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Neural Networks News
Medium
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The Future of the NFL: Predicting Plays Using Artificial Neural Networks
- Using neural networks, a team of researchers sought to accurately predict NFL plays, which could help defensive coaches in selecting an optimal defense strategy that could thwart the opponents, as well as bring a premature end to dominant teams like the Patriots.
- The team based their model on NFL data sourced from the nflfastR, which contains over 350 variables and play-by-play data stretching back to 1999.
- The model, which used Long Short-Term Memory (LSTM) algorithms, managed 69.5% accuracy when applied only to the Patriots between 2012 and 2020.
- However, the team found that the correlation between certain features and play-type was insufficient, with several correlated parameters occurring only after the play.
- The team improved the model's accuracy by 4% by adding more data from all NFL teams from all available years and utilizing more features.
- The model could not guarantee perfect accuracy in predicting play-calling, but could be leveraged as a helpful tool in decision-making by coaches.
- Using more data was found to be more important than targeting specific coaches, as highly unpredictable coaches like Belichick could hinder model accuracy.
- Models like these could bring new rule considerations to the game, forcing the NFL to decide on how to handle extreme advantages that accurate models could procure for teams.
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Medium
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Activation Functions in NN
- The activation function called 'No Activation' simply outputs the given value without any processing.
- The Sigmoid activation function is used for binary classification tasks, providing outputs in the range of [0,1].
- The Tanh activation function is used for cases where three output cases are required: negative, neutral, and positive, with a range of [-1,1].
- The Rectified Linear Unit (ReLU) activation function returns the input value for positive inputs, and 0 for negative inputs, making it popular in neural networks.
- The Leaky ReLU activation function is an improved version of ReLU, allowing small non-zero outputs for negative inputs.
- The Softmax activation function is used for multiclass classification, producing a probability distribution over multiple classes.
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Medium
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Remote Working And AI Tools: The 2024 Landscape
- Remote work has given rise to digital nomads who leverage technology to work while traveling.
- Remote entrepreneurship allows businesses to tap into a global talent pool and save on overhead costs.
- Challenges include communication and collaboration difficulties and the need for proper mental health care.
- Advancements in technology like VR, AR, and AI tools are shaping the future of remote work and entrepreneurship.
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Medium
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Simple Explanation of Kolmogorov-Arnold Networks (KANs)
- Kolmogorov-Arnold Networks (KANs) are neural networks that feature learnable activation functions on the edges of the network.
- KANs do not use traditional linear weights and offer enhanced interpretability and visualization compared to MLPs.
- Empirically, KANs outperform MLPs in various tasks and require fewer parameters.
- KANs are suitable for scientific research and have applications in discovering new mathematical and physical laws.
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Medium
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Kolmogorov-Arnold Networks: The Fresh Approach Shaking Up AI
- Kolmogorov-Arnold Networks (KANs) are shaking up AI by reimagining activation functions within neural networks.
- Unlike Multi-Layer Perceptrons (MLPs), KANs use flexible, learnable univariate functions as weights and activation components.
- This innovative approach allows KANs to fluidly adapt information flow as they are trained.
- KANs have the potential to tackle complex tasks in more capable and intuitive ways compared to traditional models.
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Medium
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What are Convolutional Neural Networks (CNN)? The Art of Computer Vision For Beginners
- Convolutional Neural Network or CNN is a type of neural network used for computer vision tasks.
- CNNs have unique architecture that allows them to process images efficiently.
- The main building blocks of CNNs are the convolutional layer, pooling layer, and flatten layer.
- CNNs are able to detect features and objects by using multiple layers of convolutional filters.
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Medium
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Discover the secret of predicting cryptocurrency prices using neural networks!
- Neural networks like Recurrent Neural Networks (RNN), Convulsive Neural Networks (CNN), Convolutional Recurrent Neural Networks (CRNN), and Generative Adversarial Neural Networks (GAN) can be used to predict cryptocurrency prices.
- These neural networks can analyze time series data, including cryptocurrency price data, to identify patterns and make informed investment decisions.
- Using neural networks, traders and investors can analyze large amounts of data and uncover hidden patterns in the cryptocurrency market.
- While neural networks can provide valuable insights, it's important to note that market risks and unforeseen events can still impact cryptocurrency prices.
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Medium
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Feature importance in Neural Network
- The importance of features in a neural network is determined by how much the output changes when a feature is changed by 1 unit.
- The process of calculating feature importance is similar to how parameters are adjusted in a neural network using partial derivatives and the chain rule.
- To compute feature importance, all possible routes from the input to the output are considered, and the weights and derivatives of activation functions along these routes are multiplied.
- The resulting multiplications for each input are summed to determine the overall feature importance.
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Analyticsinsight
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Neural Networks Power Explosive Growth in $7.9 Trillion AI Sector
- The global economy of Neural Networks may be improved by 7.9 trillion dollars per year through generative AI.
- Major players in the generative AI sector include Tesla, Accenture, Palantir Technologies, ServiceNow, and the Future of Artificial Intelligence Corp.
- Scope AI Corporation is widening its problem space and appointing a new CEO to ensure sustainable growth.
- Tesla focuses on artificial intelligence with their High-Self Drive software, Accenture adopts a 'Human by Design' approach, Palantir pioneers AI-enabled solutions, and ServiceNow provides AI-powered workflow solutions.
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Medium
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A Simplified Explanation Of The New Kolmogorov-Arnold Network (KAN) from MIT
- The Kolmogorov-Arnold Network (KAN) is a new architecture from MIT that promises to revolutionize neural networks.
- KAN redefines the role of activation functions by incorporating univariate functions that act as both weights and activation functions.
- This innovative approach allows for activation at edges and modular non-linearity, potentially enhancing learning dynamics and input influence on outputs.
- KAN has the potential to enable networks that are fundamentally more capable of handling complex tasks.
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Semiengineering
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Research Bits: April 30
- Researchers from the Max Planck Institute for the Science of Light and Massachusetts Institute of Technology have developed reconfigurable recurrent operators based on sound waves for photonic machine learning. The method allows optical neural networks to be programmable on a pulse-by-pulse basis without complicated structures and transducers.
- Scientists at the University of Florida have built a 3D ferroelectric-gate fin nanomechanical resonator that enables spectral processors to integrate different frequencies on a monolithic chip for wireless communications. The processors deliver enhanced performance and have indefinite scalability.
- Researchers from MIT and MIT-IBM Watson AI Lab have developed an on-device digital in-memory compute machine learning accelerator that is resistant to side-channel and bus-probing attacks. The accelerator splits data into random pieces to combat side-channel attacks and utilizes encryption and physically unclonable functions to prevent bus-probing attacks.
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Medium
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Skin Tumor Classification using Convolutional Neural Networks
- Researchers have developed a groundbreaking approach to classifying skin tumors using Convolutional Neural Networks (CNNs) implemented in Python.
- The approach includes a Graphical User Interface (GUI) designed with Tkinter, allowing users to select an image and receive a prediction.
- The CNN model was trained on a dataset of over 11,000 images, ensuring a robust learning experience.
- An evaluation using an additional set of 2,000 images further tested the model's reliability.
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Medium
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Deep Learning Demystified: Exploring Neural Networks
- Deep learning has captured the imagination of researchers and enthusiasts alike, promising to revolutionize problem-solving.
- Neural networks, inspired by the human brain, are at the heart of deep learning.
- Training involves adjusting connections between neurons based on errors to improve predictions.
- Neural networks have applications in image classification and natural language processing, among others.
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Medium
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Implementing a Simple Artificial Neural Network in Java for XOR Problem
- The XOR (exclusive OR) problem is a simple example used to illustrate the concept of data that is not linearly separable.
- A neural network with a hidden layer is used to solve the XOR problem.
- The XOR problem serves as a simple demonstration for the power and capabilities of neural networks.
- Neural networks built on this architecture can be extended for various practical applications.
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Medium
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Activation Functions in Neural Networks -PART 1
- Activation functions in neural networks play a crucial role in understanding important information from input and discarding irrelevant data.
- The activation function of a node defines the output of that node for a given input and introduces non-linearity transformations in the network.
- There are three types of activation functions in deep learning: linear activation function, non-linear activation functions, and rectified linear unit (ReLU).
- Linear activation function is proportional to the input, while non-linear activation functions solve the limitations of linear functions by introducing non-linearity.
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