DIFFERENCE BETWEEN MACHINE LEARNING AND DEEP LEARNING

Author: Ms Himani Garg

INTRODUCTION

Artificial intelligence is the overarching system. Machine learning is a subset of AI. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. Machine learning and deep learning are both subfields of artificial intelligence (AI) that focus on creating algorithms and models that can learn from data. However, they differ in terms of their scope, techniques, and the types of problems they are best suited for. Here are the key differences between machine learning and deep learning:

Meaning of Machine Learning (ML): Machine learning is a broader field that encompasses a variety of techniques for data analysis and model building. It involves training algorithms on large datasets to identify patterns and relationships and then using these patterns to make predictions or decisions about new data. It includes both traditional statistical methods and modern algorithms for making predictions and decisions based on data.

Meaning of Deep Learning (DL): Deep learning is a subset of machine learning that specifically deals with artificial neural networks, which are inspired by the structure and function of the human brain. Deep learning focuses on learning representations of data through neural networks with multiple layers (deep neural networks).

TYPES 

Here are the main types of machine learning and deep learning:

TYPES of Machine Learning:

  1. Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where each data point has an associated target or label. The goal is to learn a mapping from input data to output labels, allowing the model to make predictions on new, unseen data. Ex- Spam detection, Customer sentiment analysis.
  2. Unsupervised Learning: Unsupervised learning deals with unlabeled data. The goal is to find patterns, relationships, or structures within the data. Common techniques include clustering (grouping similar data points) and dimensionality reduction (reducing the number of features while preserving important information). Ex- Market Basket Analysis, Delivery Store Optimization, Identifying Accident Prone Areas.
  3. Reinforcement Learning: In reinforcement learning, agents learn how to make a sequence of decisions to maximize a cumulative reward. It involves an agent interacting with an environment and learning from the consequences of its actions.  Some examples of reinforcement learning in image processing include:
  • Robots equipped with visual sensors to learn their surrounding environment
  • Scanners to understand and interpret text
  • Image pre-processing and segmentation of medical images, like CT scans
  • Traffic analysis and real-time road processing by video segmentation and frame-by-frame image processing
  • CCTV cameras for traffic and crowd analytics

TYPES of Deep Learning:

  1. Feedforward Neural Networks (FNN): These are traditional artificial neural networks composed of input, hidden, and output layers. They are used for various tasks, such as image classification and regression. Ex- image classification, object detection, natural language processing, and machine translation.
  2. Convolutional Neural Networks (CNN): CNNs are designed for processing grid-like data, such as images and videos. They use convolutional layers to automatically learn spatial hierarchies of features, making them well-suited for computer vision tasks. Examples of CNN in computer vision are face recognition, image classification, etc. It is similar to the basic neural network.
  3. Long Short-Term Memory (LSTM) Networks: LSTMs are a type of RNN designed to overcome the vanishing gradient problem, making them better at handling long sequences and capturing long-term dependencies. LSTMs have been used for speech recognition tasks such as transcribing speech to text and recognizing spoken commands.
  4. Gated Recurrent Unit (GRU) Networks: GRUs are another type of RNN similar to LSTMs but with a simpler architecture, making them computationally more efficient in some cases. Recurrent neural networks (RNNs) are the state-of-the-art algorithm for sequential data and are used by Apple’s Siri and Google’s voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data. It is one of the algorithms behind the scenes of the amazing achievements seen in deep learning over the past few years. 

But when do you need to use an RNN?

“Whenever there is a sequence of data and that temporal dynamics that connects the data is more important than the spatial content of each individual frame.” – Lex Fridman (MIT)

  1. Transformers: Transformers are a class of deep learning models that have gained popularity for natural language processing tasks. Most applications of transformer neural networks are in the area of natural language processing. They use a self-attention mechanism to process input data in parallel and have been used in models like BERT and GPT.

Differences 

  1. In traditional machine learning, models are typically based on handcrafted features extracted from the data. These features are then used as inputs to various algorithms, such as decision trees, support vector machines, and random forests, to make predictions or classifications. Deep learning models, on the other hand, automatically learn hierarchical features from raw data through multiple layers of artificial neurons (neural networks). These models are capable of automatically discovering complex patterns and representations within the data, making them well-suited for tasks involving large amounts of unstructured data, such as images, text, and speech.
  1. Traditional machine learning often requires feature engineering, where domain experts manually design and select relevant features from the data. The quality of features plays a crucial role in the performance of machine learning models.Deep learning models can work with raw data directly, which reduces the need for extensive feature engineering. They excel in scenarios where the data is high-dimensional and rich, as they can learn relevant features on their own.
  2. Training machine learning models typically involves optimizing model parameters (e.g., weights in linear regression) using various optimization algorithms. The training process often requires tuning hyperparameters to achieve good performance.Deep learning models have a more complex architecture with many parameters, making training computationally intensive. Deep learning models also require large amounts of labeled data for effective training.
  3. Machine learning is suitable for a wide range of applications, including but not limited to linear regression, classification, clustering, and recommendation systems. It works well when the relationships between features and outcomes are not highly complex.Deep learning excels in tasks involving complex and unstructured data, such as image recognition, natural language processing, speech recognition, and autonomous driving. It has achieved state-of-the-art results in these domains.

Conclusion

Deep learning is a specialized subfield of machine learning that focuses on neural networks with multiple layers, particularly well-suited for tasks involving large amounts of complex, unstructured data. Traditional machine learning, on the other hand, covers a broader range of techniques and is often used for tasks with simpler data representations and fewer parameters. The choice between machine learning and deep learning depends on the specific problem and the nature of the available data.

REFERENCES