ARTIFICIAL NEURAL NETWORK IN DEEP LEARNING/PYTHON/ARTIFICIAL INTELLIGENCE

 Artificial Neural Network

• Architecture Component
• Different Types of Artificial Neural Networks
• Applications of Artificial Neural Networks
• Advantages of Artificial Neural Networks
• Disadvantages of Artificial Neural Networks

Artificial Neural Networks (ANNs) are made to replicate how the human brain works, aiming to simulate its functions. They constitute a subfield of artificial intelligence inspired by biological neural networks. Similar to how neurons are connected in the human brain, artificial neuron networks consist of units connected across multiple layers.

Artificial neurons, often referred to as units, are arranged in layers to form the network structure of an ANN. Depending on how complicated the network is, a layer may have a few hundred or millions of units in it. An artificial neural network AI typically consists of intermediate hidden layers, an output layer, and an input layer. External data is fed into the input layer so that the neural network in artificial intelligence may analyze or learn from it. After that, this data is transformed by processing it through one or many other hidden layers. The output layer receives the changed data at this point and provides the network's reaction to the input data.

Interconnections between most artificial neural networks in machine learning units incorporate weights, dictating the impact of one unit on another. As information moves between units, the artificial neural network algorithm accumulates knowledge about the data, leading to an output from the output layer.

Image source original

What is Artificial Neural Network?

The term “Artificial Neural Network” is derived from the biological neural network that is present in the human brain. Neuron AI in artificial neuron networks has another more common name rather than units which is node most commonly we refer to the node for the neurons.

Belo is the typical image of a human neuron.

In artificial neural networks (ANNs), the structure mirrors the structure of biological neural networks (NNs). In ANNs, inputs correspond to dendrites, nodes represent cell nuclei, weights are analogous to synapses, and outputs resemble axons.

This table illustrates the parallelism between biological and artificial neural networks.

Biological Neural Network	Artificial Neural Network
Dendrites	Inputs
Cell nucleus	Nodes
Synapse	Weights
Axon	Output

The artificial neural network and AI emulates interconnected brain cells and is engineered by programmers. The human brain has approximately 100 billion neurons, and in this, each neuron has connections, ranging from 1,000 to 100,000 associations. An effective way to comprehend an artificial neural network example is by considering a digital gate, which accepts an input and produces an output. 

Real-World Example for Artificial Neural Networks

Let’s look at an example, in a big city there is a financial district, that is grappling with a surge in fraudulent transactions. In this financial district, banks were inundated with cases of identity theft and unauthorized access to accounts, causing financial losses and eroding customer trust.

To tackle this situation data scientist Maya turned to artificial neural networks (ANNs). She gathered vast amounts of transactional data, including user behavior, transaction history, and account details.

Using this data, she trained an artificial neural network machine learning model that can detect patterns indicative of fraudulent activity. The network analyzed each transaction in real-time, flagging suspicious behavior such as unusually large transactions, irregular spending patterns, or multiple failed login attempts.

As the ANN model processed more data, its accuracy increased, enabling it to distinguish between legitimate transactions and fraudulent ones with greater precision. This new development ability allowed banks to identify and prevent fraudulent activity before it could inflict financial harm.

How do Artificial Neural Networks learn?

The artificial neural network Python undergoes training using a training dataset. Consider teaching an ANN to recognize a dog: it's exposed to numerous dog images to learn the identification process. Once the training concludes, the network's ability to correctly identify dog images is tested. By presenting new images, the ANN determines whether they depict dogs or not. Human-provided descriptions verify the network's accuracy. If discrepancies arise, backpropagation comes into play. This method involves specifying the weights of the links in the units of the artificial neural network (ANN) and adjusting them according to the error rate. This iterative process continues until the network can effectively identify dogs from the images, minimizing errors.

The architecture of an artificial neural network

To grasp the workings of a neural network, understanding its components is crucial. A neural network comprises numerous artificial neurons, referred to as units, arranged in layers. Let's explore the different types of layers present in every artificial neural network. The diagram below illustrates the various layers within the network.

Image source original

Input Layer: This layer, as implied, receives inputs in various formats provided by users or programmers.

Hidden Layer: Positioned middle of the input and output layers, the hidden layer undertakes calculations crucial for identifying hidden features and patterns within the dataset or training data.

Output Layer: As the name suggests, this layer acts as an endpoint to present the final output to the users. After a series of transformations enabled by the hidden layer, the input is processed, resulting in the result passed through this layer.

The artificial neural network processes inputs by computing the total weighted sum of inputs along with a bias, represented through a transfer function.

This component calculates the weighted sum, which is then passed to the activation function to generate the output. The activation function specifies whether the node should be activated or not. Only activated nodes persist to the output layer. We have many activation functions, therefore we need to carefully choose the most suitable function.

Different types of Artificial Neural Networks

Feedforward Neural Network: It is one of the most common neural networks it operates in a single direction, in this the data moves from the input to the middle layers and from the middle layers to the output layer it does not have backpropagation.

Convolutional NeuralNetwork (CNN): Similar to the feedforward neural network, the CNN incorporates weighted connections between units or nodes, determining the influence of one unit on another. It employs one or multiple convolutional layers that execute convolutional operations on input data and pass the obtained results to subsequent layers. CNNs are extensively utilized in tasks like image processing and speech recognition, particularly in computer vision applications.

Modular Neural Network: Comprising multiple independent neural networks, a modular neural network operates distinctively, with no interaction among its components. Each network handles a specific sub-task with unique inputs. The advantage lies in its modular approach, breaking down the complex computational processes into little components, and reducing complexity while obtaining the desired output.

Radial Basis Function Neural Network: This network takes advantage of the point-to-center distance and uses only two layers. The first layer maps the radial basis functions in the hidden layer, while the output layer computes the resulting output. These networks are typically used in models that represent underlying patterns or features in data sets.

Recurrent Neural Networks(RNNs): RNNs differ by retaining the output of a layer and feeding it back to the input to enhance outcome prediction. It begins similarly to a feedforward neural network, with each node or unit in subsequent layers remembering information from previous steps, functioning akin to a memory cell to improve computational performance.

We will look at these neural networks in much detail in further chapters.

Applications of Artificial Neural Networks

• Social Media: Artificial neural networks play an important role in many social media platforms, on Facebook you might see suggestions for "people you may know", Facebook does this by analyzing user profiles, interests, existing connections, and more to propose potential acquaintances. Facial recognition is another key application, leveraging convolutional neural networks by identifying facial reference points to match them with database records.
• Marketing and Sales: E-commerce platforms like Amazon or Flipkart employ machine learning to recommend a product to the user based on their browsing history. This personalized marketing spans various sectors like food services, hospitality, movies, and books. Artificial neural networks discern customer preferences, shopping history, and dislikes to tailor marketing strategies accordingly.
• Healthcare: Artificial neural networks find extensive use in healthcare, particularly in cancer detection. In oncology, they train algorithms to identify cancerous tissues at microscopic levels with accuracy akin to trained physicians. Additionally, we can use facial analysis using photos to aid in identifying rare diseases in their early stages, which increases the doctor's diagnostic capabilities, and helps the medical sector globally.
• Personal Assistants: Nowadays we often use digital personal assistants like Siri and Alexa they heavily rely on speech recognition and Natural Language Processing (NLP) which in its core uses artificial neural networks. NLP manages language syntax, semantics, speech accuracy, and ongoing conversations, enabling assistants to interact with users effectively.

Advantages of Artificial Neural Networks

• Flexibility to Complex Patterns: Complex patterns that would be challenging for traditional algorithms to detect and understand are among the patterns in data that ANNs specialize in recognizing.
• Learning Capabilities: They can pick up knowledge and get better with experience. ANNs improve their decision-making and forecast accuracy by iterations and changes to its internal parameters (weights).
• Parallel processing is the ability of Artificial Neural Networks (ANN) to carry out operations concurrently across many neurons or nodes. They may handle large volumes of data and carry out intricate computations more quickly because to this function.
• Extrapolating learnt patterns, artificial neural networks (ANNs) may categorize or forecast based on unknown or unseen input. Overfitting is lessened and model performance for new cases is enhanced by this generalizability.
• Feature Extraction: Artificial Neural Networks (ANNs) may independently extract significant features from raw data in some models, like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), therefore minimizing the requirement for human feature design.
• Fault Tolerance: Because ANNs are dispersed and have redundancy in information representation, they may frequently put up with mistakes or missing data without much sacrificing their overall performance.
• Applications of their versatility include time series analysis, natural language processing, picture and audio recognition, and recommendation systems. Their adaptability highlights their efficacy for a range of issues.
• Performance and accuracy of ANNs can be increased over time by their ability to continually store and adapt to new data as it becomes available.

Disadvantages of Artificial Neural Networks

• Computational complexity: Training big neural networks with several layers and neurons takes a lot of processing power, which increases time and energy use.
• Large datasets are necessary for ANNs to be trained to provide good generalizations. Little data might cause overfitting or inadequate generalization.
• Overfitting: Particularly with noisy or little datasets, complex neural network designs are prone to overfit. This problem is lessened by regularization methods or dropout layers.
• Hyperparameter sensitivity: A lot of hyperparameters (learning rate, network architecture, activation functions) in ANNs require fine-tuning. Appropriate value selection can be difficult and have a big effect on model performance.
• Interpretability: Because ANNs are black-box systems, it might be challenging to figure out how they make their judgments. Particularly in fields where openness is essential, this lack of interpretability might be problematic.
• Deep neural network training may be laborious, especially on big datasets, occasionally needing a lot of time and computing power.
• Adversarial Attack Vulnerability: Adversarial attacks can cause false predictions from even minute and undetectable changes to input data.
• Data Dependency: ANNs mostly depend on the calibre and representativeness of the data they are trained on. Predictions from biased or unrepresentative models may be erroneous.
• gear Dependency: Complex neural networks may not be as accessible or practical for some applications or settings if they need specialised gear to develop and train.

Summary

With its linked nodes arranged into layers that process information, artificial neural networks (ANNs) resemble the brain's neural architecture. Through methods like backpropagation, these networks use iterative adjustments of internal parameters to learn from data. ANNs are excellent for classifying and regressing in a variety of fields including finance, natural language processing, and picture identification. Because of their flexibility and ability to manage complex tasks, they continue to be essential in advancing machine learning, robotics, and artificial intelligence (AI) even if they come with difficulties including computational complexity, data dependency, and interpretability problems.

ECLAT ALGORITHM IN MACHINE LEARNING/PYTHON/ARTIFICIAL INTELLIGENCE

 ECLAT Algorithm

• Introduction and Objective
• Itemset Lattice in ECLAT algorithm
• ECLAT Algorithm Working
• Advantages of the ECLAT algorithm
• Disadvantage of the ECLAT algorithm

ECLAT machine learning, or Equivalence Class Clustering and bottom-up Lattice Traversal, is a widely used association rule mining technique similar to the Apriori algorithm. However, it's an optimized and scalable version of Apriori, boasting several key improvements:

• ECLAT operates on the vertical data format of a dataset, unlike Apriori and fp-growth, which work on horizontal transaction data.
• It employs a depth-first search technique for traversing itemsets, contrasting with Apriori, which adopts a breadth-first strategy to explore the transaction dataset.

These adaptations enhance ECLAT's effectiveness and quickness, particularly on dense datasets with few unique items and a high transaction volume. However, it might not match the performance of the FP-growth algorithm when handling sparse datasets with a multitude of distinct items.

What is the Itemset Lattice in the ECLAT Algorithm?

"In the ECLAT algorithm in machine learning, the construction of an itemset lattice mirrors the approach seen in the Apriori algorithm. This lattice serves as a representation of the search space for frequent itemsets, containing these itemsets along with their corresponding support counts.

Generating the itemset lattice involves a recursive process to produce frequent itemsets of increasing size. Recursion operates at each level, where candidate itemsets 'y' are formed by combining itemsets identified in the previous step. Should a candidate itemset meet the minimum support threshold, it earns a place within the lattice. The resulting itemset lattice is stored in memory, typically represented as a tree data structure." 

Real-World Example for ECLAT

To understand the ECLAT algorithm example much better let’s look at a real-world example. Emily owns a grocery store, but she faces the challenge of optimizing her inventory and boosting sales. She has a diverse array of products; she sought a method to identify which items were frequently purchased together by her customers.

To understand her customer buying behavior she uses the ECLAT algorithm, a powerful tool for frequent itemset mining. Emily employed the algorithm to analyze her transaction data, and she found that frequent itemsets represent combinations of products often bought together.

Emily discovered that customers who bought milk were likely to also buy bread and eggs. Similarly, those customers who buy pasta are also likely to buy pasta sauce. After knowing these patterns, Emily rearranged her store layout, placing complementary items closer together to encourage additional purchases.

Emily also uses the knowledge she gains from the ECLAT algorithm to design targeted promotions. She offered discounts on items frequently bought together, such as chips and soda or cookies and milk, encouraging customers to purchase more items during their visit.

ECLAT Algorithm Working

We first determine the minimal support, confidence, and lift levels before applying the ECLAT method. By this specification, if the transactional dataset isn't already formatted vertically, we convert it into one. The algorithm then goes through phases that are comparable to those in the Apriori algorithm: candidate creation, pruning, database search, and rule generation.

Step 1: Converting Transaction Data into Vertical Format

Most transactional datasets typically store data in a horizontal format, where each row includes a transaction ID along with the respective items contained within the transaction, as illustrated below.:

Transaction ID	Items
T1	l1, l3, l4
T2	l2, l3, l5, l6
T3	l1, l2, l3, l5
T4	l2, l5
T5	l1, l3, l5

In a vertical format representation, each row of the transaction data consists of an item and the transactions where that item appears. This format organizes the data vertically, listing items along with the transactions they belong to:

Items	Transaction IDs
l1	T1, T3, T5
l2	T2, T3, T4
l3	T1, T2, T3, T5
l4	T1
l5	T2, T3, T4, T5
l6	T2

Step 2: Candidate Generation from the Dataset

Once the dataset is transformed into a vertical format, the subsequent stage entails candidate generation, aimed at potentially forming frequent itemsets. This process involves identifying combinations of items that may occur together frequently in transactions. This process begins by establishing sets comprising single items. For instance, in a dataset containing N items, N candidate sets are created initially.

The candidate sets are subjected to assessment utilizing the minimum support count to detect frequent itemsets comprising individual items. Following this, a progressive iteration process combines these identified frequent itemsets to generate larger sets that include 2, 3, 4, 5, or even more items.

During the candidate generation phase, frequent itemsets sharing k-1 items in common are merged to form candidate itemsets containing k items. This iterative process continues until no further candidate itemsets can be generated, signaling the completion of the procedure.

Step 3: Pruning the candidate itemsets

The Apriori principle is the foundation for the ECLAT algorithm's trimming stage. This process is predicated on the idea that an item set must be frequent if a subset of it is. Essentially, an item cannot be considered frequent as a whole if it includes a non-frequent subset.

Pruning is necessary to speed up the algorithm's execution since it removes candidate sets before the dataset is scanned to determine support counts. A series of procedures are used to reduce the candidate set when itemsets of K items are generated.

For every candidate set containing k items, the algorithm scrutinizes each subset comprising k-1 items to determine if it meets the criteria for being a frequent itemset. If all of these subsets are deemed frequent itemsets, the candidate set is preserved for further generation of frequent itemsets. Conversely, if any subset is non-frequent, the entire item is discarded or pruned from consideration.

Step 4: Frequent Itemset Generation

The next step is to find the support count of the candidate itemsets that remain after pruning. To determine the support of each frequent itemset requires scanning the transaction dataset.

The review determines the support count of a candidate itemset or the total number of transactions in which it appears. Those candidates who do not meet the minimum support criteria are removed from the list. The remaining itemsets, with support counts surpassing the threshold, are recognized as frequent itemsets.

Once frequent itemsets containing k items are obtained, the process continues by creating candidate itemsets with k+1 items. This involves pruning, scanning the database, and generating frequent itemsets with k+1 items.

This sequence of generating candidate itemsets, pruning, database scanning, and identifying frequent itemsets persists iteratively until no further frequent itemsets can be generated.

Step 5: Association rule generation

After they are generated, frequent itemsets serve as the foundation for association rules. These rules are often expressed in the format {S} → {I-S}, where {S} represents a subset of the frequent itemset {I}. In this notation, {I} denotes the entire frequent itemset, while {S} represents a subset of items within {I}.

we can write the above Eclat algorithm in Python code because Eclat algorithm implementation in Python is the easiest way to understand it.

Advantages of the ECLAT algorithm

• Vertical data structure: ECLAT uses a vertical data layout (transactions represented as lists of items) rather than a horizontal one (transactions as rows), making it memory-efficient and suitable for large datasets.
• Efficiency in Memory Usage: ECLAT optimizes memory usage by representing transaction data concisely, making it more efficient than other algorithms, particularly when handling datasets with sparse structures.
• Fast Algorithm: it’s generally faster compared to Apriori, particularly on datasets with high dimensionality or when searching for high support itemsets, due to its depth-first search strategy.
• Scalability: ECLAT scales well to large datasets as it doesn’t require multiple scans of the database, making it suitable for mining frequent itemsets in big data scenarios.
• Prefix-based Intersection: the algorithm leverages prefix-based intersection strategies to efficiently generate frequent itemsets, minimizing the number of candidate itemsets generated during the search process.
• Ease of Implementation: ECLAT’s straightforward design makes it relatively easy to implement and understand, aiding adoption and adoption for different use cases.
• Mining Diverse Itemsets: It's effective in mining diverse itemsets by efficiently discovering frequent itemsets with varying lengths and support thresholds.

Disadvantages of the ECLAT algorithm

• Memory requirements: Despite being more memory-efficient compared to some algorithms, ECLAT can still demand significant memory, especially when dealing with datasets containing numerous transactions and items.
• Limited Handling of Large Dataset: Although it’s more memory-efficient than Apriori, ECLAT might still face challenges when dealing with extremely large or dense datasets due to memory constraints.
• Need for transaction Identifiers: ECLAT requires transaction identifiers or bit vectors to represent transaction sets, which can add overhead and complexity to the data representation, particularly in scenarios with high-dimensional or sparse datasets.
• Lack of Pruning Techniques: ECLAT might not perform optimally with lower support thresholds, as it can result in a more extensive search space and increased computational requirements.
• High Pruning Techniques: Unlike Some other algorithms, ECLAT might lack certain pruning strategies to efficiently reduce the search space, potentially leading to increased computational overhead.
• High Support Threshold Impact: ECLAT might not perform optimally with lower support thresholds, as it can result in a more extensive search space and increased computational requirements.
• Complexity in Parallelization: Parallelizing ECLAT might be more challenging due to the vertical data structure and the requirement of multiple intersections during the frequent itemset mining process.
• Inefficiency with Low Support Itemsets: In cases where the dataset contains numerous low support itemsets, ECLAT might generate a large number of infrequent itemsets, impacting performance.
• Dependency on Vertical Format: Although the vertical format helps in certain scenarios, transforming data into this format can be a preprocessing challenge, especially when working with data initially presented in a horizontal format.

Summary

ECLAT is a frequent itemset mining algorithm known for its efficient vertical data layout, organizing transactions to efficiently identify sets of items frequently occurring together in datasets. Utilizing a depth-first search strategy and prefix-based intersection techniques, ECLAT efficiently generates frequent itemsets, making it faster than Apriori in certain scenarios, particularly on high-dimensional datasets. Items memory-efficient approach and scalability to large datasets make it suitable for mining diverse itemsets, but it might face challenges with memory constraints and lack some pruning strategies, impacting performance in specific dataset characteristics and support thresholds. Despite this, its simplicity and effectiveness in discovering frequent item sets with varying lengths and support thresholds make it a valuable tool in association rule mining. below is the Elcat algorithm python description.

Python code

let's look at the eclat algorithm Python code: