Tensorway helps businesses revolutionize their decision-making and enhance their operations with Deep Learning models able to analyze data with precision and speed never seen before.
Almost every business dreams to boost results, without spending too many resources and finances. Sounds too good to be the truth? Not when Deep Learning is in the game.
Why should businesses care about Deep Learning?
Deep Learning is enabling advanced software capabilities, and adding tremendous value to the business and user experience. As more and more companies take advantage of Deep Learning capabilities, the market is growing rapidly, and is projected to reach $179.9 billion by 2030.
Deep Learning solutions open unimaginable opportunities known before in computer systems. Learning by example, just like a human brain, Deep Learning models understand information that was thought to be understandable by humans only. It opens new horizons for businesses to automate routine operations performed by people. Deep Learning can also identify trends and patterns, and enable powerful predicting algorithms.
Use Cases
Where is Deep Learning applied? Everywhere.
Example: ZestFinance uses Deep Learning to analyze credit risk by processing data from credit reports, bank statements, and social media to assess creditworthiness.
Example: Aidoc uses Deep Learning to analyze medical images, helping radiologists detect critical conditions like strokes or hemorrhages faster and more accurately.
Example: Comma.ai develops an open-source driver assistance system that learns from images and sensor data to recognize patterns and adapt to various driving conditions.
How we work
Key stages of Deep Learning development
Understand your business requirements
01
Data collection and preparation
02
Model design and architecture
03
Model training
04
Model evaluation
05
Model deployment
06
1
Understand your business requirements
Our team analyzes your business and identifies your needs to provide a software solution that can solve your business problems.
2
Data collection and preparation
Collect and prepare the data that will be used to train the model. This involves sourcing and collecting data, as well as cleaning and preprocessing it.
3
Model design and architecture
This step involves selecting the appropriate type of model, such as a convolutional neural network or a recurrent neural network, and determining the number and size of the model's layers.
4
Model training
By training the Deep Learning model on a large and diverse dataset, we can ensure that the model is able to generalize well and perform accurately on a wide range of inputs.
5
Model evaluation
We evaluate the model's performance on a separate dataset to identify any weaknesses or issues with the model and make necessary improvements.
6
Model deployment
If the model performs well during evaluation, it can then be deployed in a production environment, where it can be used to make predictions or decisions based on real-world data.
Frequently Asked Questions
How is Deep Learning different from Machine Learning?
How does Deep Learning give such impressive results?
Can Deep Learning solutions be smarter than humans?
How much does it cost to develop a Deep Learning-based solution?