1. Home
  2. Deep Learning
  3. Houston

Top Deep Learning Companies in Houston


3 Companies

profile

Cybersoft North America Inc

No Reviews

The Company was formed as a virtual startup in 1996 as a spin-off from Applied Research Technologies, Incorporated. Over the years, Cybersoft has established itself as a trusted, quality provider of Software Solutions & tools for multiple markets such as Artificial Intelligence (Machine Learning, Operationalization of models), Financial Services (Account Receivable Management), Enterprise Workflow based Web Applications, internet of things (IoT) and cross-platform Mobile Applications. Cybersoft takes pride in its unique, prototyped product SCRUM-based development which ensures architecting broad-range solutions. We have been developing highly scalable and comprehensive software products for over 26 years. Our methodology results in minimal defects in the post-deployment phase and higherRead More

  • dollar

    $25-49/hr

  • user

    50 to 99

  • calender

    1996

  • location

    United States (USA)

profile

Odyssey Analytics

No Reviews

Odyssey Analytics stands out as a premier software consulting firm specializing in artificial intelligence (AI). As industry leaders in AI, we are dedicated to catalyzing digital transformation and technology modernization for our clients. Our expert team harnesses the power of AI to craft bespoke solutions that elevate businesses to new heights. From machine learning algorithms to predictive analytics, we deploy cutting-edge technologies tailored to meet unique industry challenges. At Odyssey Analytics, we redefine possibilities through the strategic application of AI, ensuring our clients stay ahead in the competitive landscape. Collaborate with us to unlock the full potential of AI and propel your business into the future.

  • dollar

    $50-99/hr

  • user

    50 to 249

  • calender

    2019

  • location

    United States (USA)

profile

Kipp Technologies

No Reviews

Kipp Technologies is a USA-based software innovation company dedicated to transforming businesses through advanced digital solutions. We collaborate with startups, growing enterprises, and global organizations to develop cutting-edge technologies that drive efficiency, scalability, and long-term success.

  • dollar

    $25-49/hr

  • user

    2 to 9

  • calender

    2024

  • location

    United States (USA)

Frequently Asked Questions

Deep Learning is a subset of Artificial Intelligence that uses neural networks with multiple layers to learn from large amounts of data. It's particularly effective for tasks like image and speech recognition.

Deep Learning can automatically discover the features to be used for classification, while traditional Machine Learning requires manual feature engineering. Deep Learning also generally performs better with large amounts of data.

Deep Learning is used in various business applications, including customer service chatbots, fraud detection, personalized recommendations, and predictive maintenance in manufacturing.

Deep Learning typically requires large amounts of labeled data. The type of data depends on the project, but it can include images, text, audio, or numerical data.

The implementation time can vary greatly depending on the complexity of the problem, the amount of data available, and the expertise of the team. It can range from a few weeks to several months.

Some drawbacks include the need for large amounts of data, high computational requirements, the "black box" nature of decision-making, and potential biases in the training data.

Deep Learning often outperforms other AI techniques in tasks involving unstructured data like images or text, especially when large datasets are available. However, for simpler tasks or with limited data, traditional machine learning methods may be more suitable.

Deep Learning often requires powerful GPUs or specialized hardware, substantial storage for large datasets, and robust cloud computing resources. The specific needs depend on the scale and complexity of the project.

Businesses should focus on data privacy, model transparency, regular bias checks, and establishing clear guidelines for the use and deployment of Deep Learning systems. It's also important to have human oversight and intervention mechanisms.

A successful Deep Learning team typically needs data scientists, machine learning engineers, software developers, domain experts, and data engineers. Skills in Python, neural network architectures, and big data technologies are often required.

More Service Providers