So far in this series, we've mostly focused on how AI can interpret images, but one of the most common ways we interact with computers is through language - we type questions into search engines, use our smart assistants like Siri and...

Let’s try to help John Green Bot sound a bit more like the real John Green using Natural Language Processing. Today, we're going to code a program that takes a one word prompt and then completes the sentence that sounds like something...

After checking our model for accuracy, we will make predictions of our results from the model we created. We will visualize the predictions using the test data.

In this video, after training and testing the data, querying the questions, and obtaining responses, we are now at model completion with compiling the questions and responses to check for accuracy.

After learning to create our encoders for the input sequences and the questions, the encoders will match the data and obtain the responses.

We will begin to develop our deep learning model and input placeholders to store the maximum story length, question length, and define the vocabulary size. We will now build our encoder using the sequential model.

After creating the vectorization function, we will input the stories, queries, and answers using the vectorize function that we created using the train data.

In this video, we will understand how to vectorize the story by defining functions, using the data, and developing an index and tokenizer. We will also determine the maximum story length calculated.

In this video, you will learn to make the train-test story questions and answers. We will then use a for loop to enter the story, question, and answer into the respective parts.

You will learn to import the tokenizer with and without filters. We will fit the tokenizer into our vocabulary using the fit_on_texts built-in command.

In this video, you will learn to define the project's story length and question length, determined by the number of words in the story or question.

In this video, you will learn to combine the datasets, test data, and train data; we will use the all_data function to combine the datasets.

We will use the two datasets available for our project, the training QA and the testing QA. We will save them in our project folder and invoke them as needed.

This video demonstrates how to import step by step the four main libraries required for our project, including TensorFlow, NumPy, Keras, and Pickle, and you will learn about importing Sequential and Model.

After theoretically learning about chatbots, we will now develop a chatbot practically by importing the requisite libraries—TensorFlow NumPy, Keras, and Pickle. We will build a chatbot based on a story and make predictions of true or false.

In this lesson, you will learn about the various steps involved in developing chatbots based on deep learning techniques. We will install the required packages, define chat models and tokenization, and set up the encoder-decoder model.

In this video, we will understand what encoder and decoder are, their architecture, and long short-term memory (LSTM), a neural network used in deep learning.

This video elaborates on the deep learning architecture in chatbots, integration of deep learning with chatbots, natural language processing (NLP), source content, interaction history, and analytics.

In this video, you will learn more about deep learning and the general architecture, chatbot development with deep learning, and its related concepts.

Let's understand the benefits of chatbots in ecommerce, including personalized services, real-time interaction, collection of feedback, provide metrics, lead generation, deep analytics, and storytelling.

In this video, we will understand the benefits of chatbots in businesses, including lead qualification, lead nurturing, and data mining techniques.

We will explore the first kind of chatbot operating in the medical domain. We will look at the benefits, including the ability to schedule appointments, check patient symptoms, provide support, help with coverage and insurance claims,...

In this video, we will look at what deep learning chatbots are and how they work. We will also understand the benefits of deep learning chatbots, including improved customer experience, service integration, customer care, personalized...

In this lecture, you will learn about a generative and retrieval chatbot and compare the differences between the two chatbots.