Artificial Intelligence Difference Between Machine Learning VS Deep Learning

Deep learning is a subset of machine learning. Machine learning is the science of getting computers to act without being explicitly programmed. Deep learning is a type of machine learning that uses neural networks to learn from data.

Understanding the Hierarchy of Intelligent Systems

In order to understand the difference between machine learning and deep learning, we first need to understand the common links and the overall hierarchical structure of this niche.

1. Artificial Intelligence (AI):
   • AI is a broad field of study focused on creating intelligent systems that can perform tasks that typically require human intelligence.
   • AI encompasses various approaches, techniques, and methodologies to simulate or replicate human-like intelligence in machines.
2. Machine Learning (ML):
   • Machine learning is a subfield of AI that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions without being explicitly programmed.
   • ML algorithms learn patterns from data through training and use those patterns to make predictions or take actions on new, unseen data.
3. Neural Networks:
   • Neural networks are a computational model inspired by the structure and functioning of the human brain.
   • Neural networks consist of interconnected nodes (neurons) organized into layers.
   • Each neuron receives inputs, applies weights and biases, and produces an output based on an activation function.
   • Neural networks can approximate complex functions and learn representations by adjusting the weights and biases through a process called training.
4. Deep Learning:
   • Deep learning is a subset of neural networks that focuses on models with multiple layers (deep neural networks).
   • Deep learning architectures enable the learning of increasingly abstract and high-level representations from raw data.
   • Deep learning models automatically learn relevant features from the data, eliminating the need for manual feature engineering.
   • Deep learning has demonstrated remarkable success in tasks such as image recognition, natural language processing, and speech recognition.

Artificial intelligence Vs Machine Learning Vs Neural Networks Vs Deep Learning

Here are the main differences:

	Artificial Intelligence  (AI)	Machine Learning (ML)	Neural Networks	Deep Learning
Definition	Broad field of creating intelligent systems that mimic human intelligence.	Subset of AI that focuses on algorithms enabling machines to learn patterns from data.	Computational models inspired by the structure and functioning of the human brain.	Subset of neural networks using multiple layers to learn hierarchical representations.
Goal	Create intelligent systems that can perform tasks requiring human-like intelligence.	Develop algorithms that learn from data to make predictions or decisions.	Simulate the behavior of the human brain to solve problems.	Learn intricate patterns in data to make accurate predictions or classifications.
Approach	Various techniques and methodologies to replicate human-like intelligence.	Utilizes algorithms to learn from data, make predictions, or take actions.	Computational models composed of interconnected nodes (neurons).	Deep neural networks with multiple layers to automatically learn complex representations.
Dependency on Data	Can operate based on pre-programmed rules or leverage data for learning.	Heavily relies on data for training models and making predictions.	Requires labeled data for training to establish relationships between inputs and outputs.	Relies on large amounts of data for training to learn complex patterns and features.
Human Intervention	Can function with or without human intervention, can operate autonomously.	Requires human intervention for feature selection, model design, and evaluation.	Human intervention needed for network architecture design and parameter tuning.	Human intervention needed for model design, architecture selection, and parameter tuning.
Flexibility and Adaptability	Can exhibit flexibility and adaptability to handle new scenarios.	Can adapt and improve performance based on new data and model updates.	Can adapt and adjust weights and biases during training to improve performance.	Can adapt and learn high-level representations from data through multiple layers.
Application Areas	Robotics, natural language processing, computer vision, recommendation systems, etc.	Image and speech recognition, natural language processing, fraud detection, etc.	Image recognition, speech processing, pattern recognition, etc.	Image and speech recognition, natural language processing, autonomous vehicles, etc.
Relationship	AI is the broad field encompassing ML, neural networks, and other approaches.	ML is a subset of AI that utilizes algorithms to enable machines to learn from data.	Neural networks are computational models used in ML and AI to solve problems.	Deep learning is a subset of neural networks focused on learning complex representations.
Example	Chatbots that engage in human-like conversations.	Predicting house prices based on historical sales data.	Handwritten digit recognition using the MNIST dataset.	Self-driving cars identifying and classifying objects on the road.

What are Neural Networks?

Neural networks are inspired by the structure of the brain and nervous system, and they’re designed to process information in a way that’s similar to how our brains do it. The basic idea behind neural networks is that you have a large number of simple processing units (neurons) that are connected together in complex ways. Each neuron has an output value, which is determined by applying some function to its input values. The output value can then be used as input for other neurons, allowing the network as a whole to process information in a way that’s similar to how our brains do it.

What is Machine Learning?

Generally, when a computer learns from data, it is called machine learning. Machine learning stands at the intersection of statistical algorithms and computer science fundamentals. The algorithms are designed to complete certain tasks without hardcore programming. Thus, machine learning programs can detect data patterns and make predictions as to what the new data will look like.

The machine learning process for algorithms can either be supervised or unsupervised. The extent of supervision depends upon the data volume that algorithms receive. Here is a brief summary of what is machine learning:

• Machine Learning stands at the junction between computer science and statistics. In this technology, computers become able to learn and detect data patterns without the need for specific programming
• Machine Learning has two broad categories: Supervised and unsupervised machine learning
• OLS regression is a simple example of machine learning. 

What are the Machine Learning Steps? 

Define the Problem: 

Clearly understand and define the problem you want to solve using machine learning. Identify the question you want to answer or the task you want the machine to perform.

Gather Data: 

Collect relevant data that is necessary to solve the problem. The data should include both input features (attributes) and corresponding output labels or targets (if available).

Prepare the Data: 

Clean and preprocess the data to ensure it is in a suitable format for machine learning algorithms. This involves handling missing values, removing outliers, and transforming the data if needed (e.g., scaling or normalization).

Choose a Model: 

Select an appropriate machine learning model that matches your problem and data characteristics. Consider factors such as the type of problem (classification, regression, clustering), the size of the dataset, and the complexity of the relationships within the data.

Split the Data: 

Divide the dataset into two or three parts: training set, validation set, and test set. The training set is used to train the model, the validation set helps in tuning the model’s performance, and the test set is used to evaluate the final model’s accuracy.

Train the Model:

Feed the training data into the selected model and let it learn the patterns and relationships between the input features and output labels. The model adjusts its internal parameters to minimize errors and improve its predictions.

Validate and Tune: 

Assess the model’s performance on the validation set. Fine-tune the model’s hyperparameters (settings that control the learning process) to optimize its performance. This involves experimenting with different parameter values and selecting the best combination.

Evaluate the Model: 

Use the test set, which contains unseen data, to evaluate the final performance of the trained model. Measure how accurately the model predicts the output labels compared to the actual labels. This step helps assess the model’s generalization capabilities.

Make Predictions: 

Once the model has been evaluated and deemed satisfactory, it can be deployed to make predictions on new, unseen data. Provide the model with new input features, and it will generate predictions or outputs based on what it has learned during training.

Monitor and Improve: 

Continuously monitor the model’s performance in the real-world scenario. Collect feedback and new data to periodically retrain and improve the model as needed. This step ensures that the model stays accurate and effective over time.

What are Different Types of Machine Learning?

There are several machine learning types, including:

Supervised Learning:

In supervised learning, the algorithm is trained on labeled data, where each data point is associated with a corresponding target or output value. The algorithm learns to map input features to the desired output based on the provided labeled examples.

Unsupervised Learning:

Unsupervised learning involves training algorithms on unlabeled data. The algorithm explores the data to discover patterns, structures, and relationships without specific target labels. Clustering and dimensionality reduction are common tasks in unsupervised learning.

Semi-supervised Learning:

Semi-supervised learning is a combination of supervised and unsupervised learning. It uses a small amount of labeled data along with a larger amount of unlabeled data for training. This approach can be useful when acquiring labeled data is expensive or time-consuming.

Reinforcement Learning:

Reinforcement learning involves an agent that learns to make decisions and take actions in an environment to maximize a reward signal. The agent interacts with the environment and learns through a trial-and-error process, receiving feedback as rewards or penalties.

Transfer Learning:

Transfer learning aims to leverage knowledge gained from one task or domain to improve learning and performance on a different but related task or domain. Pre-trained models on large-scale datasets are often used as a starting point to transfer learned features or knowledge to new tasks.

Deep Learning:

Deep learning is a subfield of machine learning that focuses on artificial neural networks with multiple layers. These deep neural networks can automatically learn hierarchical representations from complex data, enabling them to excel in tasks like image and speech recognition and natural language processing.

Online Learning:

Online learning, or incremental or streaming learning, involves training models on a continuous stream of incoming data. The model updates its parameters incrementally as new data arrives, allowing it to adapt and learn from changing environments.

Ensemble Learning:

Ensemble learning combines multiple models or algorithms to make predictions or decisions. Combining multiple models’ outputs, ensemble methods aim to improve accuracy, reduce bias, and increase generalization capabilities.

Bayesian Learning:

Bayesian learning applies probabilistic principles to make predictions or decisions. It incorporates prior knowledge and updates beliefs based on observed evidence using Bayes’ theorem, providing a framework for reasoning under uncertainty.

Active Learning:

Active learning involves an iterative process where a learning algorithm actively selects and queries the most informative or uncertain samples from a pool of unlabeled data to be labeled by an oracle (usually a human expert). This approach aims to minimize the labeling effort while maximizing the learning performance.

What is Deep Learning?

As discussed above, Deep Learning is one of the machine learning types. Deep Ai Learning is a subset of machine learning. It means that deep learning algorithms are more complex and sophisticated as compared to machine learning. Deep learning has shown great results in different applications, which is why this technology is gaining a lot of popularity recently. 

Deep learning algorithms possess the ability for data analysis with a logical structure. It means that deep learning programs can deduce conclusions logically just like humans. Like machine learning, deep learning problems can also be supervised or unsupervised. Artificial Neural networks are layered structures of algorithms which make up the deep learning applications. Just like the human brain and its neural connections, the ANN design comprises biological patterns. Thus, deep learning is much more capable of drawing conclusions logically as compared to its machine learning counterparts.

Deep learning is essentially machine learning – just more evolved and more capable of making logical conclusions.

Deep Learning Vs Neural Networks – Key Differences

	Neural Networks	Deep Learning
Architecture	Consist of interconnected nodes organized into layers	Subset of neural networks with multiple layers
Learning Capability	Capable of learning and adapting to patterns in data	Excels at automatically learning hierarchical representations
Feature Extraction	Manual feature engineering is often required	Automatically learns relevant features from raw data
Complexity	Relatively simple with fewer layers and neurons	More complex with deeper architectures and millions of parameters
Performance	Good performance on many tasks; may require manual feature engineering	Outstanding performance, especially in complex tasks like image recognition and natural language processing
Data Requirements	Requires sufficient labeled training data for good generalization	Requires more data, but can leverage large-scale datasets and benefit from transfer learning
Training Time	Relatively quick, especially for smaller networks	Requires substantial computational resources and longer training times
Interpretability	Relatively interpretable, but becomes challenging as network size grows	Often considered as black boxes, interpretation remains a challenge

So, What’s the difference between Machine Learning Vs Deep Learning?

The key difference between deep learning and traditional machine learning methods is that deep learning models use multiple layers of neurons, whereas traditional machine learning models typically only use one layer. This allows deep learning models to represent more complex functions than traditional models can, which makes them better suited for many types of problems (such as image recognition).

Deep convolutional neural networks (CNNs) are one type of deep neural network model that’s commonly used for computer vision tasks such as image classification and object detection. CNNs consist of multiple layers: an input layer, one or more “convolutional” layers, and an output layer. The input layer receives an image as input, and the output layer produces a set of class scores (one score for each class) that indicate the probability that the image belongs to each class.

The convolutional layers apply a series of small filters to the input image, which allows them to extract more complex features from the image than traditional neural networks can. The output layer then uses these features to produce the final classification scores.

Conclusion

In summary, AI encompasses creating intelligent systems, while machine learning enables machines to learn from data. Neural networks simulate the brain’s behavior, and deep learning uses multi-layered neural networks for complex learning. These concepts rely on data, automate tasks, and have practical applications in various fields. They represent the forefront of advanced AI learning.