If you buy a USB-C charger that doesn’t support Power Delivery and try to use it with a Microsoft Surface, for example,

Apply different NLP techniques: You can add more NLP solutions to your chatbot solution like NER (Named Entity Recognition) in order to add more features to your chatbot. With having a NER model along with your chatbot, you can easily find out any entity that appeared in user chat messages and use it for further conversations. And also you can add a Sentiment Analysis model to identify different sentiment tones behind user messages and it will exactly give some additional colors to your chatbot.

How can you make your chatbot understand intents in order to make users feel like it knows what they want and provide accurate responses. The strategy here is to define different intents and make training samples for those intents and train your chatbot model with those training sample data as model training data (X) and intents as model training categories (Y).

Five years later, USB-C is near-ubiquitous: Almost every modern laptop and smartphone has at least one USB-C port, with the exception of the iPhone, which still uses Apple’s proprietary Lightning port. For all its improvements, USB-C has become a mess of tangled standards — a nightmare for consumers to navigate despite the initial promise of simplicity.

We are going to implement a chat function to engage with a real user. When a new user message is received, the chatbot will calculate the similarity between the new text sequence and training data. Considering the confidence scores got for each category, it categorizes the user message to an intent with the highest confidence score.

The variable “training_sentences” holds all the training data (which are the sample messages in each intent category) and the “training_labels” variable holds all the target labels correspond to each training data.

Anyone going all-in on USB-C will run into problems with an optional standard called Power Delivery. The standard allows devices to charge at a much higher wattage relative to older connectors, therefore allowing them to charge faster. But it requires the right combination of charger, cables, and device to actually achieve this.

Next, we vectorize our text data corpus by using the “Tokenizer” class and it allows us to limit our vocabulary size up to some defined number. When we use this class for the text pre-processing task, by default all punctuations will be removed, turning the texts into space-separated sequences of words, and these sequences are then split into lists of tokens. They will then be indexed or vectorized. We can also add “oov_token” which is a value for “out of token” to deal with out of vocabulary words(tokens) at inference time.

Then why it needs to define these intents? That’s a very important point to understand. In order to answer questions, search from domain knowledge base and perform various other tasks to continue conversations with the user, your chatbot really needs to understand what the users say or what they intend to do. That’s why your chatbot needs to understand intents behind the user messages (to identify user’s intention).

Apply different NLP techniques: You can add more NLP solutions to your chatbot solution like NER (Named Entity Recognition) in order to add more features to your chatbot. With having a NER model along with your chatbot, you can easily find out any entity that appeared in user chat messages and use it for further conversations. And also you can add a Sentiment Analysis model to identify different sentiment tones behind user messages and it will exactly give some additional colors to your chatbot.

After training, it is better to save all the required files in order to use it at the inference time. So that we save the trained model, fitted tokenizer object and fitted label encoder object.

I will define few simple intents and bunch of messages that corresponds to those intents and also map some responses according to each intent category. I will create a JSON file named “intents.json” including these data as follows.

Before jumping into the coding section, first, we need to understand some design concepts. Since we are going to develop a deep learning based model, we need data to train our model. But we are not going to gather or download any large dataset since this is a simple chatbot. We can just create our own dataset in order to train the model. To create this dataset, we need to understand what are the intents that we are going to train. An “intent” is the intention of the user interacting with a chatbot or the intention behind each message that the chatbot receives from a particular user. According to the domain that you are developing a chatbot solution, these intents may vary from one chatbot solution to another. Therefore it is important to understand the right intents for your chatbot with relevance to the domain that you are going to work with.

Techies hailed USB-C as the future of cables when it hit the mainstream market with Apple’s single-port MacBook in 2015. It was a huge improvement over the previous generation of USB, allowing for many different types of functionality — charging, connecting to an external display, etc. — in one simple cord, all without having a “right side up” like its predecessor.

We discussed how to develop a chatbot model using deep learning from scratch and how we can use it to engage with real users. With these steps, anyone can implement their own chatbot relevant to any domain.

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to appear between the Earth and an observer, the Earth would appear gravitationally lensed in a fashion similar to this, dependent on the Earth’s position relative to the black hole and the observer. If a black hole were to form from the Earth itself, it would create an event horizon just 1.7 centimeters in diameter. (ANDREW HAMILTON / JILA / UNIVERSITY OF COLORADO)