Our machine learning software development services involve creating self learning algorithms that can minimize errors and maximize accuracy with time.
Supervised, Un-superwised learning
Artificial Neural Networks
Technique that focuses on teaching computers to learn by example using algorithms similar to cerebral neural network structures and functions.
The common use case for NLP technology is a text-only interface or a spoken dialog system that can answer a human request. But we also use NLP to understand what customers think about your products, extract information about companies or people from news articles, generate short descriptions of text documents, and more.
Making products in the images searchable, filtering out unsafe user-generated content, making purchasing recommendations on taken photos – how cool is that! With the help of neural networks and machine learning technology we can train a system to identify people and objects in images and build high value applications.
Systems, capable of verbal interaction with humans and possessing both input and output channels, employ one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel.
Enables machines in the recognition and translation of a spoken language into text. Users control machines via speech.
Voice Style Transfer
Accent & Person Detection
Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), casts a weighted "vote" for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another.
Search & Optimization
Many problems in AI can be solved theoretically by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.