Neural Networks for Beginners - a simple and accessible introduction to the amazing world of artificial intelligence. We’ll start by explaining the basic principles of how neural networks work, using clear analogies and visual examples. Then we’ll move on to practical aspects - how to create, train, and use neural networks to solve real problems. By the end of this article, you’ll have a clear understanding of what neural networks are and how they work.

What are Neural Networks?

Neural networks are computational systems inspired by biological neural networks that make up the human brain. The key thing is that a neural network essentially solves one of two tasks: classification or regression. That is, calculate the apartment price based on its characteristics (regression) or determine - is there a cat or a dog in the photo?

They can recognize complex patterns in data and make decisions or predictions based on them. Neural networks find applications in various fields - from speech recognition and image processing to recommendation systems and fraud detection.

Neural networks consist of many interconnected nodes, similar to neurons in the brain. These nodes are organized into layers, and each layer processes information coming from the previous layer. Input data is sequentially transformed by layers until the desired result is obtained at the output.

Introduction to Neural Networks: What They Are and Why We Need Them

Neural networks are a machine learning tool capable of solving a rather limited range of tasks. And actually, they weren’t trendy at all - and only after the emergence of embeddings and the attention mechanism following the publication of the famous paper “Attention is All You Need” did the synergy of these three technologies occur, which resulted in the creation of Large Language Models (LLMs). Nevertheless, neural networks remain a fundamental building block of modern AI systems. So we’ll have to understand them, there’s no way around it.

So, in simple terms, a neural network is a system inspired by biological neural networks in the human brain. It consists of interconnected nodes called artificial neurons that process information similarly to how neurons do in our brain.

The goals and objectives of neural networks are quite broad. They can be used for pattern recognition, natural language processing, time series forecasting, and much more. The key idea is that a neural network can learn to recognize complex patterns in data, making it a powerful tool for solving diverse tasks.

Real-world examples of neural network applications are everywhere. You’ve probably encountered them, even if you didn’t realize it. Speech recognition in smartphones, movie and music recommendation systems, credit card fraud detection - these are all areas where neural networks play an important role.

The reasons for the growth in popularity of neural networks in recent years are numerous. First, large volumes of data needed to train complex models have become available. Second, the computational power of modern computers and graphics processors allows efficient training of neural networks. And finally, significant theoretical and practical breakthroughs have been achieved in the field of machine learning, making neural networks more powerful and accurate.

This is just the beginning of our journey into the world of neural networks. In the following sections, we’ll examine how they work at a deeper level, how they’re trained, and what architectures exist. In general, we’ll go through the basics a bit.

How Neural Networks Work in Simple Terms

Imagine that neural networks are a kind of electronic brain consisting of many tiny computational units called neurons. Each neuron receives input data, processes it, and passes the result forward. At least that’s the modern understanding of how the brain works - you have 86 billion neurons in your head, and they’re all connected by synapses. And your whole life is essentially the creation or removal of connections between these very neurons. And there’s something similar in artificial neural networks.

The Concept of an Artificial Neuron

An artificial neuron is the basic building block of neural networks, inspired by biological neurons in the human brain. It takes multiple input signals, multiplies each by a specific weight, sums all these weighted inputs, and applies a nonlinear activation function. The result of this calculation becomes the neuron’s output signal.

Here’s a simple Python code example illustrating how one neuron works:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19

import numpy as np

# Input signals
inputs = [0.5, 0.1, 0.2]

# Synapse weights
weights = [0.3, 0.2, 0.4]

# Activation function (in this case - sigmoid)
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# Computing weighted sum of inputs
weighted_sum = np.dot(inputs, weights)

# Applying activation function
output = sigmoid(weighted_sum)

print(f"Neuron output signal: {output}")

This simple example demonstrates how input signals are weighted, summed, and transformed by a nonlinear activation function to produce the neuron’s output signal.

Neural Network Structure: Layers and Connections

Neural networks consist of many neurons organized into layers. The input layer receives the original data, then they pass through one or more hidden layers where the main processing occurs, and the output layer generates the final result.

Neurons in adjacent layers are connected to each other, and the strength of these connections is determined by weights. During training, these weights are constantly adjusted so that the neural network can better solve the given task.

The Process of Information Transmission and Processing

When a neural network receives input data, it propagates from the input layer to the output layer, passing through all hidden layers. In each layer, neurons perform their calculations, passing results further along the chain.

This process is called forward propagation. At the output, the neural network generates some result, which is then compared with the expected result (in the case of supervised learning).

Of course, I’ve simplified many details here, but I hope the basic principle of how neural networks work has become a bit clearer. In the following sections, we’ll examine the process of training neural networks, various architectures, and their application in real tasks.

Training a Neural Network: From Theory to Practice

So, we’ve understood the basic concepts of neural networks and how they work. But how do we make these networks learn and solve real problems? This is where neural network training algorithms come into play.

Supervised vs Unsupervised Learning

There are two main approaches to training neural networks: supervised learning and unsupervised learning.

In supervised learning, we provide the neural network with training data consisting of input examples and their corresponding correct answers or labels. The network’s task is to learn to recognize patterns in the data and predict correct answers for new, previously unseen examples. Supervised learning is widely used in classification tasks (pattern recognition, sentiment analysis) and regression (price forecasting, time series).

On the other hand, in unsupervised learning, we provide the neural network only with input data without labels. The network’s task is to independently discover hidden patterns and structure in this data. Unsupervised learning is often used for data clustering, anomaly detection, and data compression.

Example of supervised learning in Python using the Keras library:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17

from keras.models import Sequential
from keras.layers import Dense

# Creating a sequential model
model = Sequential()

# Adding a fully connected layer with 64 neurons and relu activation function
model.add(Dense(64, input_dim=20, activation='relu'))

# Adding an output fully connected layer with 1 neuron and linear activation function
model.add(Dense(1, activation='linear'))

# Compiling the model with optimization algorithm and loss function
model.compile(optimizer='adam', loss='mean_squared_error')

# Training the model on training data X_train and y_train
model.fit(X_train, y_train, epochs=100, batch_size=32)

Backpropagation Method

One of the key algorithms for supervised learning of neural networks is the backpropagation method. This method allows the neural network to adjust connection weights between neurons to minimize the error between actual and desired output values.

During backpropagation, the following occurs:

1. Input data is fed to the neural network input, and the network generates output values.
2. The error between the obtained output values and desired (target) values is calculated.
3. The error propagates backward, from the output layer to the input layer, adjusting connection weights to minimize the error.
4. This process is repeated many times on the entire training dataset until the error reaches an acceptable level.

Although the backpropagation method is a powerful tool for training neural networks, it has some limitations, such as the vanishing/exploding gradient problem and inability to efficiently process sequential data. These limitations have led to the development of more complex neural network architectures, such as recurrent neural networks (RNN) and transformers, which we’ll discuss later.

The Importance of Quality Data in Training

Regardless of the chosen training approach (supervised or unsupervised), the quality and diversity of data are crucial for successful neural network training. If training data contains errors, noise, or doesn’t reflect the real situation, the neural network won’t be able to generalize patterns properly and will give incorrect results on new data.

Therefore, it’s important to carefully prepare and clean data before training a neural network. This may include removing outliers, handling missing values, normalizing data, and expanding the training dataset using augmentation methods (artificially creating new examples based on existing ones).

Additionally, to ensure the neural network’s generalization ability, it’s necessary to use diverse training data covering various situations and conditions that the network may encounter in the real world.

Moving to the next section, we’ll examine various neural network architectures, each designed to solve specific tasks and having its own strengths and weaknesses. Understanding these architectures will help you choose the most suitable model for your goals and data.

Diversity of Neural Networks: Simple Explanation of Complex Architectures

Neural networks are flexible and powerful machine learning models capable of solving a wide range of tasks. However, not all neural network architectures are the same - they specialize in different types of data and tasks. Let’s look at some of the most common architectures.

Feedforward Neural Networks (FNN)

Feedforward neural networks, also known as multilayer perceptrons, are among the simplest and most widely used architectures. In them, each neuron is fully connected to all neurons of the next layer. This allows the network to find complex nonlinear relationships between input data and desired output.

Here’s a simple example of a feedforward neural network in Python using the Keras library:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14

from keras.models import Sequential
from keras.layers import Dense

# Creating the model
model = Sequential()
model.add(Dense(64, input_dim=20, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compiling the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Training the model
model.fit(X_train, y_train, epochs=10, batch_size=32)

Feedforward networks work well with tabular data but aren’t very efficient for processing images, text, or sequential data.

Convolutional Neural Networks (CNN)

Convolutional neural networks are specifically designed to work with images and other data with topological structures. They use convolution and pooling operations to extract features from input data, making them a powerful tool for computer vision tasks such as object recognition and image classification.

Here’s an example of a simple convolutional network in Python using Keras:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32)

Convolutional networks are widely used in computer vision but are less efficient for tasks with sequential data, such as text processing or time series.

Recurrent Neural Networks (RNN)

Recurrent neural networks, including their Long Short-Term Memory (LSTM) variant, are specifically designed to process sequential data such as text, speech, or time series. They can remember previous inputs and use this information to predict next outputs.

Here’s an example of a simple RNN in Python using Keras:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13

from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

vocab_size = 10000  # Vocabulary size
max_len = 100  # Maximum sequence length

model = Sequential()
model.add(Embedding(vocab_size, 128, input_length=max_len))
model.add(LSTM(128))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32)

Recurrent networks are widely used in natural language processing tasks, speech recognition, and time series, but are less efficient for tasks with images or tabular data.

Limitations of Traditional Architectures

Although traditional neural network architectures such as feedforward, convolutional, and recurrent networks have shown impressive results in many areas, they have some limitations. For example, they may not scale well on very large datasets or complex tasks requiring accounting for long dependencies in data. Additionally, these architectures often require significant computational resources and training time.

These limitations have led to the development of new architectures such as transformers, which can better handle complex tasks and use computational resources more efficiently. We’ll look at transformers in the next section.

Evolution Toward Transformers: A New Era in Machine Learning

Despite the enormous success of traditional neural networks in various tasks, they have a number of fundamental limitations and disadvantages. Let’s examine some of these problems.

Problems of Traditional Architectures

One of the main problems of classical recurrent neural networks, such as LSTM and GRU, is the difficulty of parallel computations. Due to their sequential nature, processing long sequences takes a lot of time. Additionally, they suffer from the vanishing/exploding gradient problem, which makes training on long sequences difficult.

Convolutional neural networks, while showing excellent results in computer vision, have limitations in processing sequential data such as text or audio. They also have difficulties working with variable-length data.

Another problem is the lack of a mechanism for efficiently modeling long-term dependencies in data. Traditional architectures rely on recurrent connections, which don’t always handle this task well.

Finally, most classical neural network architectures require significant computational resources and time for training, especially when working with large volumes of data.

Advantages and Innovations of Transformers

Transformers were developed to solve many problems inherent in traditional neural networks. Here are some key advantages of this architecture:

1. Parallel computations: Transformers can process sequences in parallel, making them much faster than recurrent networks when working with long sequences.

2. Self-attention mechanism: Instead of recurrent connections, transformers use a self-attention mechanism that allows them to efficiently model long-term dependencies in data.

3. Flexibility to input data length: Transformers can process input data of variable length, which is difficult for many other architectures.

4. Universality: Transformers have shown excellent results in various tasks, from natural language processing to computer vision and audio.

Here’s a simple Python code example for creating and training a transformer using the Hugging Face library:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

from transformers import BertForSequenceClassification, AdamW

# Loading pre-trained BERT model
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Defining optimizer and loss function
optimizer = AdamW(model.parameters(), lr=2e-5)

# Training the model
for epoch in range(epochs):
    model.train()
    for batch in train_loader:
        # Getting input data and labels
        input_ids, attention_mask, labels = batch
        
        # Computing model outputs
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        
        # Backpropagation
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

Comparing Transformers with Classical Neural Networks

Transformers represent a real breakthrough in machine learning, offering many advantages compared to traditional neural network architectures. Here’s a brief comparison:

• Parallel computations vs sequential
• Self-attention mechanism vs recurrent connections
• Better modeling of long-term dependencies
• Higher performance and scalability
• Universality and flexibility to different data types

Although transformers have some disadvantages, such as high memory and computational requirements, their advantages make them one of the most promising and rapidly developing architectures in machine learning.

The transition to transformers has marked a new era in artificial intelligence, opening new possibilities for solving complex tasks and achieving higher results in many areas. In the next section, we’ll take a closer look at key concepts of neural networks and their future development.

Conclusions and Prospects: What’s Next?

In this article, we’ve examined the basic concepts of neural networks, their varieties, and operating principles. Now that we have a basic understanding, let’s summarize and look at the future of this exciting field.

Key Concepts of Neural Networks

Neural networks are powerful machine learning tools inspired by biological neural networks. They consist of interconnected nodes, or neurons, that process input data and transmit signals forward.

Neural network training occurs by adjusting the weights of connections between neurons so that the network can recognize certain patterns in data. The popular backpropagation method allows weights to be adjusted based on the discrepancy between expected and actual network output.

There are various neural network architectures, each suitable for solving specific tasks. Fully connected networks are good for working with tabular data, convolutional networks excel at image processing, and recurrent networks are efficient for analyzing sequential data such as text or time series.

Open Questions and Research Directions

Despite the impressive success of neural networks, many unsolved problems and opportunities for further research remain in this field. One key question is interpretability - the ability to understand how a neural network makes decisions. This is important for ensuring transparency and trust in artificial intelligence systems.

Another relevant topic is the efficiency of neural network training. Modern models require enormous computational resources and large volumes of data for training. Researchers are working on developing more efficient algorithms and methods that will speed up the training process and reduce resource requirements.

Additionally, ways to combine neural networks with other machine learning methods, such as symbolic learning and logic programming, are being actively studied. This could lead to the creation of more powerful and flexible systems combining the advantages of different approaches.

Preview of Transformer Architecture

In the last part of the article, we briefly mentioned transformers - a new class of models that are rapidly gaining popularity in natural language processing and other tasks. Transformers are based on the self-attention mechanism, which allows them to efficiently process sequential data and consider context.

One key advantage of transformers is their ability for parallel processing, which makes them more performant compared to recurrent networks. Additionally, they demonstrate better results in tasks requiring deep understanding of context, such as machine translation and text generation.

Examples of popular transformer architectures include BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). These models are pre-trained on huge volumes of text data, then fine-tuned for specific tasks such as question-answering systems or automatic summarization.

Although transformers have already achieved impressive results, their potential is far from exhausted. Researchers continue working on improving these models, studying new architectures, training methods, and ways to increase efficiency.

In conclusion, I’ll say that the field of neural networks and machine learning is developing rapidly, opening new possibilities and exciting prospects. Despite the achieved successes, we still have many interesting tasks and challenges ahead, solving which will allow us to create more powerful and intelligent systems capable of helping humanity in various spheres.