“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted and give you the right thing. We’re nowhere near doing that now.
However, we can get incrementally closer to that, which is what we work on”. This quote by Larry Page, co-founder of Google, gives a brief idea about what Artificial Intelligence is and its impact.
Also, as rightly stated by visionaries around the globe, the advent of Artificial Intelligence will help humanity surpass its capabilities to achieve great heights beyond its perceptions. In the next 30 years, Artificial Intelligence is expected to help in diagnoses in healthcare, surveillance, accident detection, space exploration, and more.
Experimenting is the best way to learn Artificial Intelligence. There are plenty of tools out there that can help you to experiment and create. This article aims to give you information about these Artificial Intelligence tools.
TensorFlow is an open-source tool developed by Google for Artificial Intelligence and Machine Learning. It is widely used across communities around the world. It’s used in the areas of text analytics (for example, Google smart reply), image processing (for example, Diabetic Retinopathy), Signal/waves analytics (for example, Building music or ECG scan), etc.
A wide range of research in software and hardware will provide optimized versions that can work on small devices like cell phones and tablets. It also includes elaborative information and API on image processing, CNN, RNN, linear model, etc. API use helps in the early stages of a project to finalize models and ML approaches. With these tools, a developer gets the liberty to try different models on a use case to learn patterns in a short span of time.
Mxnet is an open-source Artificial Intelligence tool that combines symbolic and imperative programming to boost efficiency and flexibility.
Imperative programs perform computation as you run them. Symbolic programs are abstract functions in placeholders where no numeric computation occurs unless it is complied with real inputs. Mxnet dynamically parallizes symbolic and imperative programs and helps in scaling across machines and GPU. Mxnet is a favorite for cloud computing because of its scalability, platform portability, and development speed.
It provides various ML packages and libraries for algorithms and R, Scala, and Python cloud integration. Our interest for Mxnet is R, Scala, and python, which helps integrate big data applications and visualization frameworks.
CNTK is an open-source deep learning toolkit built by Microsoft for neural network computation series. It focuses on models of DNN, CNN, RNN, logistic regression, etc. It also promises to work with optimized CPU and GPU utilization. It works with Keras and Azure. We will be implementing CNTK in the future as it works with Keras.
It is open source and built-in C, C++, and python, focusing on efficient CPU and GPU utilization with Faster execution, compilation, and stabilization. Its primary objective is to optimize maximum memory usage and increase compiling time for massive datasets to perform aspects of computer algebra. It mainly deals with structured data to solve mathematical problems like expression evaluation, logistic functions, regression, gradient descent, graphs, etc.
This encapsulates an object-oriented approach to leverage different wrappers across mathematical terminologies in ML. It helps granularly trace the flow of set up, initialize, implement, optimize, test, etc., unlike using built-in APIs to improvise a model. With wide access to algorithms, Theano can be a gem in minimizing hardware utilization in algebraic and statistical data use cases.
Platform: Ubuntu, Mac, Windows, CentOS
Language: C, C++, and Python
Keras is an open-source deep learning API built on tenser flow, Theano, and CNTK. It is developed in python for faster and easier experimentation in ML and specifically neural networks. It provides API Layers for modeling, pre-processing, predicting, and visualizing the outcome, opening a gateway for research on choosing the efficiency and category of the model that fits the dataset.
The right setup facilitates switching between tensor flow, Theano, and CNTK frameworks. It works seamlessly on GPU and CPU, minimizing hardware requirements and execution time. We can use Keras in the initial stages of a project, re-validate, re-test, and re-engineer our model of execution. It is an excellent tool for beginners to holistically understand image processing, text processing, and other fundamental techniques in ML.
Cv is an open source library for computer vision applications. It contains over 500 algorithms that help to identify and recognize objects, track moving things like cars or human actions, extract 3D images, etc. These aspects inspired countries and organizations to adopt OpenCV in surveillance, self-driving vehicles and accident detection, street view imaging, finding targets in drone/quadcopter streams, and many more.
Object motion detection in video or image processing opens Pandora’s box for use cases where OpenCV can be latched on. Object detection and image pattern recognition accuracy currently put OpenCV at the top of our research in image processing, but there is much more to explore. Video motion detection intrigues me to build private home surveillance with Rasberry Pi.