mlwhiz

13 ms·

31.
by mlwhiz 7y ago
I totally agree. Command based bots might be the way to go for simple applications. Banks might do very well with command based bots. They know most of the questions being asked by the customer, why not automate the process. For developers,
32.
by mlwhiz 7y ago
Not yet. Maybe I will take a look.
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by mlwhiz 7y ago
Precisely. I thought they were too difficult. They are not if the use case is viable and you can solve it programmatically. The Hard thing is to guide the flow of the user and really designing the flow.
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by mlwhiz 7y ago
Agree. But most of the functional chatbots we see on websites are using intent classification only. In my view, this is mainly because we haven't yet reached the stage where we can create intelligent chatbots. So we make do with rule-b
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by mlwhiz 7y ago
I understand what you are saying here ;)
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by mlwhiz 7y ago
I do agree it is hard. As per usefulness, Customer service industry can use it in a pretty good way. Also i think they are fun :)
37.
by mlwhiz 7y ago
I said it okayish because this post is for learning purpose and not any advanced stuff. :) In my view, it all depends on what one creates. I agree that some of today's chatbots may not be the best but that's okay. Maybe we will be
38.
Chatbots aren't as difficult to make as you think(mlwhiz.com)
38 points by mlwhiz 7y ago|45 comments
39.
Chatbots aren't as difficult to make as You Think(mlwhiz.com)
6 points by mlwhiz 7y ago|2 comments
40.
by mlwhiz 8y ago
Here I tried to provide some use cases to make your life easier with Sublime Text
41.
Two Ways you can use Sublime Text for Data Science(mlwhiz.com)
1 points by mlwhiz 8y ago|1 comments
42.
How Data Scientists Can Use Sublime Text in Their Workflow(mlwhiz.com)
1 points by mlwhiz 8y ago|0 comments
43.
Show HN: Transfer Learning Intuition for Text Classification(mlwhiz.com)
1 points by mlwhiz 8y ago|0 comments
44.
Transfer Learning Intuition for Text Classification and Sentiment(towardsdatascience.com)
1 points by mlwhiz 8y ago|0 comments
45.
Explained Transfer Learning in NLP(mlwhiz.com)
4 points by mlwhiz 8y ago|0 comments
46.
Attention, CNN and What Not for Text Classification(mlwhiz.com)
1 points by mlwhiz 8y ago|0 comments
47.
Layman Explanations – Attention, CNN, LSTM for Text Classification(mlwhiz.com)
2 points by mlwhiz 8y ago|0 comments
48.
Attention, CNN and What Not for Text Classification(towardsdatascience.com)
3 points by mlwhiz 8y ago|0 comments
49.
Don't Trust Everything That Goes on the Kaggle Discussion Forums(mlwhiz.com)
4 points by mlwhiz 8y ago|0 comments
50.
What My First Medal Taught Me about Kaggle(mlwhiz.com)
3 points by mlwhiz 8y ago|1 comments
51.
Top Data Science Resources on the Internet(mlwhiz.com)
4 points by mlwhiz 8y ago|0 comments
52.
Good Feature Building Techniques – Tricks for Kaggle(mlwhiz.com)
2 points by mlwhiz 8y ago|0 comments
53.
What My Silver Medal Taught Me about Text Classification and Kaggle in General?(mlwhiz.com)
3 points by mlwhiz 8y ago|0 comments
54.
Creating Baseline is important for your NLP models. Here are some ways(mlwhiz.com)
3 points by mlwhiz 8y ago|0 comments
55.
A comprehensive guide to preprocess text data for deep learning(towardsdatascience.com)
4 points by mlwhiz 8y ago|0 comments
56.
What Kagglers Are Using for Text Classification(hackernoon.com)
4 points by mlwhiz 8y ago|0 comments
57.
Using XGBoost for time series prediction tasks(mlwhiz.com)
4 points by mlwhiz 8y ago|0 comments
58.
Before Deep Learning for text always try these methods(mlwhiz.com)
3 points by mlwhiz 8y ago|0 comments
59.
NLP Learning Series: Part 2 Conventional Methods for Text Classification(mlwhiz.com)
3 points by mlwhiz 8y ago|0 comments
60.
Be the Peter Keating of Data Science, Not Howard Roark(medium.com)
1 points by mlwhiz 8y ago|0 comments

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