Fundamentals of Deep Learning
Fundamentals of Deep Learning is a 2-day course where participants get hands-on training in deep learning principles and practical applications.
Many analysts feel that deep learning will become prominent in business of the future.
Deep learning is a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data.
At its core, deep learning is about learning from examples — the way the human brain does. It’s imitating the way humans acquire certain types of knowledge. Because deep learning processes information in a similar manner, it can be used to do things people can do such as learning how to drive a car or identifying a dog in a picture.
Deep learning is also used to automate predictive analytics, which can involve identifying trends and customer buying patterns so a company can gain more customers and keep more of them. For example, sections on retail sites that show items “frequently bought together” are based on predictive deep learning algorithms that have considered both your current search and past buying patterns to suggest additional products you might also need.
Other common applications of deep learning include numerous everyday encounters and activities, such as virtual assistants, fraud detection, language translation, chatbots and service bots, colorization of black-and-white images, facial recognition and disease diagnoses.
The popularity of deep learning has really taken off in the last five years. It’s used for many AI-related tasks like object recognition, text processing, image processing, etc.
The major reasons for its popularity are the highly accurate results produced by deep learning models, the ability to make predictions on unstructured datasets, and provide useful insights about the same. According to reports by AI Index, there’s been a significant increase (almost six times) in the number of citations among the research community.
A McKinsey survey showed that while AI adoption has been adding value to organizations, the use of deep learning in business is still at an early stage. However, the advancements of this approach to AI are very promising. It is already transforming several industries, including healthcare, finance, and automotive.
Companies have massive amounts of data that need to be analyzed and understood. Deep learning is capable of handling it and analyzing it very accurately.
Classic machine learning algorithms like Naïve Bayes, decision trees, and support vector machines can’t directly be applied to raw data. Typically, a preprocessing step called feature extraction is necessary to convert the data into a format that algorithms can understand so as to classify it. The process is complex and time-consuming.
In contrast, deep learning eliminates the need for manual feature extraction, with neural networks capable of drawing features out of raw data automatically. Multiple processing layers produce increasingly complex representations of data, culminating in the best possible input data.
Fundamentals of Deep Learning Course by Tonex
Fundamentals of Deep Learning is a 2-day training course address fundamentals of deep learning. This two-course online training program brings a hands-on approach to understanding the deep learning methods, algorithms, and computational tools used in modern engineering problem-solving.
Leveraging the rich experience of the faculty at
Tonex, this program connects your deep learning, science and engineering skills to the principles of deep learning, machine learning and data science. With an emphasis on the application of deep learning methods, you will put these new skills into practice in real time.
Fundamentals of Deep Learning