It is possible we are at the peak of AI’s hype cycle. This doesn’t change the inevitability that AI will significantly alter careers in ways that are positive, negative and unknown.
We have spent considerable time in this newsletter exploring how AI impacts the way networks are operated and businesses are transformed.
Of course, AI also has significant implications for the people running these networks and businesses.
Rakuten Symphony Cloud Business Unit President Partha Seetala recognizes this reality and has introduced a multi-part web training series he calls “A Comprehensive and Intuitive Introduction to Deep Learning” (CIDL).
Partha posits that AI competence now puts non-adopting careers and products at a potential disadvantage.
Learning AI is not easy. Topics are highly technical and cover a range of fields without offering an intuitive or clear entry point for people new to the technology. Most tutorials focus on heavy math, creating an element of inaccessibility for many. Some are simply too superficial or only cover concepts in a bespoke manner that doesn’t offer a complete picture.
Partha plans to offer several seasons of his CIDL training. The first, which debuted this summer, is an intuitive, comprehensive and accessible introduction with value to anyone interested in working in the field of AI.
This month, in our Zero-Touch Telecom newsletter, we will share the key takeaways from each episode of season one and share our perspective on how the concepts Partha discusses can be adapted by telco operators.
An intuitive introduction to neural networks
Season one of CIDL offers an introduction to neural networks. In episode one, Partha focuses on the data structure and algorithm that powers deep learning and how neural networks work.
We know that as AI and automation transform telecom, neural networks will play a pivotal role. Understanding the intricacies of these networks, their learning processes and applications in telco settings can create a competitive advantage for people in related careers.
Partha kicks off the first episode exploring the foundations of neural networks, citing Frank Rosenblatt’s 1958 invention of the “perceptron,” which is one of the earliest artificial neural networks capable of learning from data. Inspired by the human brain, these networks process information through layers of neurons that can recognize patterns and make predictions. In telecom, this is a critical capability for network operations that handle massive data volumes.
Key concepts covered in this episode include:
- Forward pass and backward pass learning processes. How data is processed through the network to make predictions and how network parameters are adjusted to minimize prediction errors.
- How neural networks are trained. Data division, attribute scoring and iterative learning concepts are illustrated via an example where elementary students attempt to identify a giraffe based only on specific pieces of information. Through multiple iterations of identification exercises, we see how accuracy improves over time.
- The role of activation functions, gradient descent and learning rate. Activation functions like ReLU that introduce non-linearity into the network to learn complex patterns are explored as well as techniques for adjusting learning rates to fine-tune outputs.
In telecom, these techniques, capabilities and processes are applied to network management requirements like optimization, anomaly detection and customer experience management.
For instance, neural networks can analyze vast datasets to predict peak usage times and optimize resource allocations—just like predicting the next number of a sequence. Neural networks can learn to identify deviations from normal patterns to detect potential network issues early. Predictive models can anticipate customer behavior to trigger proactive measures for enhanced service quality.
The scale of telecom networks will require neural networks to efficiently handle high volume data. Understanding the role of neural networks in new network management approaches and how their capabilities can enhance the tools we use to manage networks is crucial.
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