Creating a Neural Network and Making Predictions with Python's AI

If you’re new to the field of artificial intelligence (AI), Python is an excellent language to begin with because it serves as the foundation for many AI tools and libraries. Deep learning, a method for making predictions based on data, heavily depends on neural networks. This blog will show you through the process of constructing a neural network from the ground up.

In practical applications, rather than creating neural networks from scratch, you would typically utilize deep learning frameworks like TensorFlow or PyTorch. Nevertheless, understanding the inner workings of neural networks can be advantageous as it allows you to design more effective architectures for your deep learning models.

The upcoming content will encompass:

• Embarking on an exploration of artificial intelligence.
• Gaining a comprehensive understanding of how machine learning and deep learning contribute to the realm of AI.
• Peering into the intricate mechanisms at play within a neural network.
• Acquiring the knowledge and skills necessary to build a neural network entirely from scratch using Python.

Machine Learning

The Goals of Machine Learning

• Prediction and Classification: Machine learning aims to make accurate predictions and classify data into categories, helping automate decision-making based on data.
• Pattern Recognition: It identifies patterns, relationships, and trends within data, offering insights for better decision-making.
• Optimization and Automation: Machine learning optimizes processes and automates tasks, improving efficiency and resource allocation.
• Anomaly Detection and Recommendation: It detects unusual patterns and offers personalized recommendations, crucial for fraud detection and user engagement.
• Continuous Learning and Reducing Bias: Machine learning adapts to new data, remains up-to-date, and reduces human bias in decision-making through data-driven predictions.

Feature Engineering

Feature engineering is the process of selecting and transforming raw data into meaningful features that can be used for machine learning models. It is a crucial step in the data preprocessing pipeline, as the quality of the features you use can significantly impact the performance of your machine learning algorithms.

Here’s an example of feature engineering for weather prediction using historical weather data:

Rolling Average Temperature

Feature Engineering Steps:

1. Data Collection: Collect historical weather data, including temperature, humidity, wind speed, and other relevant variables, at regular intervals (e.g., hourly or daily).
2. Creation: Calculate the rolling average temperature over a specific time window, such as a 7-day rolling average. To do this, sum the temperatures of the previous 7 days and divide by 7 to get the average temperature. Repeat this calculation for each data point in your dataset.
3. Handling Missing Data: If there are missing temperature values for certain time periods, you can impute them using the average temperature for the same time of day over several days.
4. Scaling: Normalize the rolling average temperature feature to a consistent scale, such as z-score scaling, to ensure all features are on the same scale.
5. Feature Lagging: Create lag features by including the rolling average temperature from previous days as additional features. For instance, you might include the average temperature from the previous day, two days ago, and so on.

Nerural Networks

Deep learning, powered by neural networks, has revolutionized the field of machine learning, enabling the development of highly sophisticated models capable of solving complex problems across various domains.

• Values arranged into layers
• each layer manipulates the training data
• output of a layer is the input to the next layer
• the layers are steps in feature engineering
• Vectors (an ndarray)
• Linear regression: model the relationships between dependent variables and two or more independent variables

Implement a neural network

Weight

n a neural network, the term “weight” refers to the parameters associated with the connections between artificial neurons (nodes or units) in different layers. These weights play a crucial role in the network’s operation, as they determine the strength of the connections and affect the output produced by each neuron.

1. Connections between Neurons: In a neural network, neurons are organized into layers, typically consisting of an input layer, one or more hidden layers, and an output layer. Neurons in one layer are connected to neurons in the adjacent layers.
2. Weighted Sum: Each connection between neurons has an associated weight. When a neuron in one layer is connected to a neuron in the next layer, the input from the first neuron is multiplied by the weight of the connection. These weighted inputs are summed up, resulting in a weighted sum.
3. Activation Function: The weighted sum is then passed through an activation function, which introduces non-linearity into the network. The activation function determines whether the neuron should “fire” (produce an output) based on the weighted input.
4. Learning: During the training phase of a neural network, the weights are adjusted through a process called backpropagation. The goal of backpropagation is to minimize the error between the network’s predictions and the actual target values in the training data. The weights are updated in a way that reduces this error, making the network more accurate over time.
5. Role of Weights: The weights in a neural network represent the strength and significance of the connections between neurons. They are learned from data and are critical for the network to capture complex patterns and relationships in the data.
6. Learned Features: In deep learning, deep neural networks with multiple hidden layers learn increasingly abstract and high-level features as information flows through the network. These learned features are represented by the weights and help the network make sense of the input data.

Evaluate the prediction

1. Loss and cost function
2. MSE: simple mean squared error
3. (prediction - target)²