Artificial Intelligence (AI) and Machine Learning (ML) are pivotal to the forthcoming computing revolution. Central to these technologies is their capacity to discern patterns and use historical data to forecast future events. This capability underpins the personalized recommendations you receive while shopping on Amazon or how Netflix accurately predicts your fondness for quirky 80s movies. While AI-powered systems are often described as "smart," they typically do not learn independently; they require substantial human intervention. Data scientists meticulously prepare the inputs and choose the variables for predictive analysis. However, deep learning, a subset of machine learning, automates this process, enabling more sophisticated and self-sufficient learning mechanisms.
Now, as you have clearly understood what is Deep Learning, and want to step up in this cutting-edge technology, you must know the career prospects.
What Is Deep Learning?
Deep learning can be considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with artificial neural networks, which are designed to imitate how humans think and learn. Until recently, neural networks were limited by computing power and thus were limited in complexity. However, advancements in Big Data analytics have permitted larger, sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans. Deep learning has aided image classification, language translation, speech recognition. It can be used to solve any pattern recognition problem and without human intervention.
Artificial neural networks, comprising many layers, drive deep learning. Deep Neural Networks (DNNs) are such types of networks where each layer can perform complex operations such as representation and abstraction that make sense of images, sound, and text. Considered the fastest-growing field in machine learning, deep learning represents a truly disruptive digital technology, and it is being used by increasingly more companies to create new business models.
Deep Learning vs. Machine Learning
Here's a table outlining 15 differences between Deep Learning and Machine Learning:
|
Aspect |
Machine Learning |
Deep Learning |
|
Data Dependency |
Requires less data to train effectively. |
Needs large amounts of data to train effectively. |
|
Hardware Requirements |
Generally less demanding; can work on low-end machines. |
Requires high-end hardware (especially GPUs) due to its computational complexity. |
|
Interpretability |
Often more interpretable due to simpler models. |
Less interpretable because of complex model architectures. |
|
Feature Engineering |
Requires manual intervention for feature extraction and selection. |
Learns features automatically, minimizing the need for manual feature engineering. |
|
Training Time |
Typically faster to train than deep learning models. |
Requires longer training times due to more complex architectures. |
|
Model Complexity |
Utilizes simpler algorithms like linear regression, decision trees, etc. |
Uses complex neural networks with multiple layers. |
|
Application Scope |
Well-suited for small to medium-sized data sets and simpler problems. |
Excelling in areas with substantial data and complex problems like image and speech recognition. |
|
Output Interpretation |
Outputs are generally in the form of numerical values, labels, or simple categories. |
Outputs can be more complex, like entire new images or sequences of text. |
|
Real-time Learning |
More feasible in machine learning with models that require less computational power. |
Less feasible due to the heavy computational requirements. |
|
Algorithm Variability |
Involves a variety of algorithms that can be applied depending on the type and structure of the data. |
Primarily revolves around different architectures of deep neural networks. |
|
Human Intervention |
More dependent on human expertise for setting up models and choosing the right algorithms. |
Less human intervention in processing raw data but requires careful network architecture design. |
|
Software Libraries |
Libraries like Scikit-learn, WEKA are commonly used. |
Libraries like TensorFlow, Keras, and PyTorch are more tailored to deep learning. |
|
Problem-Solving Approach |
Approaches problems with traditional algorithms that may or may not involve iterative learning. |
Approaches problems through layers of abstraction, learning from vast amounts of data. |
|
Success with Unstructured Data |
Less effective with unstructured data if carefully pre-processed. |
Highly effective with unstructured data like text, images, and audio. |
|
Update and Re-training |
Easier and quicker to update and retrain with new data. |
More complex and time-consuming to update and retrain models with new data. |
How Does Deep Learning Work?
Neural networks are layers of nodes, much like the human brain is made up of neurons. Nodes within individual layers are connected to adjacent layers. The network is said to be deeper based on the number of layers it has. A single neuron in the human brain receives thousands of signals from other neurons. In an artificial neural network, signals travel between nodes and assign corresponding weights. A heavier weighted node will exert more effect on the next layer of nodes. The final layer compiles the weighted inputs to produce an output. Deep learning systems require powerful hardware because they have a large amount of data being processed and involves several complex mathematical calculations. Even with such advanced hardware, however, training a neural network can take weeks.
Deep learning systems require large amounts of data to return accurate results; accordingly, information is fed as huge data sets. When processing the data, artificial neural networks are able to classify data with the answers received from a series of binary true or false questions involving highly complex mathematical calculations. For example, a facial recognition program works by learning to detect and recognize edges and lines of faces, then more significant parts of the faces, and, finally, the overall representations of faces. Over time, the program trains itself, and the probability of correct answers increases. In this case, the facial recognition program will accurately identify faces with time.
Example of Deep Learning at Work
Let’s say the goal is to have a neural network recognize photos that contain a dog. All dogs don’t look exactly alike – consider a Rottweiler and a Poodle, for instance. Furthermore, photos show dogs at different angles and with varying amounts of light and shadow. So, a training set of images must be compiled, including many examples of dog faces which any person would label as “dog,” and pictures of objects that aren’t dogs, labeled (as one might expect), “not dog.” The images, fed into the neural network, are converted into data. These data move through the network, and various nodes assign weights to different elements. The final output layer compiles the seemingly disconnected information – furry, has a snout, has four legs, etc. – and delivers the output: dog.
Now, this answer received from the neural network will be compared to the human-generated label. If there is a match, then the output is confirmed. If not, the neural network notes the error and adjusts the weightings. The neural network tries to improve its dog-recognition skills by repeatedly adjusting its weights over and over again. This training technique is called supervised learning, which occurs even when the neural networks are not explicitly told what "makes" a dog. They must recognize patterns in data over time and learn on their own.
After learning what is Deep Learning, and understanding the principles of its working, let's go a little back and see the rise of Deep Learning.
Applications of Deep Learning
Deep Learning has found applications across various fields and industries due to its ability to process and learn from large volumes of data. Here are some key applications:
- Image Recognition: Deep learning algorithms are particularly adept at image analysis and are used in applications ranging from medical imaging diagnostics to automated vehicle systems.
- Natural Language Processing (NLP): Tools such as translation services, speech recognition systems, and chatbots employ deep learning to understand and generate human language effectively.
- Autonomous Vehicles: Deep learning powers the computer vision systems and decision-making processes in self-driving cars, enabling them to recognize objects, navigate, and avoid obstacles.
- Facial Recognition: In security systems and smartphones, deep learning algorithms can accurately identify individuals from images and video.
- Healthcare: From diagnosing diseases from X-rays and MRI scans to predicting patient outcomes and personalizing treatments, deep learning revolutionizes healthcare.
- Finance: Deep learning is used for algorithmic trading, fraud detection, customer service, and risk management in the financial sector.
- Recommendation Systems: Companies like Netflix, YouTube, and Spotify use deep learning to analyze user behavior and preferences to recommend movies, videos, or music.
- Robotics: Robots use deep learning to interpret their surroundings and learn to perform complex tasks autonomously.
- Agriculture: Deep learning helps identify plant diseases, pests, and nutrient deficiencies. It also assists in automated harvesting systems.
- Gaming: Deep learning creates more realistic game environments and non-player character (NPC) behaviors that adapt dynamically to player actions.
- Manufacturing: Predictive maintenance, quality control, and supply chain management in manufacturing industries are increasingly relying on deep learning.
- Art and Music: Tools that create art or compose music autonomously often leverage deep learning to analyze existing styles and generate new creations.
- Voice Assistants: Siri, Alexa, and Google Assistant use deep learning to understand natural language and provide relevant answers and actions.
- Weather Forecasting: Deep learning models process data from satellites and sensors to improve accuracy in weather predictions.
- Drug Discovery: Deep learning accelerates identifying potential new drugs and predicting their effectiveness, significantly reducing development time and costs.
Importance of Deep Learning
Deep Learning has become a critical component of the modern technological landscape due to its exceptional ability to extract insights from large and complex datasets. Here are some key points highlighting its importance:
- Handling Complex Data: Deep learning excels at managing and learning from highly complex, large-volume, and unstructured data, such as images, videos, and text. Its ability to automatically extract features from such data without explicit programming makes it invaluable for many applications.
- Improved Accuracy: As more data becomes available, deep learning algorithms improve their accuracy and efficiency, often surpassing human-level performance in tasks like image and speech recognition. This increased accuracy is crucial for applications where precision is critical, such as medical diagnoses and autonomous driving.
- Automation and Efficiency: Deep learning enables significant automation in industries like manufacturing, where it can predict equipment failures, or in retail, where it can manage inventory through predictive analytics. This automation can lead to cost savings, improved efficiency, and enhanced performance.
- Personalization: In consumer applications, deep learning powers personalized experiences in services such as streaming, shopping, and social media. Analyzing user behavior and preferences provides tailored content recommendations, enhancing user engagement and satisfaction.
- Innovation in Products and Services: Deep learning is at the heart of many innovative products and services. For example, it enables realistic real-time translations and voice-activated assistants, transforming how users interact with devices and services.
- Scalability: Deep learning models can be scaled up to handle enormous amounts of data, making them suitable for applications where data volumes and velocities are continuously increasing, such as in big data analytics.
- Advancements in Science and Research: In fields such as genomics and drug discovery, deep learning helps analyze complex biological data and accelerates the pace of research, potentially leading to breakthroughs in treatments and understanding of diseases.
- Enhanced Security: Whether in cybersecurity through anomaly detection or physical security through facial recognition systems, deep learning enhances security protocols and systems, making them more robust against various threats.
- Economic Impact: Deep learning has a significant positive impact on the economy by driving efficiencies, reducing costs, and creating new products and services. It enables businesses to innovate and grow, contributing to economic development.
- Addressing Global Challenges: Deep learning monitors environmental changes, predicts climate patterns, and manages traffic flows to reduce congestion and pollution. Thus, it can help tackle some of the most pressing global challenges.
Rise of Deep Learning
Machine learning is said to have occurred in the 1950s when Alan Turing, a British mathematician, proposed his artificially intelligent “learning machine.” Arthur Samuel wrote the first computer learning program. His program made an IBM computer improve at the game of checkers the longer it played. In the decades that followed, various machine learning techniques came in and out of fashion.
Neural networks were mostly ignored by machine learning researchers, as they were plagued by the ‘local minima’ problem in which weightings incorrectly appeared to give the fewest errors. However, some machine learning techniques like computer vision and facial recognition moved forward. In 2001, a machine learning algorithm called Adaboost was developed to detect faces within an image in real-time. It filtered images through decision sets such as “does the image have a bright spot between dark patches, possibly denoting the bridge of a nose?” When the data moved further down the decision tree, the probability of selecting the right face from an image grew.
Neural networks did not return to favor for several more years when powerful graphics processing units finally entered the market. The new hardware-enabled researchers to use desktop computers instead of supercomputers to run, manipulate, and process images. The most significant leap forward for neural networks happened because of the introduction of substantial amounts of labeled data with ImageNet, a database of millions of labeled images from the Internet. The cumbersome task of manually labeling images was replaced by crowdsourcing, giving networks a virtually unlimited source of training materials. In the years since technology companies have made their deep learning libraries open source. Examples include Google Tensorflow, Facebook open-source modules for Torch, Amazon DSSTNE on GitHub, and Microsoft CNTK.
Deep Learning in Action
Aside from your favorite music streaming service suggesting tunes you might enjoy, how is deep learning impacting people's lives? As it turns out, deep learning is finding its way into applications of all sizes. Anyone using Facebook cannot help but notice that the social platform commonly identifies and tags your friends when you upload new photos. Digital assistants like Siri, Cortana, Alexa, and Google Now use deep learning for natural language processing and speech recognition. Skype translates spoken conversations in real-time. Many email platforms have become adept at identifying spam messages before they even reach the inbox. PayPal has implemented deep learning to prevent fraudulent payments. Apps like CamFind allow users to take a picture of any object and, using mobile visual search technology, discover what the object is.
Google, in particular, is leveraging deep learning to deliver solutions. Google Deepmind’s AlphaGo computer program recently defeated standing champions at the game of Go. DeepMind’s WaveNet can generate speech mimicking human voice that sounds more natural than speech systems presently on the market. Google Translate is using deep learning and image recognition to translate voice and written languages. Google Planet can identify where any photo was taken. Google developed the deep learning software database, Tensorflow, to help produce AI applications.
Deep learning is only in its infancy and, in the decades to come, will transform society. Self-driving cars are being tested worldwide; the complex layer of neural networks is being trained to determine objects to avoid, recognize traffic lights, and know when to adjust speed. Neural networks are becoming adept at forecasting everything from stock prices to the weather. Consider the value of digital assistants who can recommend when to sell shares or when to evacuate ahead of a hurricane. Deep learning applications will even save lives as they develop the ability to design evidence-based treatment plans for medical patients and help detect cancers early.
Now, as you have clearly understood what is Deep Learning, and want to step up in this cutting-edge technology, you must know the career prospects.
Deep Learning Career Prospects
The field of artificial intelligence is seriously understaffed. While not all companies are currently hiring professionals with deep learning skills quite yet, having such trained experts are expected to gradually become a crucial requirement for organizations looking to remain competitive and drive innovation. Machine learning engineers are in high demand because neither data scientists nor software engineers has precisely the skills needed for the field of machine learning. The role of machine learning engineer has evolved to fill the gap. What is deep learning promising in terms of career opportunities and pay? Quite a bit. Glassdoor lists the average salary for a machine learning engineer at nearly $115,000 annually. According to PayScale, the salary range spans $100,000 to $166,000. Growth will accelerate in the coming years as deep learning systems and tools improve and expand into all industries.
Learn More About Deep Learning
Now is the perfect time to dive into the rapidly evolving world of AI and Deep Learning. Simplilearn offers a range of tutorials and training programs designed to get you started. TensorFlow, an open-source machine learning framework, is an excellent tool for those aspiring to build a career in deep learning. Education and proper certification are essential to cultivate a skilled workforce ready to propel the next computing revolution. To advance your knowledge, consider enrolling in our Artificial Intelligence Engineer Course. Learn more and sign up today at Simplilearn's Artificial Intelligence Engineer Course.
FAQs
1. What are the three types of deep learning?
The three main types of deep learning are supervised learning, where the model learns from labeled data; unsupervised learning, which involves training the model on unlabeled data to find hidden patterns; and reinforcement learning, where an agent learns to make decisions by receiving rewards based on actions.
2. What is CNN in deep learning?
A Convolutional Neural Network (CNN) is a deep learning algorithm that can take in an input image, assign importance to various aspects/objects, and differentiate one from the other. It's predominantly used in image and video recognition.
3. How does deep learning differ from traditional machine learning?
Deep learning automatically extracts and learns features from data using large neural networks. It typically requires substantial data and computational power. Traditional machine learning relies more on manual feature extraction and simpler algorithms and can work effectively with less data and computing resources.
4. What programming languages are commonly used for deep learning projects?
Python is the most popular due to its simplicity and the extensive availability of libraries like TensorFlow, Keras, and PyTorch. Other languages used include R for statistical analysis and Java and C++ for performance-intensive applications.
5. How can I get started with deep learning if I'm a beginner?
Begin by learning Python, the most commonly used language in deep learning. Familiarize yourself with fundamental machine learning concepts through online courses and tutorials. Start experimenting with frameworks like TensorFlow or Keras, using tutorials and documentation to build simple models, gradually increasing complexity as you gain confidence.
