it is wonderful course i learned more in Deep learning and how to apply regularization

and how to optimize cost function also programming in Tensor flow

i thanks all teaching assistant for there efforts to learn us

and i recommend this course to DL beginners

I recommend everyone to go through this course if you really want to learn detail about hyperparameter tuning , optimizers and regularization used to make neural network better. It helps to open black box of Neural network and know in detail about how all works.

Some very useful insights into practical implementation and optimization of neural networks, and a very welcome introduction to TensorFlow. After coding networks in numpy you both appreciate the framework, as well as understand what it's doing behind the scenes.

Again, fantastic. Great way to explain how to tune your algorithms to improve bias and variance. Great explanation of what optimizers are used and how they function. Glad to know the nuts and bolts of the parameters usually defined in machine learning frameworks

This cause is good for me. It taught me how to tune hyperparameters and correct regularisation and optimisation to speed up the process of Machine Learning. I got some useful knowledge to utilise Tensorflow to quickly create a model and put it into the product.

This course is a must for understanding hyperparameters and their tuning and choosing the best ones for your model. Prof. Andrew explains everything very simply and precisely. This course is intended for intermediate users who have knowledge with Deep networks.

I think the most important course of the 1st 3. It tells about all the different optimizations and practical aspects of training a deep neural network. I would keep referring to its content in the future too. Thank you team for creating such a wonderful course.

This was one the best course as it has made me capable to increase the efficiency of a project as it has taught me various techniques of selection of data size ratios, tunning hyper-parameters speeding gradient checking using different techniques and many more.

I would like to thank coursera to giving me opportunity to follow Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization course. In the last few weeks I learned a lot of new theories and basics of improving deep neural networks.

Este curso ofrece una información más interesante que el primero, teniendo en cuenta que ya se saben los conceptos básicos. Hay un contenido de calidad que está bien explicado, sobre todo ayuda de cara a saber por qué se eligen los hiper parámetros en una red.

The gentle transition from NumPy based implemented deep learning functions to the Google's TensorFlow framework is so smooth and easy to comprehend. My understanding of the concepts has been solidified by the course. Thank you Andrew Ng and the Coursera team.

Extremely useful course . I highly recommend it . This course give me some helpful tips to tune hyperparameters , some optimization techniques that never heard before . The intro to Tensorflow in third weed is great . Assignment also proves to be insightful .

Very in-depth course to understand how to fine tune hyperparameters properly and which hold the greatest significance for performance. The Regularization techniques are also interesting to learn what the theory behind Adam, RMSprop, and Momentum optimizers.

The course materials are well-designed. However, I have to say this is not an easy course as I spent a lot of efforts in order to understand how to do the assignments. Overall, I strongly believe the course has taught me what I need to know about this topic!

I've learn a lot from professor Andrew Ng.He is definitely my super idol.Thanks a lot.It‘s a pretty awesome platform for me to learn from the giant.From the course of machine-learning to deep-learning,I really feel like I've made a lot of progress.Thanks.

This was an interesting and challenging course. Andrew gives good intuitions about the fundamentals of improving deep neural networks. I recommend having separate optional sections explaining the math behind some of the concepts for those who are interested

This course brought to light a lot of the more intricate topics in deep learning. Compared to my knowledge before the course, I now feel like I have a sound understanding of all the small nuts and bolts that work in a deep learning system. Loved the course.

This is one of the best courses on Coursera. Cleared a lot of concepts. Before this course, I was always thinking, what to do if I had to classify among multiple classes, but the explanation of softmax was actually very helpful in answering that question.

This was a very interesting and different course from others. I found it very helpful

for improving the NNs and the techniques taught with assignments give a well insight so as to how the problem should be dealt with.

Thank you to teachers and to Coursera.

A perfect course on Deep learning. Mathematical analysis well put forward by Andrew. I am looking forward to finish Deep Learning specialization. I would appreciate if he provides reference to textbooks to learn more about the fundamentals.

Thank you,

Vivek

Great course. It goes over many practical method to speed up the training process and also does an excellent job at explaining why these algorithm work. The programming assignment notebooks were also nice and helps to reinforce and understand the theory.

Recommend! The parts of batch norm and epoch in mini-batch solved my confusions. And the exercise of Tensorflow is simplify and useful. Without tedious documents, you can easily contruct a neural network for practical problems following the instructions.

Yet another great course from Prof. Andrew Ng and Coursera. Deeply grateful to all involved in the preparation of this course. Absolutely essential to learn these concepts if we want to build and optimize deep neural networks for creating great products!

The conciseness of the course material and interviews with industry experts offer thorough insight and can inspire confidence in new and old DNN learners alike. I look forward to learning more in the remaining courses of the Deep Learning Specialization!

I benefit a lot from this course regarding parameter initialization, hyperparameter optimization, batch normalization, optimization, etc. All the knowledge are well explained both intuitively and mathematically. Always enjoyable to learn from Andrew Ng.