Deep learning is an important part of machine learning, which enables computers to learn from data similar to how the human brain works. It uses neural networks to perform different tasks such as regression, classification, and representation.
It is known as deep learning because it contains many hidden layers, and the more layers in the model, the better it can interpret patterns and solve them. Here, let us learn more about deep learning, its architecture, and how it works.
What is Deep learning?
Deep Learning is a special type of machine learning that uses an artificial neural network to learn easily from data, much similar to how human brains see and learn. It can be used to solve a variety of problems, ranging from complex natural language processing, image recognition, speech recognition, and more.
- Deep learning algorithms teach machines to perform tasks from examples, much similar to how humans do.
- The machines learn to find the common patterns and recognize the entity all by themselves. Such as a computer can now identify a cat easily as they are being trained using deep learning algorithms.
- Deep learning uses neural networks consisting of layers of interconnected nodes, like the human brain, which consists of multiple layers, allowing them to perform much complex tasks on their own.
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The Evolution of Deep Learning
The onset of deep learning began with machine learning, which is a subset of artificial intelligence enabling computers or other machines to learn from available data and make decisions without having to conduct specific programming.
While machine learning is considered to be one level down that deep learning in its ability to handle complex problems on its own without human intervention. Deep learning is a part of machine learning, but it is different as it deals with neural networks having multiple layers.
- Deep learning models can easily handle structured as well as unstructured data on their own, reducing the need for resources.
- It can easily handle large data due to the support of Graphics Processing Units (GPUs), which can process a large amount of data at a time.
- It offers higher accuracy in complex tasks like audio processing, NLPs, computer vision, and more.
- Deep learning can easily detect any kind of pattern on its own without the assistance of any other techniques.
Core Architecture of Deep Learning
Let us check some of the major core components of Deep learning that make it work in the advanced intelligence world.
1. Neural Network
Neural networks form the foundation of deep learning and are inspired by the functioning of the human brain. They consist of a large number of interconnected nodes called neurons. These neurons are used to pass information between one another.
Each neuron performs a simple, consistent operation, but when there are many of these connected in layers, they can process complex patterns in data much similar to how our brain works. Neural networks tend to learn by adjusting the “weight” of connections between neurons, strengthening the important ones and weakening the rest. This improves accuracy and helps them make better decisions.
2. Deep Neural Network
This part of deep learning contains DNNs, which contain multiple hidden layers between the input and output layers. The better the model understands the complex representations, the better its network is.
The more layers a model has, the more complex operations it can solve. Deep learning has the ability to learn hierarchical features, making it a strong technique for solving and understanding complex patterns.
3. Activation Functions
The activation function in deep learning is used to make a decision, and it is used as a condition to decide whether a neuron should be activated and the extent of its influence on the next layer. There are different types of activation functions
- Sigmoid
- Tanh
- ReLU (Rectified Linear Unit)
How Deep Learning Works?
Deep learning uses artificial neural networks similar to those inspired by the human brain. It makes use of different elements that make the entire system possible.
- The neurons act as a building block of deep learning models, where they take some inputs, multiply them by the weights, add bias, and then apply the result in the activation function, which later produces a valid output.
- Deep learning uses multiple layers of neurons, which can receive data in the input layer and also keep hidden layers.
- The output layer makes the final prediction, for example, whether the data contains the image of a cat, or more.
Training Process in Deep Learning
Check the step by step method on how training takes place in deep learning models.
- First, the data starts to move from the input layer to the output layer, where networks make predictions.
- Now, the prediction is compared with the correct answer.
- The network goes through backpropagation and adjusts the weights and biases to reduce errors in the future.
- This happens repeatedly to reduce errors to a minimum.
Types of Deep Neural Networks
Let us check some of the major types of deep neural networks given below.
1. Convolutional Neural Networks (CNNs)
This deep learning model is used for image processing and recognition as they are specifically powerful and capable of detecting any kind of visual patterns, such as edges, colors, textures, and objects. They work by sliding filters over images to extract important features.
- They can identify objects
- CNNs can automatically learn features from images
- They work best for face detection, object detection, medical imaging, and more.
- They are used for diagnosing diseases from MRIs, X-rays, and facial recognition systems.
- They are also used in self driving car vision systems.
2. Deep Reinforcement Learning
This model combines neural networks with reinforcement learning principles of advanced intelligence models.
- The model learns by experience it goes through just like humans.
- They can discover strategies without being explicitly programmed for the same.
- They are used in robotics, games, self-driving cars, and smart resource allocation systems.
- They work well for sequential decision making
Read More: Deep Learning in AI: How It Works, Who’s Using It, Why Is It Important?
3. Recurrent Neural Network (RNNs)
The Recurrent Neural Network is used for sequence-based data, such as text, speech, and time series data. It processes information one step at a time while taking into account the previous inputs, which helps in understanding different contexts.
- They can be used to analyse audio streams and sequences over time.
- They are capable of generating texts, predicting future values, translating languages, and more.
- They are used for speech recognition, text generation, weather prediction, and more.
- They are also used for language translation.
Deep Learning vs Machine Learning: Comparison Table
Let us check a quick table between deep learning and machine learning.
| Machine Learning (ML) | Deep Learning (DL) |
| Machine learning uses algorithms that can learn patterns from data | Deep learning uses multi-layered neural networks to learn complex patterns |
| It works well with small to medium datasets | It even supports large datasets to perform well with higher accuracy. |
| It requires manual feature selection. | It offers feature extraction for better automation. |
| It is very fast | It is comparatively slow, depending on model size |
| It can run on a CPU (Central Processing Unit) and does not require external heavy resources. | Deep learning tasks often require a GPU or a TPU for the best performance. |
| Machine learning models are good for simple to moderate tasks | It works excellently for highly complex tasks, including vision, NLP, and speech |
| It is more explainable | It is harder to interpret the often-called black box. |
| The machine learning performance delivered is good. | It provides great accuracy. |
| SVM, Decision Trees, Random Forests, KNN, Linear Regression | CNNs, RNNs, LSTMs, Transformers |
| It can solve real-world problems like Fraud detection, simple predictions, and small datasets | It works well for problems like Self-driving cars, ChatGPT, face recognition, and medical imaging |
Applications of Deep Learning
Let us check some of the major applications of deep learning below.
- Speech recognition: Deep learning models are often used to convert spoken language into text. It is used in voice assistants like Siri, Google Assistant, and automated transcription tools.
- Autonomous Vehicles: Deep learning algorithms power up self-driving cars, which can be used to detect lanes, pedestrians, traffic signs, and obstacles through real-time camera and sensor data.
- Healthcare: Deep learning can be used to analyse X-rays, CT scans, and more to find diseases or infections accurately.
- Recommendation Systems: These models are capable of suggesting movies, products, music, and ads on platforms, such as Netflix, Amazon, and YouTube, based on how users interact on these platforms.
- Fraud Detection: The deep learning algorithms can be used to identify unusual patterns in transactions, detect fake accounts, prevent fraud, and enhance security systems.
- Language Translation: It can be used to translate texts between languages in real time, making use of neural translation systems.
- Sentiment Analysis: Deep learning models can detect different kinds of emotions in texts used for customer feedback analysis and social media monitoring.
Deep Learning FAQs
Q1. What is deep learning?
Ans: Deep Learning is a special type of machine learning that uses an artificial neural network to learn easily from data, much similar to how human brains see and learn.
Q2. Which is better: Deep learning or machine learning?
Ans: Deep learning is often better when the task is complex or large tasks, such as image recognition, natural language processing, speech recognition, self-driving cars, and more. You might consider machine learning when you have a small dataset and need faster training and easier implementation.
Q3. When should I use deep learning models?
Ans: Deep learning models are best suited for complex and large-scale problems, for which you will need additional resources like a GPU, a TPU, and more. It can be used to handle tasks like image recognition, natural language processing, speech recognition, self-driving cars, and more.
Q4. Is ChatGPT a deep learning model?
Ans: Yes, ChatGPT is a deep learning model which can be considered as a Large Language Model (LLM) working based on a Generative Pre-trained transformer architecture capable of generating human like texts and responses.
