Aug 27, 2019
Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!
Jul 22, 2020
Great course for anyone interested in NLP! This course focuses on practical learning instead of overburdening students with theory. Would recommend this to every NLP beginner/enthusiast out there!!
By LANKE N B J•
Jun 27, 2020
By Ashwani Y•
Apr 23, 2020
By Wellington B R•
Jul 24, 2020
By RAJAT S•
Jun 26, 2020
By Milad M•
May 15, 2020
By M n n•
Apr 16, 2020
By amit k•
May 12, 2020
By Mikhail C•
Apr 13, 2020
Not as strong as the previous 2 courses in the specialization. Each step building on itself (embedding --> LSTM --> stacked LSTMs) did not really show much improvement in the actual exercise. Also the main pieces that impacted accuracy and loss the (buffer size and batch size using SHUFFLE and BATCH_SIZE) were completely ignored. I also wish there were graded exercises that forced me to learn both the data ingestion, tokenization, and training pieces. I struggled through those in course 1 & 2 but it paid off in my understanding. Data preparation wasn't covered as in depth as the computer vision course either (i.e. how do I deal with large data? can I flow them in like the image generator?). Lots of broken links as well (some people might not know to go find the github repo to get the optional exercises).
Oct 21, 2019
Concise explanations and nice demos leading into a very easily digested lessons. Covering every important fundamental aspects without being bloated by too much technicalities (which are only useful in a more advanced implementation). But again, basic is still basic. The quizzes def need more work as to not rely on a simplistic memorization problems (which almost doesn't exist on always-connected working environment) and instead should ask for actual concepts or understanding.
Def not deep enough if you pay for it, but a good one if you can finish it during the trial period.
By Aditya G•
Jul 23, 2020
The Course was good. The content was the introduction of Natural Language Processing and it does well in explaining the theory and all but I think they should have dived a bit deeper into the topic. Sometimes in between the course, it does feel that much exciting, because some part of the code showed does not have any proper explanation. Maybe I'm saying these things because I'm in sync with Andrew Sir's teaching earlier. But overall this course will be best for beginner and it should be a great start for moving into the NLP field.
May 28, 2020
The pros: An abundance of Python Notebooks that help to build intuition, fascinating datasets, and some interesting NLP applications.
The cons: This course feels like content in progress compared to every other course I've done so far. The material is too general and references other courses when it should use this opportunity to reinforce content from other courses, e.g.courses in the DL Specialization. The lack of required coding exercises, together with the reuse of quiz questions in Week 4, were a disappointment.
By Corrie V S•
Feb 08, 2020
Some lessons in this course were so repetitive that it seemed like a waste of time. Week 2, in particular, felt monotonous and really put a damper on my interest in the information. Despite there being some useful code to learn, Laurence talks though the code in video clips, and then does a screencast of himself talking through the same code in a workbook. I have really enjoyed the 2 courses prior to the NLP course in the TensorFlow in Practice Specialization, but this one seems less developed.
By Kevin H•
May 14, 2020
The content is good, the videos well paced. The code examples are also very useful.
But I feel the structure of the class is too loose. In my opinion, it would benefit from having assignments that must be submitted and graded.
Maybe they could be small and focused - like focusing on just working with the tokenizer, or setting up Embedding layers or LSTM layers. There could also be one where you load a pretrained model and writing the next token prediction loop.
By Ethan V•
Aug 25, 2019
I'm a bit disappointed with this specialization overall. I think I expected a deeper familiarity with tensorflow, more exposure to the TFData abstraction for large datasets, more low-level exposure to extending your models to fit a specific problem in your domain. Instead I feel like this specialiaztion would better be titled "Black box manipulation of the Keras API". That's a shame, given how solid the first deeplearning.ai specialization was.
By K R V K•
Apr 07, 2020
This could have been some more intense with 2 quiz in each week (1 or 2 tough questions), giving a written explanation of what a code snippet is meant for or each line of code is meant for, spend time on explaining fundamental concepts. Highlights of course, clear and crisp in explanation of concepts and functioning of code. overall, coherence is well appreciated.
By luis a•
Oct 11, 2019
In my opinion, the course was too simple. There are many many concepts that are not covered properly. Even if they recommend going to the deep learning course from Andrew, I believe that at least could explain a bit more some parameters used in the functions and how actually work.
On the other side, you make cool thinks like text generation!
By Sina D•
Apr 20, 2020
This course does not follow the same standards as the previous courses from deaplearning.ai. The material taught in this course are two basic and do not go in-depth to introduce the major techniques that are being used in the field. The colab notebooks are not provided in most cases and you have to look for them in QA or Github.
By Stefan B•
Apr 12, 2020
In the previous two courses of the specialization, coding exercises were compulsory and graded. In this course, all coding exercises were voluntarily and not well documented. It seemed to me that for whatever reason, the makers of course 3 (natural language processing in tf) put less effort into the making. Bit disappointed.
By Dustin Z•
Jun 28, 2020
It was a good course like the rest in the series, though in this course, they don't link to the colab notebooks that Lawrence works through in the items for each week. The colab notebooks exist on lawrence's colab account but you need to hunt them down. I would suggest fixing this oversight.
By José D•
Apr 20, 2020
This third course provides main NLP concepts using Keras simple example codes. Just like Courses 1 & 2, there's no math and as explained in the videos, if you want a deeper understanding, then you want the "Deep Learning" specialization. Only quizzes, no graded exercises for this course
By Justin E•
May 02, 2020
It was good but there are several errors in the code for some weekly exercises.
I wanted to raise a PR in the author's Github repo to fix theses. However, upon seeing the backlog unaddressed PRs in the author's Github repo, I didn't bother as they will probably not be looked at.
By Rajat Y•
Nov 30, 2019
Since the course doesn't mention "Introduction" to NLP, I thought that the course will provide a detail insights to Natural Language Processing but the course only covers basics of it. Also as far as tensorflow is concerned I was expecting more hands-on experience in it.
By afshin m•
Jan 17, 2020
week 2 and week3 are disorganized - the examples don't run without making modifications based on information in the forums.
However the overall course is worth it. I hope they pay more attention to making the examples accessible and making them work.
By Thusitha N C•
Aug 01, 2020
Nothing against the instructor, he was really nice. But the content is extremely basic, to the extent that the whole course could be completed in one day. At least the previous courses had graded assignments, but this one was way too easy.
By PRATIK K C•
May 23, 2020
One example in case of text classification could have been theoretically worked out. For example classification using RNN/LSTM. How a word vector is passed as input to one unit of lstm? To view in on paper would make concepts more clear.