deep learning

Let us go back to the basics

Deep learning is training a machine to behave like a human to take better decisions to automate less complex processes. There are two anchors of deep learning, one of them is data. Now, deep learning itself has learning in its name. For learning, there is a requirement of huge labeled data. The second anchor for deep learning is computing power. High-performance GPUs having a parallel architecture are efficient for deep learning.

With these two anchors, precisely any sector, segment or industry can use it to automate processes and save cost with the help of deep learning. I am listing a few names who are already using deep learning.

  • Google in its searches uses multiple machine-learning systems, to understand the language in your query through to personalizing your search results. For example, fishing enthusiasts, when they search for “bass,”  they aren’t inundated with results about guitars.
  • Cashierless Amazon Go supermarkets are possible because of deep learning. There is no need to stand in the queues.

Science behind Deep Learning:

Deep learning works on Neural Networks

Neural networks have an input layer, where the baseline data is fed in, and an output layer, that generates the final output inferred from the fed data. These multiple layers of information provide a way for the neural network to build a rough funnel of different features that make up the handwritten digits for the data. Over the course of many, many training cycles the network will continue to generate better and better predictions on the basis of the increase in the data available for the network to analyze.

Deep neural networks excel at making predictions as the largely unstructured data increases. Hence, they deliver the best results when it comes to speech and image recognition, where they work with messy data such as recorded speech and photographs.

Drawbacks of Neural Networks:

  • Heavy Data: There is a requirement for huge amounts of data when it comes to deep learning and training.
  • Cost of Training: Deep-neural networks are really difficult to train, due to the vanishing gradient problem, which can worsen with more number of layers there are in a neural network.
  • Cost of Hardware: Given that top-of-the-range GPUs can cost thousands of dollars for effective deep learning  ($5 per hour to rent in the cloud), it is really difficult to get into the business.

Software Frameworks for Development:

There are a wide variety of deep-learning software frameworks that are available to train and validate deep neural networks, using a range of different programming languages that are used for Deep Learning.

The first best choice is Google’s TensorFlow software library, which allows users to write in Python, Java, C++, and Swift, that can be used for image and speech recognition, and which uses a huge range of CPUs, GPUs, and other processors. It is nicely documented, and has many tutorials and implemented models.

Another popular one is PyTorch, a framework that offers the imperative programming model familiar to developers. Among the wide range of other frameworks are Microsoft’s Cognitive Toolkit, MATLAB, MXNet, Chainer, and Keras.

Peep in the Future:

With most businesses in the early stages of machine learning adoption, there’s a long gestation period to finally get out with something clutter breaking though the steps have been taken in the right direction. There are heavy investments in the process with efforts that are visible pan industries. As infrastructure becomes more able and supportive, effective machine learning applications can be expected as the outcome by 2020.