Photo by Florian Olivo on Unsplash

Why APIs?

Data sets are popular resources, but they’re not always useful. Here are a few situations when data sets don’t work well:

  • You only want a small piece of a much larger data set. Reddit comments are one example. …

Photo by Erik Mclean from Pexels

The data set that we are going to use is the Cornell movie corpus. It’s a data set of more than 600 movies containing thousands of conversations between lots of characters. We’ll build a general chatbot that can have a general conversation with us like a friend, to talk about everyday life. Thats why movies are perfect because in movies you have a lot of random conversations, general conversations between friends. Our model can be trained on other more specific dataset like calender assistant or navigation assistant. These are some other applications of the chatbot.

Plan of Attack

1. Installation, setting up an environment, and getting the Dataset

In Windows Anaconda Prompt, create…

Part 1

In this part, we’ll learn about Loss Function, Optimizer-Stochastic Gradient Descent, Learning Rate & Batch Size, Overfitting & Underfitting.

3) Stochastic Gradient Descent

As with all machine learning tasks, we begin with a set of training data. Each example in the training data consists of some features (the inputs) together with an expected target (the output). Training the network means adjusting its weights in such a way that it can transform the features into the target. In the 80 Cereals dataset, for instance, we want a network that can take each cereal’s ‘sugar’, ‘fiber’, and ‘protein’ content and produce a…

We’ll cover some key concepts of deep learning with a hands-on example:

What is Deep Learning?

1) Single Neuron — Linear Unit

So let’s begin with the fundamental component of a neural network: the individual neuron. As a diagram, a neuron (or unit) with one input looks like

The input is x. Its connection to the neuron has a weight which is w. Whenever a value flows through a connection, you multiply the value by the connection’s weight. For the input x, what reaches the neuron is w * x. A neural network “learns” by modifying its weights.

The b is a special kind of weight we call the…

Neural networks(short for Artificial neural networks) model were inspired by the structure of neurons in our brain(biological neural networks).

Each cell in a neural network is called a neuron and is connected to multiple neurons. Neurons in human (and mammalian) brains communicate by sending electrical signals between each other.

But these are the only similarities between biological neural networks and artificial neural networks.

Deep Neural Network

A deep neural network is a specific type of neural network that excels at capturing nonlinear relationships in data. Deep neural networks have broken many benchmarks in audio and image classification. …


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