I really enjoyed this course, especially because it combines all different components (DNN, CONV-NET, and RNN) together in one application. I look forward to taking more courses from deeplearning.ai.
Really like the focus on practical application and demonstrating the latest capability of TensorFlow. As mentioned in the course, it is a great compliment to Andrew Ng's Deep Learning Specialization.
By Ravi P B•
Nice experience taking this course. Precise and to the point introduction of topics and a really nice way to start programming the models without going much into theory and a comprehensive and nice way to learn tensorflow framework. Mr. Laurence Moroney Sir has been excellent in all the courses and the conversations with Andrew sir are chilling as well as motivating. So its been a very good experience to take this specialization and learn tensorflow.
By Gabriel S•
Guys you are the best, i commented to Laurence to about six months ago that i finded my vocation with your course, and with the firts course of this specialization i didn't understand anything and i did the Deep Learning Specialization and i am really fascinated with AI and deep learning. By the way I am a mathematician and thank to you i am a AI research with TensorFlow as a tool for coding and building DL and ML models.
Really Thank you
By Andrei N•
Very detailed step by step tutorials of using Tensorflow with lots of effort to make things as easy to understand as possible. Especially, examples of generation a time-series pattern simulations looks very thoughtful and helpful for the course topic. A little lack of theory comparing to other courses by deeplearning.ai. Quizzes are quite undeveloped. But that is understandable, because the main goal of the course to introduce Tensoflow.
By Victor F L•
Really enjoyed the course and it definitely helped a lot with my own projects. I guess the only thing I felt should be different were some of the quizzes. It seemed to me that there was too much attention given to questions regarding little nuances of the code, while I believe they would have been more interesting and significant if they focused more on theoretical concepts of forecasting and modeling. Overall great course, nonetheless!
By Wirach L•
Before enrolled in this course, I've finished deep learning specilization taught by Andrew. This course really help me put everything together. I really hope that Laurence and Andrew keep releasing the course like this further.
Special thanks to Laurence himself, I really enjoy the way he taught. the way he progressively show how every some line of code done something and the way he wrap up in each weeks :D
By Tashreef M•
The overall course was good,. The topics were demonstrated nicely. However, the absence of programming assignment and assessments really keeps a hole within me questioning have I actually mastered it all. Would have been more fulfilling if there were some programming assignments, through there were chances for self-evaluation through given google colab assignments in the end of every week.
By Robin R•
I needed wonderful course and wonderful code to make me understand the way to solve the 'Time Series' problem. My English skill is awful, but the professor explained so, so well that I could understand pretty well. I knew the concepts of the models before I take this course, but I didn't have any opportunity to see codes realized! Thank you very much for offering me such a nice lecture :)
By Maximilian R•
Complicated ML-tasks as Sequence and Time-Series predictions are presented in a nice and easy-to-understand way (at least for me). Some of the techniques explained like image augmentation by ImageDataGenerator and creating synthetically datasets (especially time-series data) were new to me. So thank you for that nice course series and hope to see some more in the future.
By Rodrigo A Z M•
I didn't know anything about TensorFlow, but i had been study from the Andrew Ng courses, that are more focused on the theory. This course is the perfect complement, with the applications.
The only thing is that i find a little short the course, but anyway very good. Thanks Laurence and I expect to have enough time to see your another specializations!
By charles l•
This specialization was the ideal evolution in my DNN training after having taken Andrew Ng's classic ML course, followed by the deeplearning.ai DNN specialization. The instructor is excellent, as are the lecture notes, training materials, coding platform and examples! I am moving on to Advanced ML w/ TensorFlow on Google Cloud
By Ibrahim C•
This specialization was exquisite from beginning to end. Without going deeper into the mathematics of the machine learning and deep learning algorithm we learned practical uses of such algorithms using TensorFlow. All the quizzes and homework taught us a variety of techniques. I am very satisfied with this course.
By Wenlei Y•
I like all of the 4 courses in the entire series. Dr Moroney offers you the codes and you can play around by yourselves to better understand the concepts and the algorithms. My suggestion is: You can watch the videos and pass all the tests first and download the codes, and later you can study the codes in details.
By RUDRA P D•
This course is different from the other 3 courses of this specialization as it teaches a new concept i.e Time series. This thing has not been taught in the deep learning specialization. But everything that has been taught in this course is well explained. I have my gratitude to Laurence sir 's way of teaching.
By Anthony B•
At first, I thought this was tragically oversimplified. Then I realised that the real benefit of this course is the practical walkthroughs that it in the large consists of. Other courses can give you the theoretical foundations of Machine Learning, but this excels as a treasure trove of practical guidance.
By Ran X•
Very good Tensorflow ML tutorial. The explanation on data.DataSet needs to be more detailed.
By ALVARO M A N•
Personally I loved this course, I had a previous knowledge of this topic, because it's one of my favorites topics (very related to IoT analysis data). And here I've learned various top technics suchs lambda layers, or that we have to split in training, validation and testing periods the data. This is something that you don't see in many books or manual about time series with tensorflow. And finally I've learned very useful libraries that I even didn't know that exists like tf.keras.dataset, that makes so easy to give format to the data, before you had to write more code. So with this information I can write more effective and efficient code! Thanks Laurence and Andrew from Perú!
By Richard S•
This course was my ultimate motivator and goal for taking the specialization as I am doing work with time series. Very interesting to learn a traditional statistical approach, then apply DNNs, RNNs, LSTMs and CNNs to time series prediction. Even though just scratching the surface, I can apply knowledge from this course and specialization immediately.
Thank you Laurence and Andrew for a fantastic course and specialization! I am inspired and motivated to dig deeper into the theory of NNs and their application with further courses and projects.
By Saif H•
It was a brilliant course , I thoroughly enjoyed learning various aspects and techniques of Deep Learning techniques and in the process also learned a lot about TensorFlow . As mentioned by LM , its the first step and I'm really to have taken that first step.
One of the issue with the course has been the quality of audio, all the other course I have done on Coursera had very clear and audible voice over , however with this course I have struggled to hear with the audio, hopefully this can be addressed in future course.
By Hannan S•
First of all, the course was amazing! I found it great for the following reasons:
- Laurence Moroney (Instructor) was very professional and clear while delivering the knowledge
- The introductions by Andrew NG were really nice
- Easy to understand codes and understanding of thr underlying principles
- Varied topics such as CNN, NLP & Time Series
- Very insightful by providing expert opinions about different ways of model optimization
I really enjoyed the course and I thank the instructor for the same :)
By Vladimir B•
Love the course and especially the two instructors. This gave me a good overview and allowed me to formulate a roadmap for what I want to learn next (as well as what I am not interested in, looking at you CV). I wish the exercises were mandatory (like the first class of the series). Thanks for putting all this work in for basically free.
By Ara B•
The real life example of using the TF was excellent. I like to see more utilization of the TF in real life, things like anomaly detection in human interaction or imaging interpretation in health industry or economical data and modeling in financial markets. It was an excellent course overall. Many thanks!
By Edmund L C•
Excellent course that takes an amount of time right at what is specified in the program description. All learning is productive - you won't be spending hours teaching yourself how to program and debugging code, but will rather spend all time evaluating code to further your understanding of the concepts.
By Karl J•
A great course introducing syntax and application of TensorFlow to time series data. Does not go very deep, but pretty clearly is designed to show you how to apply the TensorFlow library to common situations rather than teach about time series and forecasting, which is a huge subject in and of itself!
By Rahul R•
Wonderful course........these courses tough me that there is always room for improvement. Just we need to try with small steps in the right direction. DL is not just achieved using algorithms but need patience and trial and error too. Thanks to Laurence Moroney and Andrew Ng for this wonderful course.
By Reza M•
This is a very good specialization. It helps you learn your way around TensorFlow and Keras and their knowledge base. You can literally build models in shortest formats like 5-10 cells after this. The usage of callbacks is covered as well which helps a lot in saving time and tuning model parameters.