Although a new technological advancement, the scope of Deep Learning is expanding exponentially. Deep Learning technology aims to imitate the biological neural network, that is, of the human brain. While the origins of Deep Learning dates back to the 1950s, it is only with the advancement and adoption of Artificial Intelligence and Machine Learning that it came to the limelight.
A subset of Machine Learning, Deep Learning leverages artificial neural networks arranged hierarchically to perform specific ML tasks. Deep Learning networks use the unsupervised learning approach – they learn from unstructured or unlabeled data. Artificial neural networks are just like the human brain, with neuron nodes interconnected to form a web-like structure.
While traditional learning models analyze data using a linear approach, the hierarchical function of Deep Learning systems is designed to process and analyze data in a nonlinear approach.
Deep Learning architectures like deep neural networks, recurrent neural networks, and deep belief networks have found applications in various fields including natural language processing, computer vision, bioinformatics, speech recognition, audio recognition, machine translation, social network filtering, drug design, and even board game programs. As new advances are being made in this domain, it is helping ML and Deep Learning experts to design innovative and functional Deep Learning projects.
Today, we’ll discuss the top seven amazing Deep Learning projects that are helping us reach new heights of achievement.
Top Deep Learning Projects 2020
Detectron is a Facebook AI Research’s (FAIR) software system designed to execute and run state-of-the-art Object Detection algorithms. Written in Python, this Deep Learning project is based on the Caffe2 deep learning framework.
Detectron has been the foundation for many wonderful research projects including Feature Pyramid Networks for Object Detection; Mask R-CNN; Detecting and Recognizing Human-Object Interactions; Focal Loss for Dense Object Detection; Non-local Neural Networks, and Learning to Segment Every Thing, to name a few.
Detectron offers a high-quality and high-performance codebase for object detection research. It includes over 50 pre-trained models and is extremely flexible – it supports rapid implementation and evaluation of novel research.
WaveGlow is a flow-based Generative Network for Speech Synthesis developed and offered by NVIDIA. It can generate high-quality speech from mel-spectograms. It blends the insights obtained from WaveNet and Glow to facilitate fast, efficient, and high-quality audio synthesis, without requiring auto-regression.
WaveGlow can be implemented via a single network and also trained using a single cost function. The aim is to optimize the likelihood of the training data, thereby makes the training procedure manageable and stable.
OpenCog project includes the core components and a platform to facilitate AI R&D. It aims to design an open-source Artificial General Intelligence (AGI) framework that can accurately capture the spirit of the human brain’s architecture and dynamics. The AI bot, Sophia is one of the finest examples of AGI.
OpenCog also encompasses OpenCog Prime – an advanced architecture for robot and virtual embodied cognition that includes an assortment of interacting components to give birth to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the system as a whole.
DeepMimic is an “example-guided Deep Reinforcement Learning of Physics-based character skills.” In other words, it is a neural network trained by leveraging reinforcement learning to reproduce motion-captured movements via a simulated humanoid, or any other physical agent.
The functioning of DeepMimic is pretty simple. First, you need to set up a simulation of the thing you wish to animate (you can capture someone making specific movements and try to imitate that). Now, you use the motion capture data to train a neural network through reinforcement learning. The input here is the configuration of the arms and legs at different time points while the reward is the difference between the real thing and the simulation at specific time points.
5. IBM Watson
One of the most excellent examples of Machine Learning and Deep Learning is IBM Watson. The greatest aspect of IBM Watson is that it allows Data Scientists and ML Engineers/Developers to collaborate on an integrated platform to enhance and automate the AI life cycle. Watson can simplify, accelerate, and manage AI deployments, thereby enabling companies to harness the potential of both ML and Deep Learning to boost business value.
IBM Watson is Integrated with the Watson Studio to empower cross-functional teams to deploy, monitor, and optimize ML/Deep Learning models quickly and efficiently. It can automatically generate APIs to help your developers incorporate AI into their applications readily. On top of that, it comes with intuitive dashboards that make it convenient for the teams to manage models in production seamlessly.
6. Google Brain
The Google Brain project is Deep Learning AI research that began in 2011 at Google. The Google Brain team led by Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University Professor Andrew Ng aimed to bring Deep Learning and Machine Learning out from the confines of the lab into the real world. They designed one of the largest neural networks for ML – it comprised of 16,000 computer processors connected together.
To test the capabilities of a neural network of this massive size, the Google Brain team fed the network with random thumbnails of cat images sourced from 10 million YouTube videos. However, the catch is that they didn’t train the system to recognize what a cat looks like. But the intelligent system left everyone astonished – it taught itself how to identify cats and further went on to assemble the features of a cat to complete the image of a cat!
7. 12 Sigma’s Lung Cancer detection algorithm
12 Sigma has developed an AI algorithm that can reduce diagnostic errors associated with lung cancer in its early stages and detect signs of lung cancer much faster than traditional approaches.
According to Xin Zhong, the Co-founder and CEO of Sigma Technologies, usually conventional cancer detection practices take time to detect lung cancer. However, 12 Sigma’s AI algorithm system can reduce the diagnosis time, leading to a better rate of survival for lung cancer patients.
Generally, doctors diagnose lung cancer by carefully examining CT scan images to check for small nodules and classify them as benign or malignant. It can take over ten minutes for doctors to visually inspect the patient’s CT images for nodules, plus additional time for classifying the nodules as benign or malignant.
Needless to say, there always remains a high possibility of human errors. 12 Sigma maintains that its AI algorithm can inspect the CT images and classify nodules within two minutes.
These are only a handful of the real-world applications of Deep Learning made so far. The technology is still very young – it is developing as we speak. Deep Learning holds immense possibilities to give birth to pioneering innovations that can help humankind to address some of the fundamental challenges of the real world.
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