Neural Networks - Science topic

Accountability only makes sense for the humans that use the output of any classifier, a NN being one example, not for the devices or algorithms that realize the classification. The humans are responsible and liable for implementing any decision-and any decision involves a bias.

The best-suited activation function for real-time scenarios in neural networks depends on the specific task and the desired performance characteristics. However, some of the most commonly used and effective activation functions for real-time applications include:

The choice of activation function depends on various factors such as the type of neural network, the nature of the task, and the desired trade-off between accuracy and computational efficiency. It is often beneficial to experiment with different activation functions to find the one that performs best for a particular application.

Your project idea to compare traditional approaches to building recommender systems with Graph Neural Network (GNN) based approaches is solid and aligns well with the current trends in the field. Using the MovieLens 100k dataset is a good choice, especially for its popularity in recommender system research. However, your choice of dataset ultimately depends on your research questions and the specific aspects you want to investigate.

Here are a few considerations and suggestions:

OpenAI news stream (keeps updated)

I believe the way we view security will change with the advent of Gen AI. Since any lay man will now have access to the most comprehensive and complex scripts(depending on what the model was trained on), it will definitely make it a lot harder to secure the data and infrastructure. My belief is that anything digital and connected is never secure.

We have to accept that our data can be accessed by malicious actors. What we can do is entrap such actors by associating/pegging a tracker and malicious code to all the data we store, and making sure that they can never use/view what they have extracted. So, whenever someone gains access to our data/infrastructure, they not only disclose themselves, but also get compromised through the executable scripts they downloaded. What's important to do is never store any stand alone files, and instead have scripts associated with each file(which shouldn't be able to be removed when extracting this data).

Only certain organization specific software should be allowed to extract the date, in the know that certain scripts will be executed when doing so. Appropriate measures can be taken with respect to specific scripts associated with the data file to prevent the org itself from being the victim.

The Vanishing Gradient Problem in deep learning is like that one elusive sock that always seems to disappear in the laundry. You know it's there somewhere, but no matter how hard you look, it just vanishes into thin air. But unlike the sock, this problem has serious implications for the training of deep neural networks.

So what exactly is this vanishing gradient problem? Well, imagine you're trying to teach a neural network to recognize cats. You feed it a bunch of cat pictures and expect it to learn from them. But here's the catch - the network learns by adjusting its weights based on the error it makes during training. And this adjustment is done using a technique called backpropagation.

Now, let's say you have multiple layers in your network. During backpropagation, the error signal gets propagated backwards through these layers, and each layer adjusts its weights accordingly. The problem arises when this error signal becomes too small as it travels backward through each layer. It gradually diminishes until it practically disappears - hence the name "vanishing gradient."

This vanishing gradient problem wreaks havoc on deep neural networks because those poor little gradients can't provide enough information for proper weight adjustments in deeper layers. As a result, these deeper layers don't learn much from their mistakes and end up being useless blobs of neurons.

But fear not! The brilliant minds of deep learning have come up with some nifty techniques and architectures to tackle this pesky issue. One such technique is called "gradient clipping." It's like putting a leash on those runaway gradients so they don't vanish into thin air anymore. By setting an upper limit on how large or small gradients can be, we ensure they stay within a reasonable range and prevent them from disappearing altogether.

Another approach is using activation functions that are less prone to causing vanishing gradients. For instance, instead of using sigmoid or tanh functions that squash values between 0 and 1, we can opt for ReLU (Rectified Linear Unit) activation function. ReLU is like a superhero that saves the day by only activating when its input is positive, thus preventing gradients from vanishing.

To illustrate how these techniques have solved the vanishing gradient problem, let's consider the example of image classification. In the past, deep neural networks struggled to accurately classify images with many layers due to vanishing gradients. But with gradient clipping and ReLU activation functions, networks like ResNet and DenseNet have achieved remarkable results in image recognition tasks.

In conclusion, the vanishing gradient problem may seem like a disappearing sock in your laundry, but it's a serious issue that affects the training of deep neural networks. Fortunately, techniques such as gradient clipping and using appropriate activation functions have emerged as superheroes to mitigate this problem. So next time you encounter a vanishing gradient, just remember that there are solutions out there - no need to panic!

Reference:

Nguyen-Duc T., Nguyen H., Pham V.T., Nguyen L.M., Tran D.N. (2020) A Novel Approach for Vanishing Gradient Problem in Deep Learning Using Rectified Linear Unit Activation Function. In: Le T., Le N., Hoang D., Pham C., Nguyen N. (eds) Advanced Computational Methods for Knowledge Engineering. Springer Proceedings in Mathematics & Statistics, vol 312. Springer, Cham

Dear Dariusz Prokopowicz ,

Artificial intelligence (AI) is usually defined as the simulation of human intelligence processes by computer systems. It’s become a very popular term today and thanks to its ubiquitous presence in many industries, new advancements are being made regularly.

AI systems are very much able to replicate aspects of the human mind, but they have a long way to go before they inherit consciousness - something that comes naturally to humans. Yet, while machines lack this sentience, research is underway to embed artificial consciousness (AC) into them.

Regards,

Shafagat

Yes!

In fact, Artificial Neural Network models have several points of contact with traditional econometric models.

When applying Artificial Neural Networks to the problem of forecasting economic time series, the choice of input neurons can be made using economic theory, to determine the relevant exogenous variables, and the procedures commonly used in time series analysis, such as autocorrelation and partial autocorrelation functions, tests for unit roots, graphical analysis, etc.

Furthermore, particular characteristics of a time series, such as seasonality, trend and cycle, can be learned by an Artificial Neural Network, thus enabling predictions to be made.

Dear Chhotelal Prajapati ,

To create an equation based on an ANN, you will need to specify the input variables and the desired output, and then design and train the ANN to learn the relationship between the inputs and the output.

Regards,

Shafagat

You can find multiple ways but most of them are in add-ons, just download any app made for that

I know I used youtube tutorial and app for my thesis but don't remmember which one. I might look for it when I'll have time.

Training deep neural networks, especially deep convolutional neural networks (CNNs) and deep recurrent neural networks (RNNs), can be challenging due to various difficulties and obstacles. Researchers and practitioners have devised several techniques to address these challenges. Here are some common difficulties and the corresponding solutions:

Addressing these challenges is an ongoing area of research in deep learning, with new techniques and tools continuously emerging to make training deep neural networks more accessible and efficient.

this file will help you in calculation of RMSE

Dealing with high-dimensional data, especially when using genetic algorithms and neural networks, can be challenging due to the "curse of dimensionality." High dimensionality can lead to increased computational complexity, overfitting, and reduced model generalization.

I can suggest some methods and strategies you may utilize when working with high-dimensional data:

When working with high-dimensional data, it's crucial to experiment with various combinations of the above techniques to find the best approach for your specific problem. Additionally, you may need to consider parallel computing or distributed computing to handle the increased computational requirements that can arise in high-dimensional spaces.

@Sonic2k Dbs thank you very much for your wonderful and detailed suggestions. I am just beginning to learn basic ai but i think your 3rd suggestion is in the right direction. Let's see what the future holds

Dear Malik Stalbert ,

Human technology in health care includes managerial knowledge required to marshal a health care workforce, operate hospitals and equipment, obtain and administer funds, and, increasingly, identify and establish markets.

Regards,

Shafagat

Artificial intelligence definitely could help in neuroscience due to the fast development of AI nowadays. During the coronavirus period, AI helped in fast genome sequencing, and consequently, very fast vaccines have been developed. Similarly in neuroscience requirement for real-time analysis during the treatment of neuroscience patients to find out new proteins and genes for particular functions and diseases also.

It seems your problem is to find isomorphic subgraphs. The search can be accelerated by assigning statistical signatures to the vertices and edges. An example assign the degree of a vertex and the sum of degrees of this vertex's direct neighbors.

Maybe of interest

Regards,

Joachim

@ Dr. Chen, Thank you for consulting with AI bot on behalf of me. It's interesting!

In today's world AI is the hot topic

of modern digital era but that is not ensure

AI not able to replace human intelligence

% Define your input data and labels (adjust as needed) X = randn(100, 10); % Input data (100 samples, 10 features) Y1 = randn(100, 1); % Output 1 (e.g., regression task) Y2 = randi([0, 1], 100, 1); % Output 2 (e.g., binary classification) % Create a neural network architecture inputSize = size(X, 2); numHiddenUnits = 64; inputLayer = imageInputLayer([inputSize, 1, 1]); commonHiddenLayer = fullyConnectedLayer(numHiddenUnits); outputLayer1 = fullyConnectedLayer(1); % Output layer for task 1 outputLayer2 = fullyConnectedLayer(2); % Output layer for task 2 % Create a branch for task 1 branch1 = [ inputLayer commonHiddenLayer outputLayer1 regressionLayer ]; % Create a branch for task 2 branch2 = [ inputLayer commonHiddenLayer outputLayer2 softmaxLayer classificationLayer ]; % Define the layers for the entire network (both branches) layers = [ branch1 branch2 ]; % Create and train the neural network options = trainingOptions('adam', ... 'MaxEpochs', 10, ... 'MiniBatchSize', 32, ... 'Verbose', true); net = trainNetwork(X, {Y1, Y2}, layers, options); % Make predictions X_test = randn(10, 10); % Test input data (10 samples) [Y1_pred, Y2_pred] = predict(net, X_test);

Q4 Ans

New Big Data Analytics and Artificial Intelligence (AI) technologies, including deep learning based on artificial neural networks, have become valuable tools in protecting the biodiversity of the planet's natural ecosystems in several ways:

By leveraging these technologies, researchers and conservationists can gather and analyze data more efficiently, make informed decisions, and implement targeted conservation strategies. This, in turn, enhances our ability to protect and sustain the planet's natural ecosystems and the rich biodiversity they contain.

Dear Geetanjali P.Kale .

The recent success of neural networks has boosted research on pattern recognition and data mining.

Machine learning tasks, like object detection, machine translation, and speech recognition, have been given new life with end-to-end deep learning paradigms like CNN, RNN, or autoencoders.

Deep Learning is good at capturing hidden patterns of Euclidean data (images, text, videos).

But what about applications where data is generated from non-Euclidean domains, represented as graphs with complex relationships and interdependencies between objects?

That’s where Graph Neural Networks (GNN) come in, which we’ll explore in this article. We’ll start with graph theories and basic definitions, move on to GNN forms and principles, and finish with some applications of GNN..

Regards,

Shafagat

Dear Doctor

Go To

"ABSTRACT

Deep Neural Networks (DNNs), with its promising performance, are being increasingly used in safety critical applications such as autonomous driving, cancer detection, and secure authentication. With growing importance in deep learning, there is a requirement for a more standardized framework to evaluate and test deep learning models. The primary challenge involved in automated generation of extensive test cases are: (i) neural networks are difficult to interpret and debug and (ii) availability of human annotators to generate specialized test points. In this research, we explain the necessity to measure the quality of a dataset and propose a test case generation system guided by the dataset properties. From a testing perspective, four different dataset quality dimensions are proposed: (i) equivalence partitioning, (ii) centroid positioning, (iii) boundary conditioning, and (iv) pair-wise boundary conditioning. The proposed system is evaluated on well known image classification datasets such as MNIST, Fashion-MNIST, CIFAR10, CIFAR100, and SVHN against popular deep learning models such as LeNet, ResNet-20, VGG-19. Further, we conduct various experiments to demonstrate the effectiveness of systematic test case generation system for evaluating deep learning models.

CONCLUSION AND FUTURE WORK

In this work, we studied the necessity for testing of DNN models beyond standard accuracy measure. We proposed new metrics to review the quality of test dataset of popular image classification datasets. The metrics were measured on features from penultimate layers of popular models such as LeNet, ResNet-20, VGG-19 on well known datasets like MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and SVHN. We observe that though ResNet performs better than VGG19 on CIFAR-10 in terms of accuracy, the coverage of boundary condition testing is comparatively lesser. Further, we generated more test samples guided by the proposed metrics and we observe a drop in accuracy on the previous best models. For VGG-19, test dataset consisting only of centroid samples, the accuracy significantly even with 100% samples, thereby validating our approach. As part of future work, we consider to improve our algorithm to optimize on the time taken to generate the samples. Our proposed metrics are only for classification tasks, and exploring metrics for test case evaluation for other machine learning tasks like segmentation, regression and modalities like text can be an interesting future work."

Well, maybe I'm not that expert yet and this is just a disquistion but idea can be that AI can select activation functions based on known information about trainning process. Another way can be to change NN structure as we know. Right now DNN is sum of multiplications which we after that put under activation function. What if we use multiplications or powers in some layers instead? How would that influence amount of layers needed? So many questions can be raised but all of them need testing.

In the multifaceted landscape of artificial neural architectures, activation functions emerge as pivotal computational primitives that instantiate non-linearities within the model, thereby amplifying the model's capacity for function approximation in high-dimensional input spaces. As we pivot towards natural language processing (NLP) applications—particularly large language models like transformer architectures—the exigencies for nuanced, adaptable activation functions are exacerbated. Herein, we present a disquisition on the Swish-Gated Linear Unit (SwiGLU), elucidating its mathematical formulations, computational affordances, and empirical efficacy vis-à-vis traditional activation functions in the domain of expansive language models.

Theoretical Underpinnings

The SwiGLU activation function can be mathematically characterized as a convex combination of the input x and a non-linear function f(x), effectively amalgamating aspects of both linearity and non-linearity. The architecture incorporates gating mechanisms, frequently utilized in Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) cells, which facilitate the dynamic recalibration of the information flow within the neural nodes.

SwiGLU(x)=αx+(1−α)f(x)

Here, α is a gating parameter that can either be learned through back-propagation or set heuristically.

Comparative Advantages

Application in Large Language Models

In the deployment of SwiGLU within large language models, the activation function is commonly used in the hidden layers and attention mechanisms, modulating the weight matrices and facilitating a more nuanced understanding of linguistic constructs, semantics, and contextual embeddings. Preliminary empirical analyses evince that models employing SwiGLU outperform counterparts utilizing traditional activation functions in tasks ranging from text classification to machine translation and abstractive summarization.

In summation, the SwiGLU activation function manifests as a veritable computational artifact that augments the modeling acumen of large language models. It amalgamates the advantages of both linear and non-linear functional forms, offering a nuanced mechanism for capturing the multifarious complexities inherent in natural language processing tasks. Its adoption is likely to catalyze advancements in the accuracy, efficiency, and interpretability of contemporary NLP architectures.

this question basically deals with classification, you need to identify the necessary python machine learning libraries, that supports that. as well the necessary functions

Dear Dr. Mohammad Imam Your comment does not carry a semantic load, it looks like a general instruction for creating a package for Python

To my knowledge, there is no dataset for your idea.

Jieyun Bai

Dear doctor

Go To

Neural Networks vs. Random Forests – Does it always have to be Deep Learning?

by Prof. Dr. Peter Roßbach

"How do Neural Networks and Random Forests work? Let’s begin with a short description of both approaches. Both can be used for classification and regression purposes. While classification is used when the target to classify is of categorical type, like creditworthy (yes/no) or customer type (e.g. impulsive, discount, loyal), the target for regression problems is of numerical type, like an S&P500 forecast or a prediction of the quantity of sales.

Neural Networks Neural Networks represent a universal calculation mechanism based on pattern recognition. The idea is to combine simple units to solve complex problems. These units, also called neurons, are usually organized into several layers that have specific roles.

The Neural Network consists of an input and an output layer and in most cases one or more hidden layers that have the task to transform the inputs into something that the output layer can use. Neural Networks can process all kinds of data which is coded in numeric form. The data is inserted into the network via the input layer, transformed via the hidden layer(s) and finally scaled to the wanted outcome in the output layer. In the case of an assessment of creditworthiness, the input neurons would, for example, take values for income, education, age and house ownership. The output neuron would then provide the probability for creditworthiness.

Random Forests Random Forests belong to the family of decision tree algorithms. A Decision Tree represents a classification or regression model in a tree structure. Each node in the tree represents a feature from the input space, each branch a decision and each leaf at the end of a branch the corresponding output value.

To obtain a result for a specific input object, e.g. a person who applies for a credit, the decision process starts from the root node and walks through the tree until a leaf is reached which contains the result. At each node, the path to be followed depends on the value of the feature for the specific input object. In figure 3 for example the process walks to the left, if the person has an income lower than 3000.

Similar to Neural Networks, the tree is built via a learning process using training data. The learning process creates the tree step by step according to the importance of the input features in the context of the specific application. Using all training data objects, at first the most important feature is identified by comparing all of the features using a statistical measure. According to the resulting splitting value (3000 for income in figure 3), the training data is subdivided. For every resulting subset the second most important feature is identified and a new split is created. The chosen features can be different for every subset . The process is now repeated on each resulting subset until the leaf nodes in all the branches of the tree are found.

Summary The intention of this blog was to show that Neural Networks, despite their current high visibility in the media, not always need to be the first choice in selecting a machine learning methodology. Random Forests not only achieve (at least) similarly good performance results in practical application in many areas, they also have some advantages compared to Neural Networks in specific cases. This includes their robustness as well as benefits in cost and time. They are particularly advantageous in terms of interpretability. If we were faced with the choice of taking a model with 91% accuracy that we understand or a model with 93% accuracy that we don't currently understand, we would probably choose the first one for many applications, for example, if the model is supposed to be responsible for investigating patients and suggesting medical treatment. These may be the reasons for the increasing popularity of Random Forests in practice."

Hello

the choice of gaussian distribution for displacement can be selected for smoothness, local coherence, limited range, and easy to control reasons. But in CNN document it is not explained.

Convolutional Neural Networks (CNNs) have been highly successful in various image analysis tasks, including cancer detection. However, traditional CNNs treat all image regions equally when making predictions, which might not be optimal when certain regions contain critical information for cancer detection. To address this, incorporating an attention mechanism into CNNs can significantly improve performance.

Attention mechanisms allow the model to focus on the most informative parts of the image while suppressing less relevant regions. The attention mechanism can be applied to different levels of CNN architectures, such as at the pixel level, spatial level, or channel level. By paying more attention to relevant regions, the CNN with an attention mechanism can enhance the model's ability to detect subtle patterns and features associated with cancerous regions in medical images.

When using CNNs with attention mechanisms for cancer detection, it is crucial to have a sufficiently large dataset with labeled medical images to train the model effectively. Transfer learning with pre-trained models on large-scale image datasets can also be useful to leverage existing knowledge and adapt it to the cancer detection task with a smaller dataset.

Remember that implementing and training deep learning models for cancer detection requires expertise in both deep learning and medical image analysis. Additionally, obtaining annotated medical image datasets and ensuring proper validation and evaluation are essential for developing an accurate and robust cancer detection system. Collaborating with medical professionals and researchers is often necessary to ensure the clinical relevance and accuracy of the developed methods.

To create a neural network with numerical values as input and an image as output, we can use a deep learning library like TensorFlow or PyTorch. In this example, I'll provide you with a simple implementation using TensorFlow and Keras. This implementation will demonstrate how to generate images from random numerical values using a fully connected neural network. Keep in mind that for more complex image generation tasks, you might need a more sophisticated architecture like a Variational Autoencoder (VAE) or a Generative Adversarial Network (GAN).

Before running the code, make sure you have TensorFlow and Keras installed. You can install them using pip:

pip install tensorflow keras

import numpy as np import tensorflow as tf from tensorflow.keras import layers, models # Define the input size for the numerical values input_size = 100 # Define the output image size (e.g., 28x28 grayscale image) output_image_size = (28, 28, 1) # Function to create the generator model def create_generator(): model = models.Sequential() model.add(layers.Dense(256, input_dim=input_size, activation='relu')) model.add(layers.Dense(512, activation='relu')) model.add(layers.Dense(1024, activation='relu')) model.add(layers.Dense(np.prod(output_image_size), activation='sigmoid')) model.add(layers.Reshape(output_image_size)) return model # Function to create random noise as input for the generator def generate_random_noise(batch_size, input_size): return np.random.rand(batch_size, input_size) # Function to create and compile the combined model def create_combined_model(generator, optimizer): generator.trainable = False model = models.Sequential([generator]) model.compile(loss='binary_crossentropy', optimizer=optimizer) return model # Main function def main(): # Generator model generator = create_generator() # Optimizer for the generator optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5) # Combined model combined_model = create_combined_model(generator, optimizer) # Number of epochs and batch size epochs = 20000 batch_size = 128 # Training loop for epoch in range(epochs): # Generate random noise as input for the generator noise = generate_random_noise(batch_size, input_size) # Generate fake images using the generator generated_images = generator.predict(noise) # Your code here: Instead of random noise, you should use your numerical values as input and preprocess them accordingly. # Train the combined model by passing the generated images as inputs and all 1s as the target (since they are fake) d_loss_fake = combined_model.train_on_batch(generated_images, np.ones((batch_size, 1))) # Print the progress if epoch % 100 == 0: print(f"Epoch: {epoch}, Loss: {d_loss_fake}") # Save generated images occasionally if epoch % 1000 == 0: # Your code here: Save the generated images using your preferred method (e.g., matplotlib or OpenCV). pass if __name__ == "__main__": main()

Integrating advanced machine learning algorithms with remote sensing data enables precise land cover classification and change detection. These algorithms, hungry for data like a kid for candies, gobble up vast satellite imagery to discern Earth's ever-changing wardrobe. With their digital magic wands, they sort pixels into classes, be it forests or funky urban areas. As time flies, these clever algorithms can spot changes with a keen eye, alerting us to alterations in land cover like fashion police at a runway show. So, let's give these data-craving wizards a chance to unravel the Earth's mysteries with their enchanting algorithms!

Christian Schmidt Thank You

The ValueError: Encountered 'None' gradient error indicates that TensorFlow was unable to compute the gradient for one of its variables. This can happen for several reasons, such as:

To resolve this issue, you can try some of the following solutions:

I hope these suggestions help you resolve your problem. If you continue to experience difficulties, please provide more information about your code.

It seems you're dealing with two key aspects here, finite element analysis (FEA) and neural network-based state prediction.

To integrate the two, you could potentially use the results of the FEA as input to your neural network model. For example, you could perform FEA under different conditions, and use the resulting state of each grid point and its neighbors as training data for the neural network.

Lastly, remember that both FEA and neural networks are complex tools that require a deep understanding of the underlying principles and techniques to use effectively. Always check your results for reasonableness and consistency, and be prepared to adjust your models and methods based on your results as needed.

Dear Fadhil Abdulabbas Abidi ,

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.

Regards,

Shafagat

Various theories have been proposed to understand and explain the nature of consciousness and how it arises from the brain's physical processes.

It's important to note that none of these theories currently explain consciousness fully and satisfactorily. This issue is often called the "hard problem" of consciousness, a term coined by philosopher David Chalmers. It refers to the challenge of explaining why and how we have qualitative subjective experiences, or 'what it is like' aspects of consciousness.

Understanding the nature of consciousness and its relationship to the brain remains one of the most intriguing challenges in neuroscience, cognitive science, and the philosophy of mind. As our scientific and philosophical tools evolve, we may gradually uncover more pieces of this fascinating puzzle.

Deep Neural Networks (DNNs) have emerged as powerful models that outperform Shallow Neural Networks (SNNs) in various domains. One key advantage of DNNs is their ability to learn hierarchical representations of data. Through multiple layers, DNNs progressively extract increasingly abstract features from the input, allowing them to capture complex patterns and relationships. This hierarchical representation learning enables DNNs to better understand the underlying structure of the data and make more accurate predictions.

And also, DNNs possess a larger model capacity compared to SNNs. With a greater number of parameters, DNNs have the ability to capture more intricate variations in the data. This increased capacity allows DNNs to model complex tasks that may be beyond the capabilities of SNNs.

Feature reuse and compositionally are other strengths of DNNs. In deep architectures, features learned in early layers can be reused and combined in subsequent layers, forming more meaningful and sophisticated representations. This feature reuse and compositionally enable DNNs to effectively model and generalize from the data, leading to improved performance.

Efficient gradient propagation is another critical factor contributing to the success of DNNs. DNNs employ back propagation, which allows the gradients to be efficiently computed and propagated through the layers during training. The deep structure of DNNs facilitates better gradient flow, ensuring that the network parameters are effectively updated and optimized.

In summary, DNNs surpass SNNs due to their hierarchical representation learning, larger model capacity, feature reuse and compositionally, efficient gradient propagation, and implicit regularization. These factors collectively contribute to their ability to capture complex patterns, generalize well, and achieve superior performance. Nonetheless, the selection of the appropriate neural network architecture depends on the specific requirements of the task, the nature of the data, and available computational resources.

Georgi Hristov When working with big data, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) can face specific challenges. Here are some of the problems encountered by these neural network architectures in the context of big data:

1. Computational complexity: Big data often implies a significant increase in the volume and complexity of the data. RNNs, CNNs, and GANs require extensive computational resources to process and analyze large datasets. Training and inference times can be considerably longer, requiring high-performance hardware or distributed computing systems.

2. Memory limitations: Large datasets may not fit entirely into the memory available for training or inference. RNNs, CNNs, and GANs typically require storing intermediate computations, model parameters, and gradients, which can exceed memory capacities. Handling memory limitations becomes crucial to ensure efficient processing.

3. Overfitting: Big data can still suffer from overfitting, where models become overly specialized to the training data and fail to generalize well to unseen examples. This issue is especially relevant when training deep neural networks on vast amounts of data. Regularization techniques, such as dropout or weight decay, may be needed to mitigate overfitting.

4. Lack of labeled data: Big datasets might not always have complete or accurate labels, which can hinder supervised learning tasks. RNNs and CNNs often rely on labeled data for tasks like classification or segmentation. Insufficient labeled data can lead to challenges in model training and performance.

5. Training instability: With big data, training neural networks can become more unstable. Gradient updates may oscillate or diverge due to the increased complexity and the potential presence of noisy or misleading patterns in large datasets. Careful selection of optimization algorithms, learning rates, and adaptive learning rate strategies becomes crucial.

6. Data preprocessing and augmentation: Preprocessing big datasets to extract relevant features and ensure data quality can be a time-consuming process. Similarly, data augmentation techniques, which are commonly used to artificially increase the dataset size and enhance model generalization, can become computationally expensive with large volumes of data.

7. Scalability and distributed processing: When dealing with big data, scalability becomes essential. Neural network architectures need to scale efficiently across multiple computing nodes or GPUs to handle the increased workload. Designing distributed training algorithms and ensuring efficient data parallelism or model parallelism is necessary.

These challenges highlight some of the specific problems faced by RNNs, CNNs, and GANs when working with big data. Researchers and practitioners continually work on developing novel techniques and approaches to address these issues and improve the performance and efficiency of neural network models in the context of large-scale datasets.

I know that it is efficient and reliable. I would like to know how to implement it in the lab or how to simulate it in MATLAB , as well as which equipment needs to be used for practical lab.

The number of samples required to apply a Convolutional Neural Network (CNN) reliably can vary depending on various factors such as the complexity of the problem, the diversity of the data, and the desired level of accuracy. While there is no fixed rule, a general guideline suggests that having thousands of samples is often beneficial for training a CNN effectively. However, the actual number of samples needed can vary significantly depending on the specific task and dataset. In some cases, even with just a few hundred or a couple of hundred images, it is possible to achieve reasonable results. Ultimately, it is important to strike a balance between having enough data to capture the underlying patterns and avoiding overfitting, where the model becomes too specialized to the training set.

While neural networks excel at learning patterns and generating outputs based on existing data, creating a completely new language for communication requires a level of abstraction and conceptualization that current neural networks have not achieved. Language development involves complex cognitive processes, cultural and social influences, and shared understanding among users, which are beyond the capabilities of neural networks in their current state. While there have been advancements in natural language processing and generation, creating a truly novel language with its own rules and semantics remains an open challenge for AI research.

Seyed Hossein Ahmadi ,

You should download the dataset from UCI

There it states there that:

"One class is linearly separable from the other 2; the latter are NOT linearly separable from each other"

It is also worth reading where the data came from and how it was collected. For more information read the introductory paper there which is:

The Iris data set: In search of the source of virginica

By A. Unwin, K. Kleinman. 2021

Also the original paper by Fisher can be foundin [1]. I would also get the Anderson papers given in Kleinman before making a determination if it is really an error or not.

[1] THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS BY R. A. FISHER

Available at(https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-1809.1936.tb02137.x)

Hello, Jyoti Mishra ,

The choice between standard or customized neural network models, specifically for convolutional neural networks (CNNs), depends on several factors and the specific requirements of your task. Let's explore both options:

Advantages of Standard Models:

Advantages of Customized Models:

When to Choose Standard Models:

When to Choose Customized Models:

In practice, it is often beneficial to consider a hybrid approach. You can start with a standard model as a baseline and then customize it by adding or modifying specific layers to suit your needs.

Ultimately, the choice between standard or customized models should be based on factors such as available data, task requirements, computational resources, and the level of innovation or customization desired for your project.

.?

After checking the correlations of a single independent variable and the output, the model based on two independent variables (each of them should have a high correlation with the output, but a low mutual correlation) can be created, and MSE can be checked. Then the third type of input can be added, which lowers MSE the best. Then the fourth type of input, and so on, just to the moment that reducing MSE by adding any new independent variable is not possible. It is called the step-up procedure. The step-down procedure is similar, but you start with all types of input data and search for lowering MSE by removing the inputs one by one.

No, they don't have consciousness. But they are able to behave as if they have consciousness. At least it's easier for us to explain this behavior this way than through the real AI structure.

Sandesh Karki . May be you should contact him.

CNN is typically used to deal with higher dimensional data as it provides robust automatic feature engineering. However, CNN does not consider the correlation between instances. The math behind RNN and its variants allows this relationship to be explored and helps create more accurate modeling, especially for dynamically changing data over time. This is why we have sometimes found a combination of the two (e.g. a hybrid LSTM & CNN ). Combining the two characteristics will be very beneficial.

For Me, most of he time I prefer to use LSTM as always dealing with data drift.

Neural networks are currently explored in the field of non-terrestrial networks. Some examples include [1] that optimizes the trajectory of mmWave UAV, [2] optimizes the reconfigurable intelligent surfaces for signal enhancesment, or both [3].

Hope this helps

References:

[1] https://discovery.ucl.ac.uk/id/eprint/10113392/1/UAV_energy_CL_R1.pdf

[2] https://arxiv.org/pdf/2212.02967.pdf

[3] https://openresearch.lsbu.ac.uk/item/9325z

While neural networks have proven to be powerful and effective in many applications, they are not without criticisms. Some of the common criticisms include:

Addressing these criticisms remains an active area of research in the field of neural networks. Techniques such as explainable AI, transfer learning, regularization methods, and uncertainty estimation are being explored to improve interpretability, generalization, efficiency, and robustness of neural networks.

A deep neural network (DNN) typically outperforms a shallow neural network due to its ability to learn complex and hierarchical representations of data. There are several reasons why deep neural networks excel in various tasks compared to shallow networks:

Overall, the depth and architecture of deep neural networks enable them to learn and represent complex patterns, hierarchies, and relationships within the data, leading to superior performance compared to shallow networks. However, it's important to note that the effectiveness of deep neural networks also depends on appropriate network design, regularization techniques, optimization methods, and dataset characteristics.

Yes, addressing ethical requirements is crucial for the acceptance of data analytics by futurists. Future perspectives are likely to emphasize privacy protection, fairness, informed consent, accountability, transparency, and evaluating social impact. Ethical considerations will play a significant role in shaping the future of data analytics.

NEURAL NETWRORKS, WHEN USED FOR PREDICTIVE ANALYTICS IN DATA MINING, WILL USE VARIABLES IN A DATAFRAME to make a prediction of the dependent variable, having subset the data as training and test portions, usually 80 and 20 percent, respectively. I will give an example of a case whereby longitudes, depth of a well, and fluoride levels in a study area are set out as the columns for prediction. the units of longitudes in UTM are in millions, eg, borehole X is located in longitude 5664988, drilled to a depth of 5m and has 0.72 mg/L in fluoride levels content. Now if there are 100 such rows of data and u r given a new row with only depth and longitude, one readily notices that the units of longitude far out overwhelms the other variable units. the values of longitude will overwhelm those of the depths, and this will end up giving wrong answers. This calls for standardization of data--also known as NORMALIZATION. this will make predictions to be undertaken wen al units are similar....hence solves the problem of overfitting.

This may involve computing the average of every column variable, and getting its standard deviation, then using these two parameters to standardize, or just picking the vector value of every column, so that the highest value is substracted from this vector, then divided by these highest value,. This way, the value will range between 0 and 1. the values so-scaled are the ones used for error back propagation until a reasonable ,acceptable error range is attained, so that inverse scaling is now performed to get the predicted value. one may have to do this in software's like R and python as a calculation may go for over 5000 iterations during back propagation ,say, with gradient descent.

model weights and bias will help compute the models convergence--the point at which the model may be taken to have generated n appropriate answer. several good examples abound on YouTube links to that effect, as shown hereunder:-

Dear Wisam Mohammed Abed Alqaraghuli ,

Deep learning neural networks learn how to map inputs to outputs from examples in a training dataset.

The weights of the model are initialized to small random values and updated via an optimization algorithm in response to estimates of error on the training dataset.

Given the use of small weights in the model and the use of error between predictions and expected values, the scale of inputs and outputs used to train the model are an important factor. Unscaled input variables can result in a slow or unstable learning process, whereas unscaled target variables on regression problems can result in exploding gradients causing the learning process to fail.

Regards,

Shafagat

There are several types of deep neural networks, some of the most common types are:

1. Feedforward Neural Networks: These are the most basic type of neural network where the data flows only in one direction, from input to output.

2. Convolutional Neural Networks: These are widely used in image and video analysis tasks, where the features of input data are learned by applying a filter over consecutive patterns.

3. Recurrent Neural Networks: These types of networks are designed to work with sequence data, such as text or speech.

4. Autoencoders: These networks are used for unsupervised learning where the network learns to encode the input data into a low-dimensional representation, and then decode it back to its original dimension.

5. Generative Adversarial Networks: These networks are used to generate new data from the existing data. A generator network and a discriminator network are trained simultaneously to generate new data that can closely match the original data.

Wisam Mohammed Abed Alqaraghuli

Neural Networks have three key issues that have to be considered carefully: Duration of development, amount of data it can handle, and the cost of computation, at times it becomes computationally expensive.

A Convolutional Neural Network (CNN):

The different layers in a typical CNN architecture include:

1) Convolutional layer.

2) Pooling layer.

3) Activation layer.

4) Activation layer and so on....

Neural networks, which are a subset of artificial intelligence, have a diverse range of applications in various fields. Some of the main uses of neural networks include image and speech recognition, natural language processing (NLP), predictive analytics, robotics and automation, gaming and entertainment, fraud detection, and medical diagnosis. Neural networks can be trained to recognize and classify images, analyze and understand human language, predict future outcomes based on historical data, enable machines to learn and adapt to new situations, detect fraudulent behavior, and analyze patient data to predict the likelihood of certain diseases or conditions. As technology advances, the range of applications for neural networks is expected to continue to grow.

Including nonlinearity in neural networks is important because many real-world problems have nonlinear relationships between input and output variables. Nonlinear relationships cannot be modeled effectively using linear models, such as linear regression or logistic regression, which assume a linear relationship between the input and output variables.

Nonlinearities can be introduced in neural networks through activation functions, which introduce nonlinearity into the output of each neuron. Without activation functions, a neural network would simply be a linear function of the input. Activation functions allow neural networks to model complex, nonlinear relationships between inputs and outputs.

For example, in a neural network used for image recognition, activation functions can capture complex features of the image that would be difficult to model with a linear function. The activation function can recognize patterns like edges, curves, and textures, which are often nonlinear features of the image.

Including nonlinearity in neural networks also allows them to learn more complex representations of the input data, which can improve their performance on a wide range of tasks. In addition, nonlinearity allows neural networks to model relationships that are invariant to translation, rotation, and other transformations of the input data.

Overall, including nonlinearity in neural networks is critical for modeling complex, real-world problems with nonlinear relationships between input and output variables. It allows neural networks to capture complex features and relationships that would be difficult or impossible to model with linear functions.

Dear Wisam Mohammed Abed Alqaraghuli ,

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

eural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

https://aws.amazon.com/ru/what-is/neural-network/#:~:text=A%20neural%20network%20is%20a,that%20resembles%20the%20human%20brain.

Regards,

Shafagat

Dear Sami Naili

Your question is interesting. You may go through the following articles for your solution -

% Import the required functions

addpath('functions/'); % Folder containing the fractional derivative functions

% Load training data

load('train_data.mat'); % Replace with your own training data

% Define FONN parameters

N = size(train_data, 2) - 1; % Number of input features

M = 10; % Number of hidden neurons

alpha = 0.8; % Fractional order (0 < alpha < 1)

eta = 0.01; % Learning rate

epochs = 100; % Number of training epochs

% Initialize weight matrices

W = rand(M, N);

b1 = rand(M, 1);

V = rand(1, M);

b2 = rand(1);

% Training loop

for epoch = 1:epochs

for n = 1:size(train_data, 1)

% Forward pass

x = train_data(n, 1:N)';

y = train_data(n, N+1);

% Compute hidden layer output

u = W * x + b1;

z = tanh(u);

% Compute output layer output

v = V * z + b2;

y_hat = v;

% Compute gradients

delta2 = (y_hat - y);

delta1 = V' * delta2 .* sech(u).^2;