What is Deep Learning?
Deep Learning is a technique that is a subset of machine learning (ML), that refers to training of Neural Networks. Machine Learning allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Deep Learning is preferred in cases where the data is unstructured and massive. It is inspired by the structure of the human brain— thereby the analogy, “Neural Networks” .
It is the most efficient way to deal with unstructured data, as it extracts patterns from the raw data using neural networks. Neural Networks were a field discovered around the early 1950s. But, why are they still trending now? The entire credit goes to the current era of Big Data where massive data is generated everyday by Tech and Industrial Giants. Also, the brilliant configurations of software and hardware devices like GPUs, which are feeding the hunger of algorithms with data.
Applied Deep Learning is an empirical, iterative model. The accuracy is achieved using the ongoing cycle between ideas, code and experimentation. It takes a finite number of iterations to finally come up to a decision.
The main steps for building a Neural Network include defining the model structure (such as number of input features and outputs), initializing the model’s parameters and looping over to calculate the current loss (forward propagation), gradient (backward propagation) and updating the parameters (gradient descent). Another important step is preprocessing the dataset. Tuning the learning rate (which is an example of a “hyperparameter”) can make a big difference to the deep learning algorithm. Hyperparameter optimization also uses this cycle of implementing idea into code and performing experiments on different datasets. Hyperparameter optimization also uses this cycle of implementing idea into code and performing experiments on different datasets.
Deep Learning has applications in various fields like Natural Language Processing, Self-Driving Cars, Medical healthcare, Speech Recognition, and what not. One of the most intriguing application is that of a GAN, Generative Neural Network. GANs produce highest quality images, which are photo realistic and diverse. GANs can generate highly realistic videos and images of celebrities.
One of the most trending applications was an AI-generated “real fake” video of Barack Obama. Researchers at the University of Washington used a neural network to analyze millions of videos to understand the movement of various elements of his face as he talked.
Understanding and interpreting multi-dimensional data is a tedious task, which turns out to be a burden on the human mind. To prevent these brilliant brainpower from doing such laborious tasks, Deep Learning algorithms come to our rescue.
Along with analyzing huge amounts of data, Deep Learning models can even use it for applying that knowledge in real-world problems — technically known as feature extraction and classification — Text to speech conversion, Speech Emotion Recognition, Driver Drowsiness detection and virtual assistants to name a few. Furthermore, deep learning platforms can also benefit from engineered features while learning more complex representations which engineered systems typically lack.
Despite the myriad of open research issues and the fact that the field is still in its infancy, it is abundantly clear that advancements made with respect to developing deep machine learning systems will undoubtedly shape the future of machine learning.
Deep Learning in Neural Nets is more than a temporary fad. Physics seems to dictate that any future efficient computational hardware will have to be brain-like, with many compactly placed processors in 3-dimensional space, sparsely connected by many short and few long wires, to minimize total connection cost (even if the “wires” are actually light beams). The basic architecture is essentially the one of a deep, sparsely connected, 3-dimensional RNN, and Deep Learning methods for such RNNs are expected to become even much more important than they are today.
