Neural Networks Explained

What Exactly Are Neural Networks and How Do They Mimic the Human Brain?

There’s been a lot of hype lately around the application of artificial intelligence (AI) as a powerful new tool for businesses and organizations to utilize on a daily basis. But few talk about the underlying concept that is actually at the core of it all: neural networks explained. Neural networks form the backbone of any technology that’s able to process information that’s unorganized and very disorganized—in essence, anything more than raw data that a human would normally process in a split second. So rather than running line for line of code to process information, AI runs neural networks, which process information and learn through pattern recognition from data that’s been fed into the system.

There is a huge difference between a program that runs on lines of code and a neural network. A program that runs on lines of code is a program that runs on rules. These rules are usually written in a very linear fashion, and the program follows them step by step. A neural network, on the other hand, is a program that learns by recognition. The network is exposed to data and learns from it. That’s to say, in order to teach a child to recognize cats, you wouldn’t explain to the child that a cat has whiskers on its nose and that those whiskers could be described using analytical geometry, for example. No. You would show the child a lot of pictures of cats until the child’s brain was able to map out the visual patterns that occur in most of the pictures that the child saw. This is how a neural network learns from data.

As an illustration, the structure of a neural network is analogous to our own nervous system:

Artificial Neurons (Nodes): Artificial neurons are the basic units of neural networks and are similar to biological neurons. They receive inputs, process them, and produce an output, which is numerical in nature.
The Input Layer: This is the first point where the network receives information from outside the system, for example, all the individual pixels of a photo, a string of text, etc.
Hidden Layers: This is where the “deep” part of “deep learning” takes place, and the network’s engine goes to work extracting complex features from previous layers to help in learning how to process information for complex tasks.
The Output Layer: The final part of the network. This is where the work is completed by the network in order to analyze information to help make a final decision (for example, to be against)
Synaptic Weights and Biases: These are the internal parameters of a neural network that can be used to fine-tune the accuracy of the neural network. This includes the amplitude of the signal traveling from one neuron to another as well as an offset value or even a simple bias.
Activation Functions: The activation function determines whether or not a neuron “fires” (i.e. outputs a value greater than zero) or not (i.e. it outputs nothing at all).

How Do the Different Layers of a Neural Network Actually Process Unprocessed Data?

By examining the layers within a network, one can gain insight into the architecture of that network. First, the unstructured data that enters a pipeline does not magically turn into a correct answer as it traverses down the pipeline. Instead, it goes through a multi-layered process where each layer builds on the knowledge that the previous layer gained from processing the data.

A typical deep neural network is composed of multiple layers starting with the input layer, followed by one or more hidden layers and finally an output layer. For example, a deep neural network processing a photograph of a car would begin by processing the simplest possible features in the first hidden layer. These features would include the contrast between local groups of pixels and the presence of straight lines. The second hidden layer would then combine these basic lines together into geometric shapes, such as circles and rectangles. Even deeper layers would then process these basic shapes together to recognize individual components of cars, such as wheels, car windows, and car headlights. Finally, the output layer of the network would aggregate information from all of these components together to produce a distribution over the possible objects in the scene, with a high probability for the object being a car in this case and a low probability for all other objects.

This complex architecture requires a specialized workflow to process unstructured information and to convert it into structured information that can be used for decision-making:

Feature Hierarchy Extraction: All the deep layers in the network first extract a feature hierarchy for the input data. The more layers you have the data go through, the more abstract the feature extracted becomes.
In general every node multiplies the data incoming to it (as a matrix) with the weight stored in the connection from the preceding node (as a 1-dimensional vector). This is a standard dot product between two matrices, one of which is a data matrix and the other a weighted vector of size equal to the number of input features. The result of the dot product is a vector (a single row in the resulting matrix), which is then passed through the activation function, together with the constant bias added to it.
Bias: This is a number that is added to the sum of inputs to a node, which can be used to shift the activation function’s output curve (e.g., to move a sigmoid curve to the left).
Non-Linear Mapping: By using activation functions, like the ReLU (Rectified Linear Unit) function, the NN can learn to recognize more complex structures within the input data and not just linear structures.
Forward Propagation: The process of the data going through the network from the input, through all the hidden layers, to the output node of the network.

Why Is SevenMentor Institute the Ultimate Training Ground for Launching an AI and Deep Learning Career?

Global tech markets are undergoing tremendous transformation at a massive scale, and companies are revising salary structures to employ people with the ability to understand the intricacies of machine learning systems. Just reading tutorials online will not be enough for any individual to land a job in today’s cut throat competition. What is required is a structured education framework that equips individuals with relevant knowledge and hands-on experience through industry-relevant projects that can enable them to become employable artificial intelligence engineers.

At SevenMentor Institute, we train students to develop terminal-level skills, i.e., skills to develop a system from scratch. Students learn to develop complex neural network architectures and tune and optimize them to get the best results. Our team of system architects and developers has years of hands-on experience managing production code. This ensures that our students do not get trapped in a traditional academic environment and learn precise technical skills that are required to work as AI engineers in engineering squads of large MNCs.

SevenMentor is best to launch your career with the above features.

Production-Grade Syllabus: The syllabus covers learning to build, train, and then deploy multi-layer neural networks using standard enterprise frameworks such as TensorFlow, Keras, and PyTorch.
Our Live Workshop Environment: Experience Tech Learning unlike academic lectures, where you’d get to participate in a real-world, high-velocity environment of Large MNCs like Samsung, Microsoft, Google, Facebook, Oracle etc.
Placement Assistance: Our Career Cell is focused on the placement of our students. We will help you in the development of your portfolio, fine-tuning your GitHub page, which is public. We will also help you in placement by conducting interviews with top MNCs and recruiting organizations.
Flexible Classroom and Online Formats: We have weekday and weekend as well as live virtual batches.

Different Types of Neural Networks Used by Engineers

Most Neural Networks are of a very different form and can be as specialized as tools in a mechanic’s garage. That is to say, just as a mechanic would not use a car wheel brace to tighten a car’s The software engineers and the AI scientists who design and train these neural networks use the type of network architecture that is best for processing the data that the neural network is to work with.

These are used by software engineers and AI scientists, respectively, to design and train them. They are, typically, used to solve very different problems and are selected on the basis of the type of data under processing. For example, simple problems with unprocessed data are typically simple tabular data problems and are generally solved by Feedforward Neural Networks (FNNs), images by Convolutional Neural Networks (CNNs), and sequential data such as speech, video etc by Recurrent Neural Networks (RNNs).For example, a house price predictor or a customer churn predictor. In these cases, a simple FNN is all that is required. However, as we move to more complex problems that involve spatial data (images) or data that is of a sequential nature (text, audio), we must switch to more complex neural networks that are specifically designed to handle these types of problems.

The current AI applications work in a distinct ecosystem of different types of neural networks. Each of them can be fine-tuned in order to solve very specific problems during the calculation:

Feedforward Neural Networks (FNN): The simplest form of network where data moves in only one direction—forward—from input to output without looping back.
CNN: This is one of the most widely used neural network types today. The core of a CNN are mathematical matrices (or "kernels," "filters," etc.) that are slid over the 2D data. Because of this property, CNNs are especially used for computer vision, facial recognition, and all kinds of medical image processing.
RNN: These models have internal memory loops, allowing them to remember previous input and keep processing it after the input has already been passed to the network. These models are particularly effective for dealing with time series data or sequences in general. There are many different flavors of RNNs.
Long Short-Term Memory Networks (LSTM): A particular development of the simple RNN that is particularly well-suited to remembering long-term dependencies in data and, as a result, is widely used in natural language processing (NLP) and machine translation, for example.
Generative Adversarial Networks (GAN): A dual network structure of a generator that produces data, which is compared by a discriminator on realism. GANs are often used for generation of synthetic data that looks very realistic, such as images or even artwork.
Transformers: As the backbone for all modern Large Language Models (LLMs), the transformer processes entire sentences at once with its self-attention mechanism as opposed to word by word.

How Do Neural Networks Actually Learn? The Mechanics of Backpropagation and Optimization

When a neural network is first created it has no idea what it is doing. All of the weights and biases are initialized to random numbers. As a result, the network’s first predictions will be completely off the mark, resulting in significant errors. However, during the network’s training it will work to correct these errors through a series of adjustments that help the network become smarter over time.

The network is first ‘uninformed’ or ‘unknowledgeable’ when first initialized with random numbers for the weights and the biases, and the predictions or outputs it makes based off of this initial setup are therefore completely inaccurate and purely guesswork. It’s only in the following training period, or learning period, where the network gradually amends errors that were calculated via a particular loss function. This is done via backpropagation (meaning ‘backwards propagation’), where the network goes ‘backwards’ (or is ‘run backward’) from the last layer of the network, the output layer, all the way back to the first layer of the network, the input layer, distributing blame to each of the individual neurons where mistakes have been made along the way.

To correct errors in the network’s output, a number of steps must be taken in a particular order to optimize the network’s performance.

The Chain Rule of Calculus: This is a key step in backpropagation where the loss function, the so-called error, is calculated with the so-called Chain Rule of Calculus. The chain rule of calculus calculates the gradient (direction and rate of change) of the loss function with respect to the internal weights of the network.
Gradient Descent Optimization: The process of altering the NN's internal weights to minimize the error that the NN has made in its predictions.
Learning Rate Calibration: This is a key hyperparameter. It determines the size of the step the optimizer takes with each update of the network’s weights. This size of the step has to be chosen carefully. If the step is too large, the network can get worse instead of better with each iteration of training. If the step is too small, then the network will take too long to train.
Epoch Iterations: Repeating forward and backpropagation for thousands of epochs to go through the entire training data set until error has decreased to a constant value close to zero where it plateaus.

What Are the Biggest Challenges in Training Neural Networks and How Do Developers Fix Them?

As opposed to being able to create a neural network architecture and train it in a clean notebook environment, in a production environment things go horribly wrong because deep learning architecture is a very sensitive piece of code; it is very difficult to make, and even if you get it to work in your training environment, it doesn’t mean it is going to work in your production environment. It is a very fragile piece of code, and if there is even a tiny imbalance in the data you are using to train it or even in the parameters that you are using, the whole thing can completely collapse.

These are the two biggest problems in deep learning today and keep many machine learning engineers up at night. The two problems are overfitting and underfitting. Overfitting a model occurs when a model is too highly customized to the training data set and includes a lot of noise. For such a situation, it will be hard for the model to perform well in predicting unseen data. An underfit model happens when the model is not complex enough (that is, it lacks sufficient layers or units) to identify all the major relationships present in the training data.

Engineers use architectural properties and a battery of debugging techniques to keep their models stable. Here are the techniques we use to solve overfitting and underfitting problems:

Overfitting: Getting a 99% training accuracy but terrible real-world test accuracy due to over-memorization.
Underfitting: network is not complex enough to learn required information in data, resulting in poor performance on any data set (training / test data).
Dropout Regularization: This technique for training deep neural networks is a brilliant way to avoid overfitting by forcing a network to not rely on specific neurons (or a subset of them) to process information by randomly switching off (i.e. dropping) a large fraction of neurons in a layer during a training cycle.
Data Augmentation: Artificially expanding a limited dataset by rotating, cropping, or color-shifting existing images so the network learns generalized features instead of static pixels.
Vanishing Gradients: A problem that occurs in very deep networks, where the gradients of the loss function tend to become very quickly zero as they backpropagate through the layers. This results in the first layers of the network not learning anything.
Early Stopping Regularization: Trains model on data until accuracy on validation data stops increasing and then stops training to prevent the model from training when the model would perform very poorly on real-world test data.

Got Questions? Here Are Some FAQs

1. Do I need to be a math genius to learn and work with neural networks?

No, you don’t need to be a math genius to learn how to use neural networks. Modern development tools for neural networks, such as PyTorch and TensorFlow, handle all the complex math behind the scenes. So, as long as you have a good grasp of the concept of how weights, biases and gradients work, you can immediately start building highly effective neural networks using very simple Python code.

2. What is the main difference between Machine Learning and Deep Learning?

The broad term for the process of a machine learning algorithm to learn to make decisions and answers is “Machine Learning”. In general, Machine Learning algorithms require a lot of effort from human engineers in the form of selecting and isolating relevant features. This process is very similar to selecting the relevant columns from a huge spreadsheet. Deep Learning on the other hand is a subset of Machine Learning. In Deep Learning, the large neural networks with multiple layers automatically learn to discover, isolate, and train to select the relevant features from the raw un-structured data.

3. Why are Graphical Processing Units (GPUs) so important for training neural networks?

Matrix Multiplication is the heart of a Neural Network. The most efficient method to perform such multiplication is by using large amount of small, simple cores instead of few complex cores. Standard CPU is designed to perform complex computations one at a time. On the other hand, GPU has thousands of cores which can perform huge amount of parallel computations. This results in up to 50 times performance boost in training a Neural Network.

4. How long does it typically take to learn neural networks from scratch at SevenMentor?

Three months to Six months is the duration to learn Neural Networks from scratch by SevenMentor’s AI & Deep Learning Training Programs, which are designed for absolute beginners to attain the skills of AI & Deep Learning Experts to deploy at work in just 3 to 6 months, with intensive 3 hours of classroom training held on weekday or 2 hours of classroom training held on weekend and rest of time spent on coding and deployment of the concepts learned in class.

5. Can a neural network run directly on a standard corporate laptop, or do I need cloud access?

You can easily design, write, and test smaller neural networks locally on a modern consumer laptop using basic datasets. However, when it comes to training deep architectures on massive enterprise datasets (like thousands of high-resolution images or millions of text blocks), you will shift to cloud-based development environments like Google Colab, AWS, or SevenMentor’s dedicated laboratory servers to tap into high-end GPU power.

6. What specific job roles can I apply for after mastering neural network architectures?

Mastering these technical concepts opens the door to the most lucrative and high-growth titles in the modern technology sector. Graduates routinely secure positions such as Machine Learning Engineer, AI Data Analyst, Computer Vision Specialist, NLP Developer, Data Scientist, and Research Systems Engineer.

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