Technologies Driving Artificial Intelligence

The following is a series of four articles as PowerPoint slides in this blog site  
  1. History of Artificial Intelligence, Machine Learning and Deep Learning in slides
  2. Technologies Driving Artificial Intelligence
  3. Artificial Intelligence and Machine Learning Life Cycle
  4. Applications of Artificial Intelligence A to Z
The slides are collections from my presentations at various skill development programmes. These are meant for the benefit and information of AI enthusiasts, developers and researchers as well, to be aware of those great men and women who contributed to this field of human knowledge,  the key technologies that enable its progress, the general life cycle nature of AI projects and the wide variety of its present and future applications. 

Comments from readers are welcome to improve the content.













 

Comments

  1. 360DigiTMGNoidaMarch 13, 2021 at 2:48 AM

    Nice and very informative blog, glad to learn something through you.
    artificial intelligence course in noida

    ReplyDelete
  2. anjani02October 23, 2025 at 12:47 AM

    Online Business Analyst Course
    Develop core business analysis skills—requirements gathering, documentation, and process improvement. Gain job-ready expertise through live case studies and tools.

    ReplyDelete
  3. Tony SFebruary 2, 2026 at 2:59 AM

    This piece gives a solid look at the core technologies that power modern AI. It’s helpful to see how Computer Vision Services contribute to real-world applications. The mention of Natural Language Processing Services also shows how machines are learning to interpret human intent. A clear and accessible explanation for anyone interested in the field.

    ReplyDelete
  4. DevangAugust 23, 2026 at 11:37 PM

    Interesting overview of the technologies driving artificial intelligence and how they continue shaping modern digital solutions. It's clear that **AI Integration Companies**, **Python Development Companies**, and **Next.js Development Companies** each play an important role in bringing these innovations into real-world applications. Finding the right team to **Hire MERN Stack Developers** is also valuable for building scalable AI-powered platforms. Helpful references include AI Integration Companies, Hire MERN Stack Developers, Next.js Development Companies, and Python Development Companies.

    ReplyDelete

Post a Comment

Popular posts from this blog

Modeling Threshold Logic Neural Networks: McCulloch-Pitts model and Rosenblatt’s Perceptrons

Image
Biological neurons exhibit a spiking output behavior in response to its inputs (refer Figures 10, 11 of " Essential biology and electrochemistry for Deep Learning " ). Inputs signals from the dendrites collectively raise the internal electric potential of the cell. Once this internal potential reaches or crosses the threshold value  the neuron fires. This is a nonlinear behavior During the formative years ANNs (1943-1958), McCulloch-Pitts introduced the idea of neural networks as computing machines. Frank Rosenblatt in 1962, and later Minsky and Papert in 1988 developed a large class of artificial neural networks called Perceptrons. McCulloch-Pitts model Warren Sturgis. McCulloch a neurophysiologist and Walter Harris Pitts a logician published a paper “A logical calculus of the ideas immanent in nervous activity”, 1943 which introduced the simplest artificial model of the biological neuron. McCulloch and Pitts modeled the nonlinear behavior with threshold logic function. ...
Read more

Artificial Intelligence and Machine Learning Life Cycle

Image
The following is a series of four articles as PowerPoint slides in this blog site   History of Artificial Intelligence, Machine Learning and Deep Learning in slides Technologies Driving Artificial Intelligence Artificial Intelligence and Machine Learning Life Cycle Applications of Artificial Intelligence A to Z The slides are collections from my presentations at various skill development programmes.  These are meant for the benefit and information of AI enthusiasts, developers and researchers as well, to be aware of those great men and women who contributed to this field of human knowledge,  the key technologies that enable its progress, the general life cycle nature of AI projects and the wide variety of its present and future applications.  Comments from readers are welcome to improve the content.  
Read more

Regularization and Generalization in Deep Learning

Image
About Regularization and Generalization Regularization is one of the most important concepts in machine learning. In mathematics and statistics, finance, computer science, machine learning and inverse problems regularization is the process of adding information to solve an ill-posed problem. In the context of machine learning optimization problems, it applies a modification to the objective functions to reduce generalization error of the learning model even at the cost of increased training error. Generalization refers to the capability of a trained model to make the right predictions when faced with unknown input data during its operational life. In the rest of this article, we try to gain an intuitive understanding of the mathematical basis of regularization theory for inverse problems and its application to improve the generalization performance of learning algorithms. I have included some mathematical content to emphasize that, there is sufficiently strong mathematical founda...
Read more