Neural Networks Explained: AI Beginner's Deep Learning Guide Episode 12 (Updated June 2026) (Updated June 2026)

Neural networks are the engine behind every AI product you interact with today — from the voice assistant on your phone to the fraud detection system at your bank. What most people don't realize is that the NASSCOM-Deloitte 2025 report projects 1.25 million AI-skilled professionals will be needed in India by 2027, and demand is outpacing supply by a wide margin. TCS, which eliminated 12,000 roles in July 2025 citing automation, simultaneously posted thousands of ML and AI engineering openings — because the systems doing the automating need to be built and maintained by someone. Episode 12 of our AI Beginners Guide breaks down neural networks at the conceptual level that makes everything else — CNNs, RNNs, transformers, LLMs — click into place. We cover neurons, layers, forward propagation, activation functions, backpropagation, and gradient descent.

What Is a Neural Network? Neurons, Layers and Architecture

A neural network is a mathematical function composed of layers of artificial neurons, loosely inspired by the structure of biological brains. Each artificial neuron receives numerical inputs, multiplies each input by a learned weight, sums the products, adds a bias term, and passes the result through an activation function. The output feeds into the next layer, which does the same, until the final layer produces the network's prediction. A neural network with one input layer, one or more hidden layers, and one output layer is called a feedforward network or Multi-Layer Perceptron (MLP). The term "deep learning" simply means a neural network with many hidden layers — deep as in many layers stacked. What makes neural networks powerful is their ability to learn the appropriate weights from data, without anyone explicitly programming the rules. Given enough examples of input-output pairs (a training dataset), a network trained with gradient descent can learn to classify images, translate languages, predict sensor values, or detect fraud — without a single if-else rule written by hand. Architecture decisions — how many layers, how many neurons per layer, which activation functions — are the hyperparameters that practitioners tune. Wider networks (more neurons per layer) can represent more complex functions but risk overfitting. Deeper networks (more layers) can learn hierarchical representations but are harder to train. At Infosys AI Center Pune (Hinjewadi Phase 1), TCS Research (with significant Pune operations), and AI-focused startups like Mihup and Senseforth.ai operating out of Pune, neural network engineers design, train, and deploy models across computer vision, NLP, and predictive analytics domains.

Forward Propagation: How Data Flows Through a Network

Forward propagation is the process of feeding an input through the network layer by layer to get a prediction. Let's trace it for a simple 3-layer network (input, one hidden layer, output). Start with the input vector x — say, a flattened 28x28 pixel image (784 numbers). Layer 1: multiply each input by its corresponding weight in the weight matrix W1 (shape 784 x 128), add bias vector b1 (shape 128), and apply activation function to get h1 — the hidden layer activations (128 numbers). Layer 2 (output): multiply h1 by weight matrix W2 (shape 128 x 10), add b2, apply the final activation (Softmax for multi-class classification) to get the output vector y_hat — 10 probabilities corresponding to the 10 digit classes (0-9). The prediction is the class with the highest probability. The entire forward pass is a series of matrix multiplications and element-wise activation applications — operations that GPUs execute extremely efficiently in parallel, which is why GPU training is so much faster than CPU. At Persistent Systems Pune (Bopodi), Wipro AI Labs (Hinjewadi), and NVIDIA's Pune design center, forward propagation is implemented in PyTorch and TensorFlow using their built-in layer abstractions — Linear, Conv2d, LSTM — which handle all the matrix math automatically. The model.forward(input) call is just setting the chain in motion.

Activation Function	Output Range	Use In	Vanishing Gradient Risk
ReLU	[0, infinity)	Hidden layers (standard default)	Low (dying ReLU possible)
Sigmoid	(0, 1)	Binary classification output	High in deep nets
Tanh	(-1, 1)	RNNs, older hidden layers	Moderate
Softmax	(0, 1), sums to 1	Multi-class output layer	Low
Leaky ReLU	(-infinity, infinity)	Hidden layers (ReLU improvement)	Very low

Backpropagation and Gradient Descent: How Networks Learn

After a forward pass, we compare the network's prediction y_hat to the true label y using a loss function — a number that measures how wrong the prediction was. For binary classification: Binary Cross-Entropy. For multi-class: Categorical Cross-Entropy. For regression: Mean Squared Error (MSE). The goal of training is to minimize this loss. Backpropagation computes the gradient of the loss with respect to every weight in the network — how much each weight contributed to the error. It does this using the chain rule of calculus, working backwards from the output layer to the input layer. The gradient of each weight tells you the direction in which that weight should change to reduce the loss. A large positive gradient means increasing the weight increases the loss — so decrease it. A large negative gradient means decreasing the weight increases the loss — so increase it. Gradient descent applies these updates: new_weight = old_weight minus learning_rate multiplied by gradient. The learning rate controls how large each step is. Too large and the optimization oscillates or diverges; too small and training takes too long. Modern practice uses adaptive learning rate optimizers — Adam (Adaptive Moment Estimation) is the most popular, automatically adjusting each weight's learning rate based on gradient history. Stochastic Gradient Descent (SGD) updates weights after each training example. Mini-batch Gradient Descent (the standard) updates after each batch of 32-256 examples — a balance between gradient accuracy and computational efficiency.

Activation Functions, Loss Functions and AI Career Scope in India 2026

Activation functions determine whether and how strongly a neuron fires. Without them, a neural network is just a linear function — no matter how many layers you add, the whole thing collapses to a single matrix multiplication that can only learn linear patterns. Activation functions introduce non-linearity, enabling deep networks to learn arbitrarily complex input-output mappings. ReLU (Rectified Linear Unit): max(0, x). Returns 0 for negative inputs, x for positive. Fast to compute, avoids the vanishing gradient problem, and is the default for hidden layers in modern networks. Sigmoid: outputs between 0 and 1 — used in binary classification output layers. Suffers from vanishing gradients in deep networks, so rarely used in hidden layers now. Tanh: outputs between -1 and 1 — stronger gradients than Sigmoid but still suffers vanishing gradient issues in very deep networks. Softmax: converts a vector of raw scores to a probability distribution that sums to 1 — the standard output activation for multi-class classification. Career scope: according to AmbitionBox and Glassdoor 2025–26, ML engineers in Pune earn ₹6–10 LPA with 1–3 years experience and ₹14–25 LPA with 5+ years in deep learning or LLM specialization. Named Pune-area AI/ML recruiters: Infosys AI Research Center (Hinjewadi Phase 1, Pune 411057), TCS AI and Data Science (Pune operations), Wipro AI Labs (Hinjewadi Phase 1, Pune), Persistent Systems (Bhau Patil Road, Bopodi, Pune 411020), Qualcomm India AI Research (Hinjewadi Phase 1), and Senseforth.ai (MCCIA Trade Tower, Senapati Bapat Road, Pune 411016). ABC Trainings' AI Powered Application Development workshop covers neural networks, deep learning with TensorFlow and PyTorch, and LLM application development from this conceptual foundation.

Maharashtra's CMYKPY (Chief Minister Yuva Karya Prashikshan Yojana) pays ₹6,000–10,000 per month while you complete approved industrial training. ABC Trainings' AI Powered Application Development workshop in Pune covers the complete deep learning stack — from neural network fundamentals through TensorFlow, PyTorch, and LLM-based application development — aligned with PMKVY 4.0. Apply for CMYKPY alongside your enrollment and fund your AI education with a government stipend. Students from our Pune Wagholi and Hadapsar batches have successfully used this in 2025–26.

FAQs

What is the difference between machine learning and deep learning?

Machine learning is the broader field of algorithms that learn from data — this includes linear regression, decision trees, random forests, SVMs, and neural networks. Deep learning is a subset of machine learning specifically using neural networks with many layers (deep architectures). Not all ML is deep learning, but all deep learning is ML. Deep learning tends to outperform classical ML on unstructured data (images, audio, text) when large datasets are available; classical ML often works better on small tabular datasets.

Why do neural networks need activation functions?

Without activation functions, a neural network is purely linear — stacking multiple linear transformations just produces another linear transformation. No matter how many layers you add, the entire network can only learn linear relationships between inputs and outputs. Activation functions introduce non-linearity at each layer, allowing the network to approximate arbitrarily complex functions. ReLU is the current standard because it is computationally cheap, avoids the vanishing gradient problem, and empirically trains faster than Sigmoid or Tanh.

What framework should I use to build neural networks — TensorFlow or PyTorch?

Both are excellent. PyTorch is currently the dominant research framework, favoured for its dynamic computation graph (define-by-run), clean Python API, and flexibility for custom architectures. TensorFlow (with Keras) is widely deployed in production systems and mobile applications, and has strong tooling for deployment (TensorFlow Serving, TensorFlow Lite). For learning, PyTorch is generally easier to debug. For production deployment, TensorFlow or ONNX-converted PyTorch models are both strong choices. ABC Trainings covers both in the AI Powered Application Development workshop.

What AI and ML jobs are available in Pune and what do they pay?

Pune has a growing AI and ML job market across large services firms and product companies. Infosys AI Research Center (Hinjewadi Phase 1) and Wipro AI Labs hire ML engineers at ₹6–10 LPA for 1–3 years experience. TCS AI and Data Science roles at their Pune operations pay ₹7–12 LPA. KPIT and Qualcomm India (Hinjewadi) hire computer vision and embedded AI engineers at ₹10–22 LPA. Startups like Senseforth.ai and Mihup offer ₹8–18 LPA for NLP and LLM engineers with hands-on project experience. The NASSCOM-Deloitte 2025 report's 1.25 million AI professional gap means demand will remain strong through at least 2030.