Machine Learning Archives - Winklix - Software Development Blog

November 30, 2023November 30, 2023

The Cutting-Edge Technologies Powering Artificial Intelligence

Introduction:

Artificial Intelligence (AI) has evolved into a transformative force across various industries, revolutionizing the way we live and work. Behind the scenes, a myriad of advanced technologies work in tandem to enable AI systems to learn, reason, and make decisions. In this article, we’ll explore the key technologies that form the backbone of AI, driving innovation and shaping the future.

Machine Learning (ML): At the core of AI lies Machine Learning, a subset of AI that empowers systems to learn from data without explicit programming. ML algorithms analyze patterns, make predictions, and continually improve performance with more exposure to data. Deep Learning, a subset of ML, involves neural networks with multiple layers that mimic the human brain’s structure. Deep Learning has been particularly instrumental in image and speech recognition, natural language processing, and other complex tasks.
Natural Language Processing (NLP): NLP enables machines to understand, interpret, and generate human language. This technology is essential for chatbots, language translation, sentiment analysis, and voice recognition. NLP algorithms rely on linguistic models and semantic understanding to process and respond to human language in a way that is contextually relevant.
Computer Vision: Computer Vision allows machines to interpret and make decisions based on visual data. This technology has applications in image and video analysis, facial recognition, object detection, and autonomous vehicles. Convolutional Neural Networks (CNNs) are commonly used in computer vision tasks, mimicking the visual processing of the human brain.
Reinforcement Learning: Reinforcement Learning is a paradigm where an AI agent learns by interacting with its environment and receiving feedback in the form of rewards or penalties. This technology has proven effective in training AI systems for complex decision-making scenarios, such as game playing, robotics, and autonomous systems.
Robotics: AI and robotics go hand in hand, with AI algorithms powering the brains of robots. Machine learning algorithms enable robots to adapt to changing environments, learn from experience, and execute tasks with precision. This fusion of AI and robotics has applications in manufacturing, healthcare, logistics, and more.
Edge Computing: Edge Computing involves processing data closer to the source rather than relying solely on centralized cloud servers. In the context of AI, edge computing reduces latency and enhances real-time processing capabilities, making it crucial for applications like autonomous vehicles, smart cities, and IoT devices.
Quantum Computing: As AI models grow in complexity, the demand for faster and more powerful computing grows as well. Quantum Computing holds promise in significantly accelerating AI computations, especially for solving complex optimization problems and training large-scale models.
Explainable AI (XAI): As AI systems become more intricate, the need for transparency and interpretability grows. Explainable AI focuses on developing models that provide understandable explanations for their decisions, enhancing trust and facilitating human understanding of AI-driven outcomes.
Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator, and a discriminator, engaged in a competitive process. This technology is used for generating synthetic data, creating realistic images, and enhancing data augmentation techniques, contributing to advancements in image and content generation.

Conclusion:

The field of Artificial Intelligence is a dynamic and rapidly evolving landscape, with technologies continuously pushing the boundaries of what AI systems can achieve. The integration of machine learning, natural language processing, computer vision, and other cutting-edge technologies is driving unprecedented advancements, making AI an indispensable tool in solving complex problems and shaping the future of technology. As researchers and engineers continue to innovate, the synergy between these technologies will likely unlock new possibilities, bringing about transformative changes in diverse sectors of our society.

August 5, 2020August 5, 2020

Understanding Python for Machine Learning – Part 1 (Data Types in Python)

Machine learning has become one of the most sought after fields in the current times. With advancements in both software and hardware, building complex mathematical and computational heavy models have become easier and quicker. Introduction of TPU (Tensor Processing Units) have further expedited the process of machine learning models. While all the advancements in the field of hardwares have enabled this change, one notable change has been in the use of python for machine learning algorithms.

Marking a shift from traditionally used language R (which is still preferred by statisticians), python has gained popularity in recent times amongst many python developers and python consultants. Many new 3rd party machine learning tools now provide python support out of the box. Infact, many companies have now started calling themselves python development companies.

Hence, through this series of articles, we will understand python for machine learning and how python consultants can leverage python to build robust machine learning models. As a first article in this series, we will understand different data types in python and their syntax in python.

Let us begin!

There are majorly 7 types of data in python

Text	String (str)
Numeric	Int, Float, Complex
Sequence	List, Tuple, Range
Mapping	Dictionary
Set	Set, Frozenset
Boolean	Bool (True/False)
Binary	bytes, bytearray, memoryview

We will now understand each of the above types in detail.

Text

Strings in python are a stream of characters, contiguous in nature. They are often represented in between single quotes. Strings allow slicing on them, that is, we can use parts of a string to extract substring out of it. One important thing to note is that unlike R, indexes in python start from 0.

Syntax and Output:

We can also use + operator to concatenate the strings.

Numeric

This type of data includes use of integer (signed), float (real values of floating point) etc. These are number inputs. It is important to note that all integer types in Python 3 are long. Hence, there is no separate long type in Python 3.

Syntax and Output:

Sequence

This is probably the most frequently used data type in Python. It finds several applications at different places. The basic data types like numeric and text are also used inside the sequence data type. That is, a sequence data type can contain data of type integer and float. To understand the concept better, let us understand what is a list, tuple and a range.

List

As mentioned above, since sequence data type can contain other data types as well, it is also called a compound data type. Lists are very versatile. List stores a sequence of values. It can be accessed using ‘[ ]’ and can be manipulated using operators like ‘+ and *’.

Syntax and Output:

Tuple

Similar to lists, except the values in the tuple cannot be changed/updated. The size of the tuple can also not be changed once created. In other words, tuples can be thought of as “read-only lists”. One change also lies in the syntax of tuple and list. Tuples are formed using parentheses ( ‘( )’ )

Syntax and Output:

Range

Range is often used in loops, to define a set of numbers over which the counter will iterate. It is used to create a sequence of numbers, with start(included) and end(not included) as its parameters.

Syntax and Output:

Mapping (Dictionaries)

Dictionaries are key-value pairs in python. Like hashtables, we have a key which corresponds to a particular value. When we want to access the value, we use the key corresponding to the value and the value is retrieved.

The key is generally an integer or a string, though they can be any of the python data types. The value can be any python object.

Syntax and Output:

With this, we come to the end of this article. In this article, we learnt how python developers and python consultants can use different data types in different scenarios. A python development company, which is planning to expand into machine learning and AI, should be able to understand and deliver on this basic premise of data types in python. This base can then be used to build great machine learning models.

As promised, as a multi-series tutorial, we will cover “Linear Regression in Python” in the next post.

Until then, bye bye!

June 11, 2020June 11, 2020

Object Recognition Software

Do you know the difference between cat and cactus ? There are chances that you may or your team may , but this is not the most efficient way you utilise your time . That is where object recognition technology are acting as a time saver for many business organisation . It can also facilitate add on functionality for your customer , specially when you are dealing in app which is concern about image categorisation and content management .\

What Is Object Recognition ?

In simple words , we can define object recognition as technology which has capability to recognise objects in picture or videos with the help of AI software . If you have ever used Google Lens or have used Google Photos in your mobile phone , then tan tana… !! You are already familiar with object recognition .
Right from identification of cover of book to identification of plans in wild , object recognition technology has come a long way .
As it is new technology , many business owners really did not know how this technology exactly works and hence had made an assumption that the access to this technology is only possible by heavily investing on this technology . However this is not the case with object recognition . In fact it has tools which can easily be incorporated by even small and mid sized business in their software .
These tools facilities identification of single or multiple objects in photoset or image . Although Facial recognition is also one type of object recognition technology , but is always treated separately due to its complexity in nature and legal concerns .

How Does Object Recognition Work ?

Before proceeding with how object recognition works , let’s quickly look at the difference between object detection and object recognition . Object detection is concern about locating object in image , wherein object recognition is being used to identify object in given image .

Object recognition is being used to find out more specific categories of objects , such as dog or cat . In some cases , object recognition can even be performed without using AI technology . In those cases template matching or image segmentation is being used to recognise objects .

Even more advance object recognition can be done by following one of the two approaches : machine learning and deep learning , both of the technology have their own pros and cons .

Also Read : What Is Machine Learning & Its Applications ?

Machine Learning

We can define machine learning is training of software to perform certain activity , as in this case recognition objects in photo . You have to provide software set of images and then list feature which can later be used in recognition of object .
Major advantage of machine learning is it necessarily does not require large datasets or computing power , and hence cost of software development is always low .
Disadvantage of this technology is it require more efforts of your team to set up completely . If you have inserted limited photos to include in your trading sets , then your software might not able to identify with accuracy as it has not access to broader set of images .

Deep Learning

Deep learning has ability to adapt Manny different functions or activity , but at the same time it requires large data sets , substantial hardware as well as more computation power . In this approach software has ability to train itself using artificial neural network .
In this images are simply to be labeled as ” Cat ” or ” No Cat ” , and then there will be no extra efforts to be put to train software to distinguish cat .
The downside of this approach is it requires massive datasets which may even contain millions of images which might not be easily accessible for smaller companies to access those information .

Why There Is A Need For Object Recognition Software ?

Object recognition can actively be used in various industry to cater their needs for identification of objects and theory reducing human interaction and time . Human may or may not sort and analyse images at the speed that a well trained algorithm can .
Software also has capability of identifying objects that can’t be even be seen by humans . Object recognition software are also able to detect patterns . As a human being we can get tired of viewing same pattern again and again , while on the other hand software won’t . They have the ability to view more images in a year than human in their entire lifetime .

Where To Find Vendor Solution ?

As in case of any other software development , in object recognition also you necessarily need not to start finding solutions right from scratch . If you are not expertise in handling this technology or don’t have access to datasets , you can use TenserFlow Object Detection API from Google .
Apple also offers machine learning API which is called Core ML , which can actively be used to run object recognition model on iOS device or in cloud as well .

Hire Software Development Company For Your Object Recognition Software

If your team does not have any previous experience with artificial intelligence , then it will be quite hard to crack machine learning . That is why it is important to hire right software development team who have deep experience in handing variety of object recognition tools . Whether you are looking to create solution right from scratch or want pre-trained mobile app to fit for your purpose , Winklix can help you to find perfect solution for your need .