Neural Network: Artificial Neural Networks arguably work close enough to the human brain. Conceptually, artificial neural networks are inspired by neural networks in the brain, but their actual implementation in machine learning is far from reality. ANN takes in multiple inputs and produces a single output. It is important to note that ANNs are inspired by the animal brain but nowhere close to biological neural networks.
In this post, we will explore some basic understanding of artificial neural networks and Neural Network Algorithms, their behind-the-scenes working, and a quick glance at their algorithms. This is part 2 of the previous post, – Deep Learning: An Introduction to Artificial Neural Networks.
AILabPage defines artificial neural networks (ANNs) as “Biologically inspired computing code with a number of simple, highly interconnected processing elements for simulating (only an attempt) human brain working and processing information models.” It’s way different than a computer program, though. There are several kinds of Neural Networks in deep learning. Neural networks consist of input and output layers and at least one hidden layer.
- Multi-Layer Perceptron
- Radial Basis Network
- Recurrent Neural Networks
- Generative Adversarial Networks
- Convolutional Neural Networks.
Neural Network Algorithms are based on radial basis functions and can be used for strategic reasons. There are several other models of the neural network, including what we have mentioned above. For an introduction to the neural network and its working model, continue reading this post. You will get a sense of how they work and are used for real mathematical problems.
ANNs learn, get trained, and adjust automatically, just as we humans do. Though ANNs are inspired by the human brain, they run on a far simpler plane. The structure of neurons is now used for machine learning, also called artificial learning. This development has helped various problems come to an end, especially where layering is needed for refinement and granular details are needed.
Exploring Neural Network Algorithms
Neural network algorithms are important parts of modern machine learning. They imitate human brain activity to solve difficult problems. Neural networks are made of connected artificial neurons arranged in layers. They take input data and use weighted connections to process it, gradually transforming and improving the information.
Imagine you have a big box of different shapes and colors of toys, and you want to teach a robot to sort them. A neural network is like a smart robot brain that learns from examples.First, you show the robot some toys and tell it which ones are red and which ones are blue. The robot looks at the toys and tries to find patterns, like the red ones might be bigger or have more corners. It adjusts its brain connections to remember these patterns.
Then, you give the robot new toys it hasn’t seen before. It uses its brain to guess if they are red or blue based on the patterns it learned. Sometimes it might make a mistake, but it keeps learning and getting better.So, a neural network is like a helpful robot brain that learns from examples to figure out things, just like you learn from seeing and doing things many times.
During training, the weights change gradually using backpropagation to make the model’s predictions match the desired results. This helps neural networks to identify patterns, group data, and make predictions very accurately in different areas, like recognizing images and speech or diagnosing medical conditions. Neural networks are becoming increasingly important in artificial intelligence and data analysis because they can easily adapt and process multiple things at the same time.
Neural Network Architecture
Neural networks consist of input, output, and hidden layers. The transformation of input into a valuable output unit is the main job. They are excellent examples of mathematical constructs. Information flow in a neural network happens in two ways.
- Feedforward Networks: In these networks, signals only travel in one direction without any loop, i.e., towards the output layer. Extensively used in pattern recognition. This network with a single input layer and a single output layer can have zero or multiple hidden layers. This method has two common designs, as below.
- At the time of it’s learning or “being trained”
- At the time of operating normally or “after being trained”
- Feedback Networks – In this recurrent or interactive networks can use their internal state (memory) to process sequences of inputs. Signals can travel in both directions with loops in the network. As of now limited to time-series/sequential tasks. Typical human brain model.
Neural Network Algorithms works on three main layers of its architecture i.e input layer, hidden layer (though there can be many hidden layers) and output layer.
Architectural Components – Neural Network Algorithms
- Input Layers, Neurons, and Weights: The basic unit in a neural network is called the neuron or node. These units receive input from an external source or some other nodes. The idea here is to compute an output-based weight associated with it. Weights are assigned to the neuron based on its relative importance compared with other inputs. Now finally, functions are applied to this for computations.
- Let’s assume our task is to make tea, so our ingredients will represent the neurons,” or input neurons, as these are building blocks or starting points. The amount of each ingredient is called a “weight.” After dumping tea, sugar, species, milk, and water in a pan, mixing will transform them into another state and color. This process of transformation can be called an “activation function”.
- Hidden Layers and Output Layers: The hidden layer is always isolated from the external world, hence its name. The main job of the hidden layer is to take inputs from the input layer and perform its job, i.e., calculate and transform the result into output nodes. A bunch of hidden nodes can be called a hidden layer.
- Continuing the same example above, in our tea-making task, now using the mixture of our ingredients coming out of the input layer, the solution, upon heating (the computation process), starts changing color. The layers made up of the intermediate products are called “hidden layers”. Heating can be compared with the activation process; in the end, we get our final tea as an output.
The network described here is much simpler for ease of understanding compared to the one you will find in real life. All computations in the forward propagation step and back-propagation step are done in the same way (at each node) as discussed before. Neural Network Algorithms
Neural Network Work Flow – Layers of Learning
The neural networks learning process is not very different from humans, humans learn from experiences in lives while neural networks require data to gain experience and learn. Accuracy increases with the amount of data over time. Similarly, humans also perform the same task better and better by doing any task they do over and over.
Neural Network Algorithms’ underlying foundation of neural networks is a layer and layers of connections. The entire neural network model is based on a layered architecture. Each layer has its own responsibility. These networks are designed to make use of layers of “neurons” to process raw data and find patterns in it and objects which are usually hidden by naked eyes. To train a neural network, data scientist put their data in three different baskets.
- Training data set – This helps networks to understand and know the various weights between nodes.
- Validation data set – To fine-tune the data sets.
- Test data set – To evaluate the accuracy and records margin of error.
Layer takes input, extract feature and feeds into the next layer i.e. each layer works as an input layer to another layer. This is to receive information and the last layer job is to throw the output of the required information. Hidden layers or core layers process all the information in between.
- Assign a random weight to all the links to start the algorithm.
- Find links to the activation rate of all hidden nodes by using the input and links.
- Find the activation rate of output nodes with the activation rate of hidden nodes and output link.
- Errors are discovered at the output node and to recalibrate all the links between hidden & output nodes are.
- Using the weights and error at the output; cascade down errors to hidden & output nodes. Weights get applied to connections as the best friend for neural networks.
- Recalibrate & repeat the process of weights between hidden and input nodes until the convergence criteria are met.
- Finally the output value of the predicted value or the sum of the three output values of each neuron. This is the output.
- Patterns of information are fed into the network via the input units, which trigger the layers of hidden units, and these, in turn, arrive at the output units.
Deep Learning’s most common model is “The 3-layer fully connected neural network”. This has become the foundation for most of the others. The backpropagation algorithm is commonly used for improving the performance of neural network prediction accuracy. It’s done by adjusting higher-weight connections in an attempt to lower the cost function.
Behind The Scenes – Neural Networks Algorithms
There are many different algorithms used to train neural networks with too many variants. Let’s visualise an artificial neural network (ANN) to get a fair idea of how neural networks operate. By now we all know that there are three layers in the neural network.
- The input layer
- Hidden Layer
- The output layer
We outline a few main algorithms with an overview to create our basic understanding and the big picture behind the scene of these excellent networks.
- Feedforward algorithm
- Sigmoid – A common activation algorithm
- Cost function
- Gradient descent – Applying the learning rate
In neural networks, almost every neuron influences and is connected to each other as seen in the above picture. The above 5 methods are commonly used in neural networks.
Recursive Neural Networks – Call it a deep tree-like structure. When the need is to parse a whole sentence we use a recursive neural network. Tree-like topology allows branching connections and hierarchical structure. Arguments here can be how recursive neural networks are different from recurrent neural networks?
Recurrent Neural Networks – Call it a deep tree-like structure. These neural networks are used to understand the context of speech, text or music. The RNN allows information to loop through the network. Tree-like topology allows branching connections and hierarchical structure. In RNNs data flow is in multiple directions. These networks are employed for highly complex tasks i.e. voice recognition, handwriting and language recognition etc.
Convolutional Neural Networks (CNNs) are an excellent tool and one of the most advanced achievements in deep learning. CNNs got too much attention and focus from all major business players because of the hype of AI. The two core concepts of convolutional neural networks are convolution (hence the name) and pooling. It does this job at the backend with many layers transferring information in a sequence from one to another.
A very young family member of Deep Neural Networks Architecture. Introduced by Ian Goodfellow and his team at the University of Montreal in 2014. GANs are a class of unsupervised machine learning algorithms. So as the name suggests it is called an Adversarial Network because this is made up of two neural networks. Both neural networks are assigned different job roles i.e. contesting with each other.
Example (Chess Game Between Me & My Son)
Lets assume two neural networks“, that compete as opponents of each other, for example, me and my son. If I play chess continuously with my son, my game would surely improve because he is the best. On the back end of this scenario, I would analyze what I did wrong and what he did right. and learn from each mistake. Further, I need to think of a strategy that will help me beat my son in the next game and repeat until I win.
The setup involves a “Neural Network Duel”: two competing neural networks that simulate me and my son playing chess. As I play against my virtual opponent (simulating my son), the neural network observes and learns from my gameplay. After each match, the network analyzes my moves and compares them to the simulated “best” moves made by my son’s network. By identifying mistakes and strategic improvements, I iteratively refine my approach.
This process helps me strategize and adapt to my son’s virtual gameplay, aiming to formulate a winning strategy. Through continuous refinement, I will evolve my play style, ultimately seeking to triumph in the chess duel against my neural network opponent.
Working Of Example
To create a scenario as in the above example where two neural networks learn and adjust their strategies to play chess against each other, we can use a technique called “Reinforcement Learning” with a combination of neural network architectures such as Deep Q-Networks (DQN) or Policy Gradient methods. Here’s how it could work:
- Algorithm Choice: Using a combination of Q-learning and neural networks.
- Q-learning is a type of reinforcement learning where an agent (the neural network) will learn to take actions in an environment (the chess game) to maximize a reward signal (winning the game).
- Network Architecture:
- Two Dueling Networks: Creating two separate neural networks, each representing a player (me and my son). These networks will estimate the “quality” of actions (moves) based on the current board state.
- Deep Q-Networks (DQNs): Each player’s neural network can be based on a DQN architecture, where the input is the encoded state of the chessboard, and the output is a Q-value for each possible move. These Q-values represent the expected future rewards if a particular move is chosen.
- Experience Replay:
- As I play against my virtual opponent (son’s neural network), record the sequence of states, actions, rewards, and next states. Store these experiences in a replay buffer.
- Learning Process:
- Q-Value Update: Periodically sample a batch of experiences from the replay buffer. For each experience, calculate the temporal difference error using the Bellman equation to update the Q-values of the neural network for the player who made the move.
- Policy Improvement: Using an exploration strategy (e.g., epsilon-greedy) to select moves during gameplay. Over time, balance exploration and exploitation to ensure that the networks continue to learn and improve their strategies.
- Adaptive Learning:
- As I analyze my gameplay and identify mistakes, adjust the learning rates, exploration parameters, and neural network architectures to fine-tune the learning process. This can help accelerate learning and improve fusion to better strategies.
- Opponent Modeling:
- My neural network can learn not only from my mistakes but also from the successes of my virtual opponent (my son’s neural network). This process simulates my son’s strategies and adapts to counter them.
- Iterative Improvement:
- Continue playing games against the opponent’s neural network, updating the networks’ parameters, and fine-tuning my strategies based on the outcomes and analysis of each game.
This “Neural Network Duel” setup leverages reinforcement learning concepts and neural network architectures to simulate a learning process similar to how I and my son would improve by playing against each other. In DL it uses many layers of nonlinear processing units for feature extraction and transformation. Over time, the neural networks should adjust their strategies and develop more effective play styles.
Books Referred & Other material referred
- Open Internet reading and research work about Neural Network Algorithms
- AILabPage (group of self-taught engineers) members hands-on lab work.
Points to Note:
When to use artificial neural networks as opposed to traditional machine learning algorithms is a complex one to answer. It entirely depends upon the problem at hand to solve. One needs to be patient and experienced enough to have the correct answer. All credits if any remain on the original contributor only. The next upcoming post will talk about Recurrent Neural Networks in detail.
Feedback & Further Question
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Conclusion – For any effective machine learning model requirement is only one which is reliable data pipelines. We have seen in the post above that ANNs don’t create or invent any new information or facts. ANN help us make sense of what’s already in front of us hidden in our data. Deep Learning, in short, is going much beyond machine learning and its algorithms that are either supervised or unsupervised. ANN’s structure is what enables artificial intelligence, machine learning and supercomputing to flourish. Neural networks are powered by language translation, face recognition, picture captioning, text summarization and a lot more.
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