Deep learning Neural network for beginners using Iron Man
Summary: This article is to describe Deep learning and Artificial Neural network of AI in Simpler way for beginners with No-code approach.
Table of Contents
- How deep learning model and artificial neural network works?
- What are Layers, activation function and artificial neurons or neurons?
- How to train a Deep learning model?
- Why Forward propagation and backpropagation during training?
- What is Gradient descend ?
- What are Global optima and Local optima?
- Why do we require Optimizers and loss function ?
- How to evaluate a Deep learning model ?
Tony was building ANN to protect city. But how does that works?
Tony Stark, aka Iron Man, wants to build a system that can automatically classify incoming threats to the city. He decides to create an ANN, which is inspired by the way that the human brain processes information.
To build his ANN, Tony starts with a set of artificial neurons, which are like the building blocks of the network. Each neuron takes in input data, processes it, and then passes the output to other neurons in the network. The output of each neuron can be thought of as a prediction or decision.
In order to train his ANN, Tony feeds it a large dataset of past threats and their corresponding classifications. The ANN uses an algorithm called backpropagation to adjust the strength of the connections between neurons so that the network learns to make accurate predictions.
Over time, Tony’s ANN becomes better and better at classifying threats, and it can even start to recognize new types of threats that it hasn’t seen before. This is because the network has learned to identify patterns and features in the data that are associated with different types of threats.
How Tony was architecting Layers, activation function and artificial neurons?
As Tony continues to work on his ANN, he realizes that he can improve its performance by adding more layers of artificial neurons. Each layer is made up of multiple neurons that are connected to each other, forming a network within the overall network.
For example, Tony might add a hidden layer to his network, which sits between the input layer (where the data is fed into the network) and the output layer (where the network makes its predictions). The hidden layer can help the network to learn more complex features and relationships in the data, which can improve its accuracy.
To make the neurons in each layer work together effectively, Tony needs to choose an activation function for each neuron. The activation function is what determines whether a neuron “fires” or not, based on the input it receives.
For example, Tony might choose a sigmoid activation function, which maps the output of each neuron to a value between 0 and 1. This can be useful for binary classification problems, where the network needs to predict whether something is a threat or not.
Finally, Tony realizes that the artificial neurons in his network are the key to its success. Each neuron takes in multiple inputs (such as the data about a specific threat) and computes a weighted sum of those inputs. It then applies the activation function to the result, producing an output that is passed on to other neurons in the network.
By adjusting the weights of the inputs and the choice of activation function for each neuron, Tony can fine-tune the performance of his ANN. Over time, the network becomes better and better at classifying threats.
How his model learns during training?
Tony realizes that a deep learning ANN can potentially achieve even better performance than the original ANN he developed. A deep learning ANN is a neural network that has many hidden layers, making it capable of learning very complex patterns and relationships in the data.
To train his deep learning ANN, Tony starts by preparing a large dataset of past threats and their classifications. He then divides this dataset into three parts: a training set, a validation set, and a test set.
The training set is used to train the network, while the validation set is used to evaluate the network’s performance during training and adjust its parameters. The test set is used to evaluate the network’s final performance after training is complete.
Tony then chooses a deep learning architecture for his network, which involves selecting the number of hidden layers, the number of neurons in each layer, the type of activation function to use, and other design choices.
He decides to use a deep learning architecture called a convolutional neural network (CNN), which is commonly used for image recognition tasks. A CNN is a specialized type of deep learning ANN that is designed to process images and other types of multidimensional data.
To train the CNN, Tony uses a variant of backpropagation called stochastic gradient descent (SGD). SGD is a powerful optimization algorithm that iteratively adjusts the weights of the network to minimize the error between its predictions and the true classifications.
Tony trains the CNN by repeatedly feeding batches of training data into the network, adjusting its weights with SGD, and evaluating its performance on the validation set. He continues this process until the network achieves satisfactory performance on the validation set.
Once training is complete, Tony evaluates the performance of the CNN on the test set to ensure that it is able to make accurate predictions on new, unseen data.
Why Forward propagation and backpropagation during training?
he needs a way to actually train the network to make accurate predictions. This is where forward and backpropagation come into play.
Forward propagation is the process by which input data is passed through the network to produce an output. Tony feeds the network a set of training examples (i.e. past threats and their classifications) and the network produces a prediction for each example.
However, these initial predictions are likely to be inaccurate. That’s where backpropagation comes in. Backpropagation is the process by which the network’s errors are calculated and used to adjust the weights of the connections between neurons. This is done through an algorithm that propagates the errors backwards through the network, adjusting the weights of each neuron along the way.
For example, if the network predicts that a non-threat is actually a threat, the error in the prediction is backpropagated through the network and the weights of the connections between neurons are adjusted so that the network learns from its mistake.
Over time, with repeated cycles of forward and backpropagation, Tony’s network becomes better and better at predicting the classifications of incoming threats. This is thanks to the network’s ability to learn from its mistakes and adjust its weights accordingly.
Now, we need to understand how Tony used stochastic gradient descent for building a new armor which is the part of main threat detection system as well.
Imagine that Tony is trying to build a new suit of armor that can fly faster and more efficiently than his previous models. He has a large dataset of past flight tests, which includes information such as altitude, speed, and fuel consumption.
To design the new suit, Tony decides to use a machine learning algorithm called stochastic gradient descent (SGD). SGD is a powerful optimization algorithm that is commonly used in machine learning to adjust the parameters of a model (such as the weights in a neural network) to minimize its error on a training set.
To apply SGD to the problem of designing the new suit, Tony first needs to define an objective function that measures the performance of the suit based on the flight test data. For example, he might define the objective function as the sum of squared errors between the predicted and actual flight data.
Tony then uses SGD to adjust the weights and other parameters of the suit’s design in order to minimize the objective function. He does this by iteratively feeding batches of flight data into the suit’s simulation, calculating the errors between the predicted and actual data, and using those errors to adjust the suit’s parameters.
Each iteration of SGD updates the weights and parameters in the direction that minimizes the objective function. This process continues until the suit’s performance on the training set reaches a satisfactory level.
Once training is complete, Tony tests the new suit on a separate test set of flight data to ensure that it can make accurate predictions on new, unseen data.
In the end, Tony’s use of stochastic gradient descent helps him design a new suit that is faster and more efficient than his previous models, thanks to its ability to learn from past flight test data and optimize its design parameters accordingly.
But , Tony had to struggle a lot to reach at global optima during designing AI assistant.
Tony is now trying to design a new AI assistant that can help him manage his busy schedule. He has a large dataset of past appointments and their outcomes, such as whether he was on time, whether he was well-prepared, and whether the meeting was productive.
To design the AI assistant, Tony decides to use a machine learning algorithm called gradient descent. Gradient descent is a common optimization algorithm used in machine learning to adjust the parameters of a model (such as the weights in a neural network) to minimize its error on a training set.
Tony starts by defining an objective function that measures the performance of the AI assistant based on the past appointment data. For example, he might define the objective function as the average time he was late for appointments.
The goal of gradient descent is to find the set of parameters that minimize the objective function. However, there may be many different sets of parameters that result in the same (or similar) value of the objective function. These are called local optima.
A local optimum is a set of parameters that minimizes the objective function within a certain region of the parameter space, but is not the overall minimum of the objective function. It is possible for gradient descent to converge to a local optimum instead of the global optimum, which is the set of parameters that minimizes the objective function across the entire parameter space.
In Tony’s case, if the AI assistant is designed using gradient descent and converges to a local optimum, it might perform well on some appointments but not on others. For example, it might be great at ensuring that Tony is on time for most appointments, but not as effective at making sure he is well-prepared for high-stakes meetings.
To avoid getting stuck in a local optimum, Tony might try using different starting points for the optimization process, or try different optimization algorithms altogether. By doing so, he increases the chances of finding the global optimum and designing an AI assistant that performs well across a wide range of appointments.
Optimizers and loss function in his suit. Let’s understand that as well.
Tony is now designing a new suit of armor that can adapt to different combat situations. To build this suit, he needs to use a deep learning algorithm that can learn from past battles and optimize its design to improve performance.
To train the suit, Tony first needs to define a loss function that measures how well the suit performs on a given combat mission. For example, he might define the loss function as the sum of squared errors between the predicted and actual outcomes of the mission, such as the number of enemies defeated, the amount of damage sustained, and the success rate of the mission objectives.
Next, Tony needs to choose an optimizer that can adjust the parameters of the suit’s design to minimize the loss function. There are many different optimizers to choose from, each with its own strengths and weaknesses.
One optimizer that Tony might consider is the stochastic gradient descent optimizer, which we explained earlier. SGD works by adjusting the weights and biases of the suit’s neural network in small increments based on the errors between the predicted and actual outcomes of the combat mission.
Another optimizer that Tony might consider is the Adam optimizer, which is a popular and powerful optimization algorithm used in deep learning. Adam works by adjusting the learning rate of the neural network based on the magnitude of the gradients, which can help prevent the network from getting stuck in local optima.
As Tony trains the suit, he might experiment with different loss functions and optimizers to see which combination works best. He might try different loss functions that focus on different aspects of combat performance, such as speed, agility, and durability. He might also try different optimizers that can adjust the suit’s design in different ways, such as by changing the learning rate, the momentum, or the regularization.
In the end, by using deep learning and experimenting with different loss functions and optimizers, Tony is able to design a new suit of armor that can adapt to different combat situations and perform at peak performance. Whether he’s battling alien invaders or rogue robots, Tony can always count on his AI-powered suit to help him save the day!
But, now Tony need to evaluate all his armors, suits and Stark defense system to protect from cyber attacks.
Tony Stark is designing a new AI system that can detect and prevent cyber attacks on Stark Industries’ computer networks.
To design the AI system, Tony first needs to collect a large dataset of network traffic data and label each data point as either normal or malicious. He then trains a neural network using this dataset to classify new network traffic as either normal or malicious.
After training the model, Tony needs to evaluate its performance to see how well it can detect and prevent cyber attacks. There are several metrics that he can use to evaluate the model’s performance, such as accuracy, precision, recall, and F1 score.
Accuracy measures the proportion of correct predictions made by the model. However, in the case of cyber attacks, the dataset is likely to be imbalanced, with far fewer malicious data points than normal ones. In such cases, accuracy may not be a reliable metric.
Precision measures the proportion of true positives (i.e., correctly classified malicious data points) out of all data points predicted as malicious. Recall measures the proportion of true positives out of all actual malicious data points. The F1 score is the harmonic mean of precision and recall, which balances between them and provides a more comprehensive evaluation of the model’s performance.
Tony may also choose to visualize the model’s performance using a confusion matrix, which shows the number of true positives, true negatives, false positives, and false negatives. This can help him identify which types of errors the model is making and make adjustments accordingly.
By evaluating the model’s performance using these metrics and visualizations, Tony can identify areas for improvement and fine-tune the model to better detect and prevent cyber attacks. This way, he can ensure that Stark Industries’ computer networks remain secure and protected from malicious threats.
