Python AI | How to Build a Neural Network & Make Predictions
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Python AI: How to Build a Neural Network & Make Predictions If you`re just starting out in the artificial intelligence (AI) world, then Python is a great language to learn since most of the tools are built using it. Deep learning is a technique used to make predictions using data, and it heavily relies on neural networks. Today, you`ll learn how to build a neural network from scratch. In a production setting, you would use a deep learning framework like TensorFlow or PyTorch instead of building your own neural network.
That said, having some knowledge of how neural networks work is helpful because you can use it to better architect your deep learning models. • How to build a neural network from scratch using Python Imagine that you need to write a Python program that uses AI to solve a sudoku problem. The difference between these techniques and a Python script is that ML and DL use training data instead of hard-coded rules, but all of them can be used to solve problems using AI. Machine learning is a technique in which you train the system to solve a problem instead of explicitly programming the rules.
Getting back to the sudoku example in the previous section, to solve the problem using machine learning, you would gather data from solved sudoku games and train a statistical model. The task is to use this dataset to train a model that predicts the correct outputs based on the inputs. The image below presents the workflow to train a model using supervised learning: Workflow to train a machine learning model The combination of the training data with the machine learning algorithm creates the model. Then, with this model, you can make predictions for new data.
The goal of supervised learning tasks is to make predictions for new, unseen data. Prediction problems become harder when you use different kinds of data as inputs. What if you want to train a model to predict the sentiment in a sentence? An example of a feature engineering technique is lemmatization, in which you remove the inflection from words in a sentence. The following image presents the process of lemmatization and representation using a bag-of-words model: Deep learning is a technique in which you let the neural network figure out by itself which features are important instead of applying feature engineering techniques.
This means that, with deep learning, you can bypass the feature engineering process. With neural networks, you don`t need to worry about it because the networks can learn the features by themselves. A neural network is a system that learns how to make predictions by following these steps: You can think of each layer as a feature engineering step, because each layer extracts some representation of the data that came previously. One cool thing about neural network layers is that the same computations can extract information from any kind of data.
The process to extract meaningful information and train the deep learning model is the same for both scenarios.
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