Ever wonder how Amazon comes up with suggestions of what you should buy next? Or how Netflix recommends movies that you’re most likely to watch? Moreover, how do Siri, Alexa, or Cortana respond to your queries? Behind all these technologies we deal with daily are deep learning algorithms at work. A type of machine learning, deep learning and neural networks attempt to mimic the human brain and make accurate predictions.
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This article will dive deep into the concept of deep learning and neural networks and walk you through the scope of deep learning as a career.
What is Deep Learning?
Deep learning is a machine learning technique that trains computers to learn by example, which instinctively comes to humans. It essentially involves a multi-layered artificial neural network (ANN) that simulates the neuron connections in the human brain. The multiple layers help refine and optimize the accuracy with which the ANNs make predictions.
One of the fastest-growing fields in machine learning, deep learning-driven digital technologies, in a way, has enabled the automation of predictive analytics. Computers learn to perform classifications directly from texts, images, or sound in deep learning through large labeled datasets and neural network architectures. Thus, deep learning and neural networks simplify the task of data scientists who need to collect, analyze, and interpret massive amounts of data for predictive modeling.
How Does Deep Learning Work?
Deep learning neural networks or ANNs imitate the human brain to accurately identify, classify, and define objects within the input dataset. Like the human brain is made of neurons, deep learning neural networks comprise layers of nodes, and nodes within each layer connect to adjacent layers.
While a human brain neuron receives impulses from thousands of other neurons, signals in ANNs travel between nodes of interconnected layers, assigning weights and biases to the input. In machine learning, a weight (w) controls the strength of the connection between two neurons and dictate’s the influence of the input on the output. On the other hand, a bias (b) serves as an additional input to the next layer and has the value 1. The bias ensures that the neuron activates even when all the inputs are zeros.
A heavier weighted node exerts more effect on the subsequent layer of nodes, with the final layer collating the weighted inputs to give an output. The input and output layers of an ANN are called visible layers. While the input layer is where the model takes in data for processing, the output layer is where the deep learning model makes the final prediction. Deep learning models typically contain as many as 150 hidden layers in their neural network.
Real-Life Examples of Deep Learning
Below are a few examples of deep learning and neural networks translating into practical, everyday applications and services:
- Language translations
- Chatbots and service bots
- Virtual assistants
- Facial recognitions
- Recommendation engines
- Image colorization
- Vision for driverless vehicles and drones
- Industrial automation
- Text generation
- Personalized medicine
Deep Learning Skills
Deep learning is a powerful machine learning technique. Therefore, building deep learning models requires advanced machine learning skills. Let’s look at some of the key skills you will need to master deep learning:
Applied Mathematics
Mathematical skills, including statistics, are essential to understanding how deep learning algorithms work. These mathematical skills include linear algebra, probability theory, statistics, calculus, algorithms, and optimization.
Data Engineering
Since deep learning involves a considerable amount of data, having fundamental data engineering skills is fundamental. Data engineering skills mainly include data pre-processing, data extraction, transformation, and loading (ETL), and knowledge of Oracle, MySQL, and NoSQL databases.
Programming
While many programming languages can be used in machine learning, some of the most popular ones include Python, Java, C++, R, and JavaScript. What’s more, these high-level programming languages come with libraries and packages that simplify your work further.
Machine Learning Algorithms
Knowledge of machine learning algorithms is a must if you want to master deep learning. Machine learning algorithms that come in handy include Naive Bayes, K-nearest Neighbor, Support Vector Machine, Linear Regression, Logistic regression, Random Forest, Decision Tree, K-means Clustering, and Hierarchical Clustering.
Deep Learning Algorithms
A crucial part of your deep learning skillset is deep learning algorithms. Some popular deep learning algorithms include Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Belief Network (DBN), and Generative Adversarial Network (GAN).
Deep Learning Frameworks
Lastly, you need to learn various deep learning frameworks that help design, train, and validate deep neural networks. The most widely used deep learning frameworks are TensorFlow, PyTorch, Keras, ScikitLearn, Theano, DL4J, Sonnet, Gluon, and MXNet.
Natural Language Processing
NLTK, Gensim, Word2vec, Sentiment Analysis, and Summarization are some of the top natural language processing libraries and techniques used in machine learning.
Soft Skills
Apart from the technical skills discussed above, machine learning professionals must have relevant soft skills and behavioral skills, including:
- Domain knowledge
- Reasoning and problem-solving skills
- Communication skills
- Rapid prototyping
Scope of Deep Learning
The field of artificial intelligence and machine learning offers lucrative career avenues with life-long learning opportunities. According to Payscale, the average yearly salary of a machine learning engineer with deep learning skills is US$ 110,491. Moreover, with almost every industry and sector adopting AI-driven technologies to improve business processes and products, there is a concomitant rise in demand for skilled AI professionals.
Moreover, the global AI software market is forecasted to witness a staggering growth in the coming years, reaching about US$ 126 billion by 2025. The market includes many AI applications, including robotic process automation, machine learning, and natural language processing. Needless to say, deep learning skills will be highly valued among recruiters looking for the best talents in the AI field. Thus, the scope of machine learning and deep learning is pretty broad and promising, both in terms of opportunities and salary.
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Conclusion
Artificial intelligence (AI) and its subsets such as machine learning and deep learning have proved that computers can perform tasks that typically require human intelligence. From virtual assistants and chatbots to autonomous vehicles, AI-driven technologies have permeated almost every aspect of our lives. As algorithms evolve and learn, the list of real-world applications and use cases of machine learning and deep learning will continue to grow.
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What is the difference between deep learning and machine learning?
Deep learning is a subset of machine learning. However, the two differ in the type of data they deal with and the learning methods employed. Machine learning works with structured, unlabeled data to make predictions. Even if it uses unstructured data, it is pre-processed to impart some structure and organization. On the other hand, deep learning eliminates most of the pre-processing. Instead, it ingests and processes unstructured data, and with automated feature extraction, deep learning algorithms eliminate dependence on humans. Moreover, deep learning mimics the human brain to learn by example, whereas machine learning is about computers performing tasks without explicit programming.
Why is deep learning deep?
The DEEP in deep learning comes from the multiple hidden layers in the artificial neural networks (ANNs) of deep learning models. Each layer comprises nodes that are interconnected with nodes in adjacent layers, and each node of the layers is assigned a weight that determines the strength of the output. Thus, computers use multiple layers of neural networks to learn from data; the more layers in the model, the DEEPER the learning.
What is NLP AI?
Natural Language Processing (NLP) is a branch of computer science and AI that trains computers to understand natural languages like text and speech. Thus, the goal of NLP is to build machines that understand and respond to voice or text data just the way humans do. NLP combines deep learning, machine learning, and statistical models with computational linguistics so that computers can process human language and the sentiment of the speaker or writer. Real-world applications of NLP include voice-controlled assistants like Alexa and Siri, autocorrect/autocomplete features, customer service chatbots, tools like Grammarly, etc.
