Deep Learning Development
Experience the Potential of AI with Deep Learning
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.
Introducing Deep Learning into business has the potential to improve workflow productivity, competitiveness, and increase customer retention.
Manufacturing
Using AI-powered Deep Learning solutions, you can predict future trends and adjust production based on high or low demand.
Example: Vicarious provides a robotics platform for automating manufacturing processes. It is based on Deep Learning models to enable robots to perform tasks such as picking and placing objects, sorting, and inspection.
Cybersecurity
Deep Learning helps protect your system, as well as IoT devices, as companies and individuals need to stay one step ahead of hacker attacks.
Example: Deep Instinct developed a platform designed to protect against cyber threats, such as malware, ransomware, and phishing attacks, by using Deep Learning to analyze and classify data in real-time.
Education and e-learning
Everyone from K-12 to mid-career students can experience DL’s effect on education. Models can summarize materials and generate open questions, quizzes, and identify each student’s strengths and weaknesses to build learning plans tailored to their needs.
Example: Knewton develops adaptive learning technology for higher education. It helps identify gaps in students’ knowledge and brings students back on track for college-level study by offering a relevant curriculum.
Customer support and automation
Chatbots, automatic extraction of documents, and auto-forwarding incoming messages to specific departments or persons reduce costs, boost business, and allow focusing on other tasks.
Example: Cogito applied Deep Learning to create a tool that can analyze conversations in real time. Their algorithm is cognizant of the tone and content of the dialogue. The tool provides details about customers’ feelings based on volume changes, as well as pitch and mimicking detection.
Retail and Ecommerce
Using Deep Learning will help you better understand your customers and differentiate your products by offering personalized content and aligning with customer interests.
Example: Dynamic Yield's platform is based on Deep Learning models to analyze customer data and provide personalized recommendations to shoppers in real-time.
Automotive
Deep Learning models can help build self-driving cars with advanced detection capabilities.
Example: Comma.ai develops an open-source driver assistance systems. The platform is trained on a large dataset of images and sensor data from a variety of driving scenarios, allowing it to learn to identify patterns and characteristics that are indicative of different driving conditions.
Healthcare and Medical Research
Deep Learning enables better diagnosis and treatment plans for patients by analyzing trends, patterns, and behaviors.
Example: Notable Labs provides a platform for identifying personalized treatment options for cancer patients. By using Deep Learning to analyze genomic data from cancer patients it identifies genetic mutations that may be contributing to the development or progression of the disease.
Finance
Deep Learning solutions are used in finance to analyze financial data, such as customer data, stock prices, trade patterns, to identify trends and make predictions about future market movements.
Example: ZestFinance is a company that provides a platform for analyzing and underwriting credit risk. Its platform is based on Deep Learning models to analyze data from a variety of sources, such as credit reports, bank statements, and social media profiles, to identify patterns and characteristics that may be relevant to creditworthiness.
How we work
Key stages of Deep Learning development
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.
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.
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.
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.
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?