What is Deep Learning? Understanding the Foundation of Neural Networks

What is Deep Learning? Understanding the Foundation of Neural Networks

Introduction

Artificial Intelligence (AI) and Machine Learning (ML) have seen a breakthrough field in transforming the ways machines learn to accomplish tasks with the help of deep learning. The breakthroughs in technologies such as self driving cars, virtual assistants, or even systems generating human like text or images, are driven by it.

While traditional machine learning algorithms might not be up for complex tasks involving large dataset, deep learning excels at this, using layered neural networks that mimic how the human brain works. In this article we explore what deep learning is, how neural networks work, and the massive applications that are driving our future.

1. What is Deep Learning?

Machine learning has a most specialized branch called as deep learning which makes use of artificial neural network (ANN) model to solve some complex problems. As the name implies, machine learning is all about learning using simpler algorithms whereas deep learning is used to learn using multiple layers of networks or basically in other ways termed as ‘deep’ to learn it itself from huge amounts of data.

The way in which neurons in the human brain are interlinked is what deep learning was inspired by. These artificial neurons compose a neural network which can process the data as the human minds does: they can recognize the patterns, learn from them and perform various operations having no human interaction.

Deep learning is important to AI because it outperforms traditional methods on problems that they can't, like image classification, speech recognition and language processing.

2. How Do Neural Networks Work?

A neural network is at the heart of deep learning, which is a system of connected nodes or neurons built to mimic the workings of the brain. The same as above, neural networks are organized in layers and each layer learns more complex features from the data.

Basic Structure of a Neural Network:

• Neurons (Nodes): Artificial equivalent to brain cells, the building blocks of a neural network. Input is fed to each neuron, which 'processes' this and sends it to the 'next' neuron in the network.
• Layers: The Neural network is comprised of input layers, hidden layers and output layers. Data is processed by each layer, and passed on to the next layer.
• Weights and Biases: That is, these are parameters that we adjust during training such that our predictions have as little error as possible. The key differences between the neurons are the weights, which determine he strength of the neurons connection and the biases which shift the output of the activation function to better match the data.
• Activation Functions: Will determine whether a neuron should be activated or not. Some common activation function are ReLU (Rectified Linear Unit) and sigmoid.

The Learning Process: Forward Propagation and Backpropagation are the terms used to describe when an Inference is made using Deep Learning.

• Forward Propagation: The data is passed from the input layer, through the hidden layers, and output layer of the neural network. In other words, we make predictions or classifications based on the data.
• Backpropagation: The network then, takes its predictions and compares them to reality, calculating the error. We adjust the weights and biases via backpropagation to make the error smaller and therefore the model more accurate with time.

One of the most beautiful points of deep learning is that neural networks are capable of learning hierarchical representation of data, i.e., neural networks can understand complex features in terms of simpler ones in multiple layers.

3. Deep Learning is Crucial to Artificial Intelligence.

It has become increasingly popular for unstructured, complex data as compared to old machine learning algorithms. However, deep learning enables machines to learn tasks that were previously impossible for them to do at or close to human level of accuracy.

Comparison Between Traditional Machine Learning and Deep Learning:

• Feature Extraction: Features must usually be hand extracted from the data in traditional machine learning by domain experts. In contrast, automatic learning of relevant features in deep learning is done through its neural networks.
• Scalability: Traditional methods may find it hard to handle the hugeness and complexity of data, but deep learning does excel with big data.
• Performance: Deep learning works well because it's very good at finding complicated patterns, doing so significantly better than other models when testing out on something like image classification or speech recognition.

4. Applications of Deep Learning

The flexibility and power of Deep Learning have led to people adopting it in many different industries. Here are some of the most significant applications:

1. Natural Language Processing (NLP):

How Human Language Is Processed And Understood By Machines Is Seeing Revolution Thanks To Deep Learning. Examples of deep learning models like Recurrent Neural Networks (RNNs) and Transformers are widely utilized for applications such as chatbots, language translation, and sentiment analysis.

Example: Deep learning is how Google Translate makes translations more accurate and more fluent.

2. Computer Vision:

Deep learning algorithms, more specifically convolution neural networks (CNNs), are used in computer vision for goals such as facial recognition, object detection, and image classification. However, these networks operate differently, analysing visual data by layering them with parts that enable them to pick out objects with amazing accuracy.

• Example: Deep learning helps Facebook automatically tag people in the photos, recognizing their faces.

3. Healthcare:

In medical image analysis, prediction of disease and even drug discovery, deep learning is finding applications in healthcare. AI, for instance, can now recognize early symptoms of cancer from just CT scans helping doctors give better diagnoses.

• Example: An AI system so good it can detect one of more than 50 eye diseases in retinal scans, nearly as good as a human, was developed by Google DeepMind.

4. Speech Recognition:

Deep learning algorithms are used by voice assistants like Siri, Alexa and Google Assistant to turn speech to text and recognize the user commands. As a result, these systems keep getting better and better at understanding speech patterns, accents and languages.

• Example: Deep learning helps Amazon Alexa know what voice command was made and what should be the response.

5. Types of Neural Networks in Deep Learning #AI #MachineLearning #ArtificialIntelligence

To address these specific challenges in deep learning, different types of neural networks are utilized. Here are some of the most common types:

1. Feedforward Neural Networks (FNN):

• A neural network that has the simplest form of data flow (one direction only: input to output) without having data flowing in loop.
• Image and text classification tasks like it are used.

2. Convolutional Neural Networks (CNN):

• CNNs are designed to process convolutional layers in transporting image data and to recognize patterns in images. Identify visual patterns, and they are very efficient about it.
• It is used in applications of image recognition, self driving cars and object detection.

3. Recurrent Neural Networks (RNN):

• Created to deal with a sequence of data as it comes such as text or time series data. RNNs are made up of a memory component which lets them remember information from inputs before, meaning they are great for NLP and speech recognition.
• The applications include language modeling, machine translation and chatbot development.

4. Generative Adversarial Networks (GANs):

• GANs consist of two neural networks: one is a generator that produces data (G), and another is a discriminator that assesses the data (D). They create realistic images, videos and even music and they’re used in creative fields.
• Example: We use GANs to generate hyper realistic fake images (deepfakes), or to increase the resolution of imagery.

Conclusion

Over the last few years, deep learning has revolutionized AI to such a degree that today machines can do the impossible — tasks that only we humans were believed to be capable of. But, as the technology of deep learning, based in neural networks, strives to achieve these heights, the technology takes us closer to what was considered science fiction just a decade ago, from autonomous systems to healthcare.

As deep learning technology advances we'll be left with reshaped industries and new ways of interacting with the world. Deep learning is inseparable from what's going on with AI, whether that's with voice assistants, self driving cars, or advanced medical diagnostics going forward.