Enough of flirting with deep learning and deep learning frameworks; it’s time to glide across the room and say, “Hello.” Call it an advanced subfield of machine learning or future to enhanced vision in the field of technology, deep learning won’t stop now!
Imbibed in the majority of business operations, the deep learning paradigm is gaining momentum every then and now. And trust me, software development companies don’t have many options other than shifting towards developing artificial learning and machine learning models—which run on the existing and upcoming smart devices like never before. What’s more significant is other than making applications smarter and intelligent, the technology makes solving complex problems hassle-free procedure.
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Building and deploying has now become a little passé. And with such disruptive advancements like voice recognition, analytics tasks, etc are expected to be executed with a high level of sophistication. And it’s certainly not limited to the IT industry, deep learning has entered the mainstream for example from autonomous vehicles to medical research and diagnostics.
With a virtually unlimited capacity to learn, DL networks can make sense of mind-boggling amounts of data and open the doors to a whole new level of analytics.
Now I am pretty sure you must be wondering how the tech assures such impressive results. With accuracy as its core, deep learning is expected to offer best possible outcomes only (in both ways accurate and efficient). Though it was initially theorized way back in 1980, let me showcase why it has recently become useful.
First, due to the new prevalence of large amounts of labeled data. For instance, driverless car development requires millions of images and thousands of hours of video.
Second, because of its requirement of substantial computing power. High-performance GPUs incorporate parallel architecture which is quite efficient for deep learning. By combining with clusters and cloud computing, software developers no longer require working all day and all night long! In simple words, with the help of deep learning projects can be accomplished from weeks to hours or less.
A few interesting examples to consider:
- Self-Driving Tech — Most of the automotive researchers are seen using the booming tech to not just detect objects such as traffic signals or stop signals but also detect how far are pedestrians walking, that certainly reduces accidents.
- Aerospace and Defense — Mainly used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.
- Medical Research — Cancer researchers are seen using deep learning to simply detect cancer cells. For instance, UCLA has a team that is not just built on an advanced microscope that yields a high-dimensional data set but also identifies cancer cells in no time.
- Industrial Automation — Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.
- Electronics — Deep learning is being used in automated hearing and speech translation. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications.
Programming languages used for deep learning
- Python — Python developers across the globe might be seen happy these days as the language has made its way in the deep learning and AI branches realm. Featuring a wide range of libraries, Python is highly used to implement algorithms. It also supports object-oriented and procedure-oriented programming. Most computer learning software runs on Python.
- Java — Now since we all know that deep learning is found dealing with artificial neural networks, search algorithms, and many others. It has great UI, easy debugging, package services, and large scalability. Java also uses Swing and Standard Widget Toolkit, allowing you to create a beautiful and rich graphical representation of data.
- C++ — It may be one of the oldest programming languages, but it is necessary to learn C++ for deep learning. The reason why C++ works well for deep learning is that it is compatible with resource-intensive applications. Since C++ can be used for low- and high-level programming, it gives developers more control and efficiency.
Top 5 Deep Learning Frameworks for 2020
TensorFlow — The open-source machine learning framework is both created as well as maintained by Google. Mainly used on Gmail, Google Photos, Speech Recognition, the framework offers a wide range of advantages such as:
- It’s free, reliable and supported by Google
- Deep learning becomes way easier to implement
- An extensive documentation for developers
Caffe — This one is for those who have been into deep learning before. Caffe is again open-source framework highly known for its speed and has the potential to process more than 60 million images a day making it very suitable for image recognition. Whether it’s C, C++, Python, MATLAB, and CLI, the framework is compatible with everyone. The expensive architecture allows for training neural nets without hard coding and its extensible code encourages active development.
No doubt, the technology has come pretty much like a boom but it does share a bunch of fair controversies as well. For example, it doesn’t support fine granular network layers like those found in TensorFlow or Microsoft Cognitive Toolkit. It may interest you to know that Caffe played a crucial role in the Google DeepDream project.
PyTorch — PyTorch is gaining popularity these days. Developed by Facebook, the framework is highly known for its simplicity, flexibility, and customizability. After witnessing a high level of adoption within the community, deep learning models become easy to learn and execute.
Microsoft Cognitive Toolkit — With high support in Python, C++, C#, and Java, the deep learning framework in particular successfully facilitates training for voice, handwriting, and images with ease and provides scalable, optimized components.
Although, it has limited support from the community but Azure users have full, simple integration. It also has Apache Spark support and reasonable resource allocation. It also supports both convolutional and recurrent neural networks.
[Related Article: Deep Learning Frameworks You Need to Know in 2020]
Overall, there’s a lot to be done within the DL industry, and you can accomplish even more with the right deep learning frameworks.
Vikash Kumar Biography:
Vikash Chaudhary working as a manager at Tatvasoft.com which is a software outsourcing company will global locations. He is far-sighted when it comes to significant new technologies and new learning methods. He likes to share his views on many different measures and topics on software development methods. You can also follow him on Twitter and LinkedIn.