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On the verge of becoming even smarter and more competent, machine learning re-imagine the entire app creation and marketing playbook. Certainly, Machine Learning is driving a new generation experience that transforms applications and services in a way how people prefer and value.
Machine learning is a cool topic if you are interested in building kick-ass apps with intelligence that includes features such as natural language processing, prediction, face detection, speech recognition or anything like that. From Virtual Assistants to traffic predictions, machine learning is refining the conventional way of tasks performance along with smarter pieces of stuff to enhance the experience.
As it’s not an easy option to develop a machine learning system from the scratch, a number of machine learning frameworks are available for developers to make intelligent apps. There are many open source machine learning platforms with on-device processing, without big data, and just a few lines of code. So here we bring up the best machine learning frameworks that can aid developers to get things done in a very convenient way in smart application development, for both, web and mobile.
TensorFlow by Google is an open source software library for creating Deep Learning models using data flow graphs. It facilitates on-device machine learning inference with small memory footprint and low latency. Tensorflow is a part of various Google services such as Google Recognition, Google Photos, Google Search, and more.
Google Translate’s instant visual translation make use of this framework at the backend for on-device processing. This framework is quite mature and will be the best part of today's mobile apps development. Its caliber is revealed with its application by companies like Twitter, Dropbox, Snapchat, Uber, Intel, Deepmind, etc.
Core ML is a brand new foundational machine learning framework for domain-specific frameworks and functionality by Apple which is used in Apple products like Siri, QuickType, and Camera. With minimum lines of code, Core ML offers fast performance as well easy integration of machine learning models to create apps with intelligent features. With Core ML, developers can make computer vision machine learning features like object tracking, text detection, face detection, face tracking, barcode detection, and more into the iOS apps.
Core ML supports Vision for image analysis, Foundation for natural language processing and Gameplay Kit for analyzing learned decision trees. Core ML itself is built on top of low-level technologies like Accelerate Metal, BNNS, as well as Metal Performance Shaders. It seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency. Interestingly you can train and convert your models in Tensorflow into the Core ML format in order to take advantages of both platforms.
Microsoft Cognitive Toolkit is the Machine Learning framework by Microsoft, facilitating Deep Learning algorithms to work under a range of environment, from CPU to GPU to multiple machines. Skype, Cortana, Xbox, Bing etc are the commercial grade AI applications developed and rendered using the Cognitive toolkit. It enables building the Deep Learning models using Brainscript, C++, and Python.
Watson is a machine learning platform with cognitive computing by IBM. It provides APIs such as text to speech, speech to text, personality insights, trade-off analytics, tone analyzer, question and answer, and visual recognition. Without banging your heads, Watson APIs enable everything ready at your hands to build an intelligent application. Without any additional integration, Watson can ingest, enrich and normalize a wide variety of data types and also allows to leverage data from a broad range of sources with ease.
Amazon AI is a machine learning service by Amazon for developers to get the visualization wizards and tools that allow them to create ML models without complicated algorithms or technologies. Under Amazon AI umbrella there are:
Rekognition offers sophisticated deep-learning based visual search and image categorization to your App. With this image analysis feature, you can search, find and compare objects, scenes, and faces in images. Use cases for Rekognition service are Face-Based User Verification, Searchable Image Library and Sentiment Analysis.
Polly is a service that turns text into human-like speech. It lets you create interactive apps with new categories of speech-enabled products. It is a cloud-based Text-to-Speech service that uses advanced deep learning technologies to synthesize speech that speaks like a human voice.
Lex is the innovative technology that fuels Amazon’s own Alexa service. It allows you to develop conversational applications that can feature multi-step conversations. You can use Amazon Lex to develop chatbots and apps that support engaging, human-like interactions. Use cases of Lex are Informational Bots, Application Bots.
Caffe is a deep learning framework made with expression, speed, and modularity in mind by Berkeley AI Research. Model Zoo is a pre-trained Caffe ML model for performing different tasks. However, this framework is not meant for non-computer vision tasks such as sound, time series, or text. You can take the benefit when actually run it on a variety of hardware and with a single flag, the switching between CPU and GPU is set.