Basics of Deep Learning for Beginners
Deep Learning (DL) is a subset of Machine Learning (ML) and AI in general, using structures known as artificial neural networks. These layers become progressively more complex, enabling DL models to learn from, and abstract, data at various levels of representation. It is this multilayered learning process that gives the field its name, 'Deep Learning,' and distinguishes it from the broader realm of ML research.
In today’s digital world, DL has become incredibly common whether most people know it or not. We see it everywhere from services like Siri and Netflix streaming recommendations or more advanced functions like image recognition and self-driving cars. So, what is deep learning and why is it so important in the field of AI?
The Concept Behind Deep Learning
Deep Learning, at its core, utilizes a specific type of algorithm known as an artificial neural network (ANN). This network is composed of several layers of nodes, each performing a unique computation. Moving deeper through the layers, these computations become increasingly complex.
In an ANN, the first layer processes the initial input data and passes the results to the next layer. As the data progresses through the network, it's manipulated and refined at each stage. With every successive layer, the nodes in the network learn more about the data, identifying complex patterns and uncovering new insights. As this information travels deeper into the network, the DL model continues to learn from this evolving data and is eventually capable of making its own accurate predictions and decisions.
Similar to the human brain, which learns from experience through neurons, the nodes in an ANN enable Deep Learning models to learn from processing vast amounts of data. Although these models can function independently once they're trained, they're still heavily dependent on the quality and quantity of the input data they receive, requiring ongoing updates and maintenance.
Types of Deep Learning Models
Within DL, there are various models and algorithms that can be used to help machines comprehend input data and enhance their learning capabilities using layers of computation and non-linear transformations that can be combined for optimal results and experimentation. Some common examples include:
Convolutional Neural Networks (CNNs): DL model that uses a mathematical operation called convolution which places a matrix over grid-like data to filter inputs that can be used for image and video recognition.
Recurrent Neural Networks (RNNs): An algorithm that has looping connections for nodes to send information in a continuous stream in order to process sequential data, creating a memory to assist with language recognition.
Autoencoders: An unsupervised learning model that uses encoders and decoders to compress data that the machine then reconstructs using only the most important information.
Applications of Deep Learning
Deep Learning influences many aspects of our everyday lives, largely due to its ability to process and analyze vast amounts of data. As our digital universe continues to expand, so does the potential for DL applications across numerous fields:
Healthcare: Deep Learning has revolutionized healthcare, providing tools to support medical professionals in diagnosing diseases, predicting disease progression, and formulating personalized treatment plans.
Autonomous Vehicles: The advent of self-driving cars has been greatly facilitated by DL. Sophisticated algorithms, particularly CNNs, process real-time data from sensors and cameras, enabling these vehicles to recognize traffic signs, other vehicles, pedestrians, and various road features.
Social Media: On social media platforms, DL plays a significant role in content moderation, recommendation systems, and user engagement. Algorithms can auto-tag images, suggest friends or pages to follow, and curate feeds based on individual user preferences.
Tools and Libraries for Deep Learning
For developers looking to explore Deep Learning, there are a variety of tools and languages that can be used to build models and explore the field of artificial intelligence. Languages like Python and R are some of the most common ways to program DL models and offer expansive libraries and frameworks created by their communities including TensorFlow, PyTorch, and Keras.
With AI and Deep Learning showing no signs of slowing down, now is the best time to learn these languages and put them into practice if you want to become more familiar with the basics of Deep Learning and the models/algorithms being used today.
