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Neural Networks News
Medium
1d
183
Image Credit: Medium
Deep Learning 101: Optimisation in Machine Learning
- Optimisation techniques are algorithms and methods used to adjust the parameters of a model to minimise the difference between the predictions and the actual values.
- Feature scaling involves transforming the range of input variables to ensure that they are on the same scale.
- Batch normalisation helps to stabilise the learning process and allows for the use of higher learning rates.
- Different optimisation techniques like mini-batch gradient descent, momentum, RMSProp, Adam, and learning rate decay have their own advantages and disadvantages.
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VentureBeat
3d
310
Image Credit: VentureBeat
A look under the hood of transfomers, the engine driving AI model evolution
- Transformers have become the dominant architecture for cutting-edge AI products and models.
- They are ideal for tasks such as language translation, sentence completion, and automatic speech recognition.
- The attention mechanism in transformers allows for easy parallelization and massive scale when training and performing inference.
- Multimodal transformer models have the potential to make AI more accessible and diverse in applications.
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Hackernoon
5d
400
Image Credit: Hackernoon
Here’s the Neural Network That Can Predict Your Next Online Friend
- This article focuses on training a machine learning model for link prediction using Graph Neural Networks (GNNs) on the Twitch dataset.
- They choose to use Relational Graph Convolutional Network (R-GCN) model for datasets with multiple node and edge types to handle node properties that may vary.
- Hyperparameters like learning rate, number of hidden units, number of epochs, batch size, and negative sampling are crucial and can impact the model's performance.
- The model training process involves creating specific roles for Neptune and SageMaker, setting up IAM roles, and using Neptune ML API for starting model training.
- Model training involves tuning parameters like learning rate, hidden units, epochs, batch size, negative sampling, dropout, and regularization coefficient.
- The status of the model training job can be checked using the Neptune cluster's HTTP API, and results are reviewed in the AWS console, specifically in SageMaker Training Jobs.
- The article demonstrates comparing hyperparameters used in different training jobs, showcasing how variations in parameters affect model accuracy.
- Model artifacts, training stats, and metrics are stored in the output S3 bucket, essential for creating an inference endpoint and making actual link predictions.
- The completion of model training sets the stage for generating link predictions based on the trained model's artifacts.
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Siliconangle
1w
260
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DataRobot buys Aqnostiq to advance AI agent development with dynamic compute orchestration
- DataRobot acquires Agnostiq to enhance AI agent development and dynamic compute orchestration capabilities.
- The acquisition will allow DataRobot to scale the development of AI agents and improve compute orchestration and optimization functionalities.
- DataRobot's AI development platform caters to both experts and business users, offering simple point-and-click AI model creation and customization options for advanced users.
- By integrating Covalent, Agnostiq's AI infrastructure management platform, DataRobot aims to enable agentic AI deployment and management across various compute environments.
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Medium
1w
33
Image Credit: Medium
The Math of Training Neural Networks
- Backpropagation is an algorithm used to train neural networks.
- It works by calculating the gradients of the loss function with respect to each weight in the network.
- The process involves a forward pass, backward pass, and weight update steps.
- Backpropagation enables neural networks to learn from their mistakes and improve over time.
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Medium
2w
180
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RoPE: Achieving Long-Context Understanding in LLMs
- The paper titled “RoFormer: Enhanced Transformer with Rotary Position Embedding” introduces a novel approach to positional encoding in transformer architectures through a method called Rotary Position Embedding (RoPE).
- The authors propose that the inner product of query qₘ and key kₙ be formulated by a function g, which takes only the word embeddings xₘ, xₙ, and their relative position m − n as input variables.
- They express this goal as: f(θᵤ − θₖ) ≈
- The complete math proof to arrive at this result could be done in another article.
- Rotations can be combined by adding their angles, following this rule: R(θᵢ)R(θⱼ) = R(θᵢ + θⱼ)
- This is where the relative position emerges! The matrix now represents a rotation by the difference in positions θⱼ- θᵢ, which directly encodes the relative position between tokens.
- RoFormer paper and the RoPE method proposed in it represent an advancement in transformer architecture by effectively leveraging positional information through rotary embeddings.
- This not only improves model performance but also addresses key limitations associated with traditional positional encodings, particularly in leveraging relative positions, handling long sequences and maintaining computational efficiency.
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Medium
1d
241
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Spiking Neural Networks and Generative AI
- Spiking Neural Networks (SNNs) process information using discrete spikes, similar to biological neurons, making them more energy-efficient.
- SNNs operate using spikes rather than numerical representation, reducing redundant computations and improving data processing efficiency.
- SNNs can benefit Generative AI by significantly reducing power consumption through spike-based computing.
- Hybrid models that combine the energy efficiency of SNNs with the accuracy of Artificial Neural Networks (ANNs) have the potential to create powerful and energy-efficient AI.
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Medium
4d
363
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Building the Brain of AI: Inside Neural Networks
- A perceptron is a single neuron and the simplest structural block of deep learning, processing inputs through weights and activation functions to produce output.
- The mathematical foundation of a perceptron involves combining inputs into a weighted sum and applying an activation function to make non-linear decisions.
- Multiple perceptrons can work together in a multi-output network, each focusing on different tasks with unique weights for independence and expertise.
- Adding layers to neural networks introduces hidden layers that process and transform information before reaching the final output layer.
- Hidden layers create abstractions and increase pattern recognition capacity, giving the network more learning capabilities.
- Deep neural networks evolve from simple networks to hierarchical structures processing information through multiple levels of abstraction and expertise.
- Each layer in a deep neural network transforms data in increasingly sophisticated ways, learning complex relationships in the data.
- Deep neural networks are capable of learning complex patterns like image recognition by recognizing basic elements first and progressing to more complex patterns.
- The journey from perceptrons to deep neural networks highlights the progression from simple decision-making units to sophisticated learning systems.
- Understanding how neural networks learn from data and adapt their weights and biases reveals the elegance of artificial intelligence in improving through experience.
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Medium
4d
125
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Google’s DeepMind Former Scientist Who Co-developed The AlphaFold2 Protein Prediction Platform
- The startup founder was Ex-scientist at Google’s DeepMind, where they are working on advanced Artificial Intelligent models applied to scientific problems, including protein folding and medical diagnostics.
- The goal of the startup is to use generative AI (GenAI) technologies to develop new protein-based medicines by building on the prediction capabilities used in AlphaFold2.
- The startup, called Latent Labs, aims to assist biopharmaceutical researchers in computationally producing novel therapeutic molecules, including enzymes or antibodies, with enhanced properties.
- Notable investors in the startup include Google Chief Scientist Jeff Dean, Transformer architecture co-inventor and Cohere founder Aidan Gomez, and ElevenLabs founder Mati Staniszewski.
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Brighter Side of News
4d
162
Image Credit: Brighter Side of News
Neuroscientists discover how the brain overcomes fear
- Animals rely on instinctive behaviors controlled by brainstem circuits, but can suppress these responses to adapt to new environments.
- Fear responses are hardwired but can be learned to be suppressed by complex neural circuits beyond the brainstem.
- Researchers at SWC studied how mice learned to overcome instinctive fear responses to a looming shadow.
- Higher visual areas in the cerebral cortex and the ventrolateral geniculate nucleus (vLGN) were critical in suppressing fear responses in mice.
- The vLGN, a control center for instinctive behaviors, stores learned fear suppressions and receives input from the visual cortex.
- The cortex processes threats, instructing the vLGN to suppress instinctive fear reactions once learning occurs.
- This research may have implications for treating anxiety disorders by modulating the vLGN and endocannabinoid systems.
- Increased neural activity in specific vLGN neurons triggered by endocannabinoids suppresses fear responses.
- Targeted treatments for anxiety disorders could be developed by understanding the brain circuits responsible for fear suppression.
- Further studies on these brain circuits in humans may lead to therapies helping individuals overcome excessive fear responses.
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Towards Data Science
2w
392
Image Credit: Towards Data Science
Neural Networks – Intuitively and Exhaustively Explained
- Neural networks take inspiration from the communication between neurons in the human brain, which occurs from an electrochemical signal between neurons.
- Neural networks consist of mathematically simplified neurons called perceptrons, which aggregate data and output a signal based on input.
- Data scientists use activation functions, such as ReLU, Sigmoid and Softmax to inject non-linearity in neural networks.
- Back propagation is an algorithm used to train neural networks by comparing predictions to desired outputs, then updating the model based on the difference between the output and desired answer.
- Normalization is a method used to scale inputs and outputs in a neural network to a range averaging around zero to avoid values that are too small or large.
- This detailed article explores the concepts of neural networks from theory to implementation using NumPy, a numerical computing library.
- Training a neural network involves passing training data through the model and comparing predicted outputs to known outputs, updating the model based on the difference.
- Increasing the amount of training data or adjusting the regularization parameters can help enhance predictions, but advanced approaches are required to achieve more consistent results.
- The article aims to be accessible to beginners, offering a thorough understanding of neural networks while delving into more advanced concepts.
- Future articles will continue to explore more advanced applications of neural networks, zeroing in on subjects like annealing, dropout, and gradients.
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Medium
2w
9
Image Credit: Medium
Curious About AI? Here’s the Deep Learning Story You Need to Know
- This article takes you back to the roots of deep learning through a simple problem statement, unraveling its origins.
- A quantitative analyst at a hedge fund needs to develop a trading signal based on news sentiment to classify market sentiment as bullish or bearish.
- Weights and bias are introduced to improve the accuracy of the model in classifying market sentiment.
- The article discusses the concept of a perceptron learning algorithm and the evolution of decision boundaries in capturing market patterns.
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Medium
2w
69
Image Credit: Medium
Artificial Neural Networks: Architecture, Learning, and Applications
- Artificial neural networks were introduced in the 1940s but faced limitations initially.
- In the 1980s, breakthroughs in neural network research revived interest.
- ANNs are inspired by the human brain and consist of interconnected artificial neurons.
- Neural networks have various applications and require high-quality data for optimal performance.
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Medium
2w
285
Image Credit: Medium
DeepSeek: A Deep Dive into the Mathematical Foundations and Architectural Insights
- DeepSeek is built on the bedrock of deep learning with a focus on optimizing complex functions using gradient-based methods.
- Key mathematical concepts include loss function and optimization, backpropagation, and activation functions.
- DeepSeek's architectural insights include layered structure, parallelism, and scalability.
- Practical implications include transfer learning and potential future enhancements.
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Medium
2w
238
Image Credit: Medium
Uncertainty in deep learning models
- Uncertainty in deep learning models can arise from randomness or inherent noise in the dataset.
- The uncertainty cannot be reduced with more data collection.
- There are two types of uncertainty in deep learning models - Aleatoric uncertainty and Epistemic uncertainty.
- Aleatoric uncertainty is due to randomness or noise in the dataset, while Epistemic uncertainty represents the model's lack of knowledge.
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