Problem

R was facing a challenge in developing a deep-learning model for image recognition. The company needed to develop a model that could read neural networks with images and locate objects in the images. However, the team lacked expertise in this area and did not have experience working with Deepnet, TensorFlow, Keras, and MXNet packages.

The lack of knowledge and experience made it difficult for R to develop an accurate deep-learning model for graphical recognition of signs and images. The team needed to find a solution to overcome these challenges.

Learn more about our solution here.

Solution

To address the challenges faced by R, we assembled a team of experts in deep learning models. Our team had extensive experience working with Deepnet, TensorFlow, Keras, and MXNet packages.

We began by conducting an analysis of the project requirements and developing an R language project to develop a deep learning model for image recognition. We used artificial neural networks to model and solve complex problems. To achieve graphical recognition of signs and images, we developed a deep learning model using Deepnet, TensorFlow, Keras, and MXNet packages. We implemented this model with R language and achieved an accuracy rate of 80%.

Impact

Developed an accurate deep learning model for graphical recognition of signs and images
Achieved an accuracy rate of 80%

Quote of how incredible of an impact this had.

Team

Team Member #1 - Deep Learning Expert
- Developed the deep learning model using artificial neural networks to solve complex problems
- Worked with Deepnet, TensorFlow, Keras, and MXNet packages to develop the model
Team Member #2 - R Language Expert
- Implemented the deep learning model with R language to achieve graphical recognition of signs and images
- Achieved an accuracy rate of 80%

RGypr Develops 80% Accurate Image Recognition Model