Be a top AI engineer with AI Tools & Frameworks

Artificial Intelligence (AI) tools and frameworks are helpful to combine computer science, information engineering, linguistics, and AI into one and programming the system to process and analyze large datasets. Knowledge of these tools and frameworks is considered among the top AI skills.

The AI engineer must perform tasks extensively on Natural Language Processing (NLP) that includes language, audio, and video processing by leveraging many NLP libraries, frameworks and tools.

To build AI models with unstructured data, an artificial intelligence professional should know deep learning algorithms and the ways those can be applied using a framework.

A few of the tools & frameworks used in AI are:

1.Scikit-learn

It is one of the highly popular among AI, machine learning frameworks developed by David Cournapeau in the year 2007, working with classical Python and machine learning algorithms. It functions as a major support system for several learning algorithms that are both supervised and unsupervised. Python machine learning by example also includes linear, logistic regressions, classification, decision trees, dimensionality reduction, k-means, model selection, pre-processing, and clustering.

Scikit-learn is developed one the two major basic libraries of Python machine learning that are NumPy and SciPy. Tasks such as ensemble methods, data transformation, and feature selection are quick to implement within a few lines. This framework offers ample of inputs to at the beginner’s level.

2. TensorFlow

TensorFlow was mainly built for Google’s internal usage by the Google Brain team and was launched in November 2015 under Apache License 2.0. It uses a system of multi-layered nodes in ml with python that are quickly allowed to train, set up, and deploy artificial neural networks that have large datasets. The most interesting factor of this is that while a program is written in Python, compiling and running it either on CPU or GPU is possible. So, rewriting it again on C++ or CUDA level to run on GPUs.

3. Pandas

It is quite a popular framework that is majorly used by artificial intelligence professional with support for fast, flexible, and expressive data structures crafted to work on both “relational” and “labeled” data. Pandas offers an insight into Python machine learning by example in different high-level data structures that are easier in usage. There are ample of inbuilt process for grouping, combining data, filtering and also for performing time series analysis. Pandas can effortlessly get information from several sources such as Excel, JSON, CSV, SQL databases, and manage the data for performing functionalities on it.

The two kinds of data structures utilized by pandas are:

· Series (1-dimensional)

· DataFrame (2-dimensional)

Aforementioned two data structures can manage many essentialities as well as use cases from the fields like science, statistics, social, finance, analytics and other spaces of engineering.

Pandas perform and well support many kinds of data like:

· Arbitrary matrix data with heterogeneous, homogeneous kinds of data in the rows and columns.

· Data from the SQL table or Excel spread sheet mostly tabular data with columns.

· The frequency of time series ordered and unordered data is not required to be fixed, unlike other libraries and tools.

· Pandas robust in handling uneven time-series data.

· Pandas data structure can also process it even without labelling.

· Other forms of statistical or observational data sets.

4. Theano

Theano is also a great framework used by an AI engineer. It is quite alike to NumPy and is highly used for numerical computation. It considers the best benefit of a computer’s GPU that directly offers creating data-intensive calculations up to 100 times quicker than while running on the CPU. Theano is a quick AI tool with python that makes it very important for deep learning and several computationally complex tasks.

5. PyTorch

PyTorch has several kinds of tools and libraries that are very supportive in ML, computer vision, and natural language processing. The PyTorch framework is open-source and is developed on the Torch library. The PyTorch library is quite simpler to learn and use. It can smoothly integrate with the python data science.

PyTorch also offers access to developers to perform several functions on Tensors. It also has a robust framework to create computational graphs, and can also alter the runtime. There are several other advantages of PyTorch like multi GPU support, simplified pre-processors, and custom data loaders.

6. SciPy

It is one of the basic and popular tools of AI. It contains different modules for statistics, integration, optimization, and linear algebra. This library acts as the core package that makes up the SciPy stack. It is quite useful tool for artificial intelligence professionals while manipulating the images.