflow was amazing. The the concepts of gradient descent with momentum to adam optimizer was great coming from your previous course , I can't express how much this has grounded my understanding. I'm pushing myself to complete the specialization. Thanks a lot everyone !
автор: Naveen K•
25 сент. 2017 г.
The course if very structured. Can't think of any improvement in course structure. Will like to thank Andrew Sir for this great effort.
As an improvement it would be great if people can be encouraged to solve problems on different dataset on internet such as kaggle. Such sources with other help can be provided as work to do after the completion of Kaggle.
автор: Daniel V I•
9 февр. 2020 г.
A fine continuing of the previous course in this specialization.
Learning optimization algorithms to improve our parameters' update, how to normalize the inputs at each and every layer, how to prioritize certain hyperparameters over others when testing.
All culminating with Tensorflow, a platform that saves us a lot of time in programming Neural Networks.
автор: GAUTHAM M N•
16 окт. 2020 г.
This one got pretty much quicker than the last course and was quite simple, though valuable inputs were provided by Andrew Ng. He is the coolest DL teacher i could have come across till now. Tensorflow is awesome and good basic idea was given in this course. Suggest it for everyone to go through. and I am running towards finishing this specialization.
автор: GAURAB B•
19 июня 2019 г.
Brilliant material altogether.. almost a compulsory course for researchers diving on the ocean of deep learning.. While I was reading papers on deep learning I came across all these terms but couldn't understand it.. Now the picture is pretty clear... Thanks Prof. Andrew Ng for this wonderful effort. I have already recommended this course to everyone.
автор: zhijun l•
6 дек. 2018 г.
A great course talks about the detail in building Neural networks. With the first course as a foundation, student taking this definitely will get a better understanding on hyperparameter tuning and optimization, in addition on training neural networks. I recommend this course to those who would like to know neural networks more than just the concept!!
автор: shaila a•
26 июля 2020 г.
The details covered in the course are very important for pracical use. They are not commonly available on the Internet otherwise. Also, with the new libraries that make the task of coding easier, the knowledge of tuning parameters, of optimizing learning curves, is often overlooked. This course highlights the importance of that knowledge. Thank you!
14 авг. 2017 г.
Having completed Udacity 730 on Tensorflow, I found Andrew Ng filled crucial gaps in my understanding. He is not afraid of presenting some maths to build intuition, but he always presents it in a straightforward way. Compare his explanation of Adam optimisation with the source paper on the subject. Andrew boils it down and serves it up beautifully.
автор: Manoj K K•
29 окт. 2020 г.
One of the course I have ever taken. Taught me the nuts and bolts of Neural Networks. Now I feel more confident dealing with hyperparameter tuning. Before this course I am just doing trail and error method or grid search to find the hyperparameters with understanding why it work or didn't work. Now I understand what should be done to make it work.
автор: Adail M R•
13 сент. 2017 г.
Once more, Prof. Ng show in his simple style how to tackle the tough subject of hyperparameter tuning, pointing to several techniques and helping us selecting the most appropriate ones for the task at hand. The Tensorflow introduction is also very effective and engaging! Looking forward to advance my knowledge and experience with the next courses!
автор: Diego A P B•
6 мар. 2018 г.
Hyperparameter tuning and the other techniques seen in this course are not perceived to be the most fashionable areas of machine learning and deep learning. Nonetheless, they are crucial parts, and thus the techniques shown in this course will show you how to save great amounts of time and headache when trying to improve and finetune your models.
автор: K R•
12 июня 2020 г.
This course is very helpful in the matter of enhancing the knowledge from the previous course and getting the right intuitions about improving deep learning neural networks.
Thanks to Professor Andrew Ng for making it very clear and easy to understand and giving me the right tools for my Phd research .
I look forward to getting to the next course.
автор: RUDRA P D•
6 июня 2020 г.
All the topics are very understandable, the way Andrew sir describe a concepts is just awesome. During the first specialization course i.e Neural Networks and Deep Learning , I was very confused about the hyperparameters tunning (like how to know what to chose). Khan Academy has helped me a lot to understand the underlying mathematical concepts.
автор: Nestor H•
5 июня 2018 г.
It was a great course to take. I could grab basic knowledge on TensorFlow and on some optimization techniques. I consider all the optimization algorithms are based on gradient descent, it is just that they tweak some parameters, but they are gradient-descent like algorithms. In summary, Dr. Ng is a genius and it is worth taking all his classes.
автор: Jay P G•
30 дек. 2019 г.
After knowing the basics of Deep Learning and Neural Networks (From the course 1) , this course explains the crux of improving and tuning of the neural networks and it's parameters and Hyper parameters . And the intro to tensor flow at last was just awesome(not exaggerating it!!!) . Congrats to Andrew and his team for such an awesome course .
автор: Shivdas P•
24 дек. 2019 г.
This course extends what has been taught in the preceding course, especially the different hyper parameters and optimisation strategies. Getting started with TensorFlow in a complete end-to-end example has been one of the things I was looking for and this course puts all that and many other things into perspective. Thanks Andrew and team !!
автор: Tamás K•
3 авг. 2019 г.
The course was great, thank you! However, I'm really looking forward using Tensorflow in C++ or Swift. The obscure, untyped nature of Python facilitates cargo-cult habits, creates some mystic fog around the variables (since it's not explicit if e.g. 'cost' is a concrete float or an entire computation waiting to be executed) and error-prone.
автор: Eulier A G M•
31 авг. 2019 г.
The course is very well structured, most of the topics here is perhaps kind of boring due the lack of real-problems projects, but if you stick to it and learn the concepts, will boost your understanding when using Deep Neural Network Frameworks, such as Tensorflow. That makes creating DNN easy to set, understand and apply to your problems.
автор: Suhas P•
21 сент. 2017 г.
Introduction to TensorFlow was wonderful. This course has helped me visualize and experience end to end flow of an actual machine learning project that helped a lot. Thanks to Andrew for taking efforts to design the course in a user friendly way. Programming tips are intuitive, helps save your time and allows you to focus more on learning.
автор: Chandan N•
27 нояб. 2019 г.
Great insights into the theory of regularization and famous optimization algorithms like RMSProp and Adam. Helps in developing intuition regarding these algorithms work and implementing them from scratch was pretty rewarding as well.
As usual, Prof Andrew Ng patiently explains the theory and helps in building understanding of the material.
автор: Saransh M•
20 авг. 2019 г.
Started from the basics but made sure that they provided an in depth understanding of some very important concepts like hyperparameters and regularization will well structured quizzes and interesting programming assignments. Really liked the course and would suggest it to anyone trying to set their feet in the field of ML or Deep Learning
автор: Shuvayan G D•
16 июня 2019 г.
This is probably one of the best courses on hyperparameter tuning. Along with Andrew's teaching , the course assignments are just perfect to get the perfect intuition of how optimizers work in the deep learning frameworks , also you will be able to build your own optimizer from scratch after doing this course , though not recommended. : P
автор: Mohd F•
17 мая 2019 г.
This is an amazing course, it helps me a lot to gain the basic intuition, and the idea behind tunning our model, this course provides understanding basic maths of how we can knob various hyperparameters, which would eventually lead us to a better statistical model in term of both speed and performance... Thankyou coursera ...Thanks Andrew