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Neural Networks News
Medium
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How I Achieved 89% Accuracy with 2-bit Neural Networks: The Ring Quantization Story
- Efforts are being made to compress neural networks for better accessibility, as current models are too large and resource-intensive.
- A new method called Ring Quantization achieves 89% accuracy with 2-bit networks, a significant improvement over previous methods.
- Ring Quantization allows for 16x compression with less than a 3% drop from full precision, offering promising results for democratizing AI.
- The researcher behind Ring Quantization highlights the potential of the method and aims to further test it on larger models for broader impact.
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Semiengineering
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Accelerator Architecture For In-Memory Computation of CNN Inferences Using Racetrack Memory
- Researchers from various institutions have published a technical paper on using racetrack memory for in-memory computing of CNN inferences in embedded systems.
- Racetrack memory is a non-volatile technology that offers high data density fabrication, making it suitable for in-memory computing, but challenges exist in integrating arithmetic circuits with memory cells.
- The paper proposes an efficient in-memory CNN accelerator designed for racetrack memory, including fundamental arithmetic circuits for multiply-and-accumulate operations and co-design strategies to enhance efficiency and performance while maintaining model accuracy.
- The work aims to address the challenges of building efficient in-memory arithmetic circuits on racetrack memory within area and energy constraints, catering to embedded systems for CNN inference.
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Medium
1w
304
5 Basics you NEED to know about Artificial Intelligence(Simplified)
- Machine learning, a subset of AI, allows systems to learn and improve by processing data, with roots tracing back to the 1940s and 50s.
- Deep learning, relying on Artificial Neural Networks, mimics the human brain structure to analyze complex patterns, commonly used in facial recognition.
- Data serves as the fuel for AI systems, crucial for training models and their performance, emphasizing the importance of quality and quantity of data.
- In AI, feeding data is akin to fueling a car, initiating processing similar to engine ignition, resulting in the system producing content after analyzing patterns iteratively.
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Medium
1w
404
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Neural Networks Decoded: How AI Mimics the Human Brain
- Neural networks are complex interconnected nodes inspired by the human brain's ability to learn and adapt, crucial for modern AI systems.
- They consist of layers of interconnected neurons where each neuron processes inputs and sends outputs, allowing the network to learn complex patterns in data.
- Neural networks learn through backpropagation, adjusting connections to optimize performance on tasks, with various types and applications across industries.
- While facing challenges, neural networks revolutionize AI, integrated into AWS services and algorithms to create sophisticated systems improving industries and lives.
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Medium
1w
191
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Neural Nets: Part 1 — Piecewise Linearity
- Neural networks excel in capturing complex non-linear patterns in data by learning flexible, layered representations that adapt to the underlying structure.
- Mathematical foundations laid by pioneers like Joseph Fourier, Taylor, and Weierstrass have contributed to understanding non-linearity, forming the basis for modern machine learning algorithms, particularly neural networks.
- Neural networks break complex patterns down by representing them as piecewise continuous or piecewise linear functions, allowing for more manageable approximations in smaller sub-domains.
- The concept of piecewise linearity can be illustrated using Rectified Linear Unit (ReLU) activation functions, showing how neural networks represent complex non-linear patterns through a sum of several ReLU functions.
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Medium
2w
108
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Let’s Talk About the Work:
- Using AI to predict problematic DNA primer sequences in Loop-mediated Isothermal Amplification (LAMP) tests.
- AI assists in identifying and redesigning primers causing false positives in DNA amplification.
- Advanced techniques like K-mer encoding and 1D CNNs optimize primer design for accuracy.
- Hyperparameter optimization and real-world testing ensure AI reliability and continuous improvement.
- The project showcases AI's potential to enhance molecular biology and diagnostic technologies.
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Medium
2w
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Reinforcement Learning: Actor-Critic Method for Virtual Highway Environment
- The actor-critic method involves actor and critic entities working together in dynamic environment learning.
- Actor takes actions, receives rewards, enters new state, while critic assesses actor's performance.
- Quantifying agent's performance involves calculating advantages and updating actor and critic policies.
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Medium
3w
157
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Can Artificial Intelligence Replace Journalists?
- AI has revolutionized several dimensions of journalism, automating news stories, analyzing big data, and personalizing content for segmented audiences.
- AI lacks the ability to make ethical judgments, provide critical interpretation, or engage audiences with empathy and nuance.
- The study advocates for a model of symbiosis between journalists and machines, where AI handles repetitive tasks, freeing humans for storytelling, investigation, and ethical decision-making.
- The risks of unregulated AI in journalism include biased data, lack of transparency, spread of misinformation, and the creation of filter bubbles.
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Medium
3w
356
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What happens if we have complex-valued neural networks? A Thought Experiment
- Exploring complex-valued neural networks reveals hidden periodic alter-egos of activation functions.
- Transitioning neural networks to complex numbers introduces challenges with activation function differentiability.
- Complex-Valued Neural Networks (CVNNs) show promise in wave-related fields but face activation function limitations.
- Traditional activation functions like tanh become singular and non-differentiable in the complex plane.
- There is a trade-off between boundedness and differentiability leading to need for specialized functions.
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Medium
3w
181
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Four Applications of Artificial Neural Networks (ANN) for Marketers
- Artificial neural networks proving useful to marketers in various ways.
- They aid in customer segmentation, market analysis, behavior analysis, and sales forecasting.
- Neural networks learn by interpreting patterns, useful in SEO, content recommendations, and ad targeting.
- They offer better customer insights and more relevant content, shaping the future of marketing.
- Marketers embracing AI technologies gain a competitive edge in the evolving landscape.
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Medium
3w
251
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Transformers in Artificial Inteligence?
- Embeddings in AI convert words or data into numerical vectors for algorithms to understand relationships and context.
- Transformers use positional encoding to maintain the order and relationships between input tokens in natural language processing.
- In a Transformer model, transformer blocks work together using self-attention to understand contextual relationships between words.
- Transformers use linear layers and the softmax function to make predictions based on learned vector representations, turning complex patterns into clear outcomes.
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Medium
3w
114
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10 Common AI Models Explained Simply: From Trees to Neural Networks
- AI models function as decision-making tools in AI systems, each with unique strengths and applications.
- Common AI models include linear regression, used for numerical predictions like house prices.
- Logistic regression is for classification tasks, such as spam detection or loan approval.
- Decision trees operate as flowcharts to make decisions based on yes/no questions.
- Random forests consist of multiple decision trees working together, each contributing to a final decision.
- Support Vector Machines draw boundaries between data categories, useful for tasks like image classification.
- K-Nearest Neighbors algorithm makes decisions based on proximity to other data points.
- Naive Bayes relies on probability and assumes independence of features to classify items like emails.
- K-Means Clustering is an unsupervised model that groups similar data points into clusters.
- Neural Networks are inspired by the human brain and are used in advanced AI applications like image recognition.
- Reinforcement learning models learn through trial and error, receiving rewards or penalties based on their actions.
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Medium
4w
403
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What the “Model” in LLM Really Means — Explained Simply
- LLM stands for Large Language Model.
- The term 'model' in LLM refers to its ability to predict the next word based on learned statistical patterns in text.
- The model is essentially a trained mathematical function, usually a neural network, that predicts likely text sequences.
- It learns patterns from massive datasets like Wikipedia, books, and articles during training.
- LLMs predict the next word based on statistical pattern recognition but do not have human-like understanding or reasoning.
- The core functionality of LLMs is to predict the next token given prior input during both training and inference.
- Emergent behaviors like summarization, translation, and reasoning are by-products of LLMs' ability to predict text in context.
- LLMs excel in detecting and generalizing patterns in language such as grammar, tone, and reasoning structures.
- The 'model' aspect of LLMs comes from learning statistical relationships between tokens through adjusting weights in neural networks.
- Despite mimicking reasoning patterns, LLMs do not comprehend text like humans; they predict based on probability.
- Text ingestion is different from learning statistical patterns, which is crucial for the model's intelligence.
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Medium
4w
408
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ResNet Paper Explained
- The degradation problem occurs in neural networks as they get deeper, causing performance to deteriorate due to challenges like vanishing gradients and overfitting.
- Deeper networks are harder to train but are important for achieving leading results, like those on the ImageNet dataset.
- A solution to deeper models involves adding identity mapping layers copied from shallower models to prevent higher training errors.
- Learning identity mapping is difficult in neural networks with many nonlinear layers due to the challenges of preserving data perfectly.
- The degradation problem is addressed by introducing a deep residual learning framework in neural networks.
- ResNet introduces a residual connection where F(x) = H(x) - x, allowing the network to naturally learn the residual component to reach the desired output.
- PyTorch implementation of the residual block includes self.block(x) as the residual function and adds the original input back to get the final output.
- The loss function is computed based on the final output, optimizing the residual function F(x) to improve network performance.
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Medium
4w
271
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Welcome to GraphX Lab: Your Visual Guide to Machine Learning and AI
- GraphX Lab focuses on teaching machine learning and AI through a visual approach, emphasizing understanding math visually and intuitively.
- The platform aims to guide learners through core mathematical concepts into practical AI topics.
- Lessons are presented in a visual, practical, and machine learning-focused manner, avoiding traditional dry textbooks.
- The learning experience takes learners from math to model, explaining each step in simple terms.
- MJ, a passionate AI educator, is leading the effort to create a visual-first learning experience, providing insights behind the models.
- GraphX Lab offers a comprehensive learning roadmap more than just a reading list.
- The platform plans to launch Udemy courses, templates, and tools for learners.
- Start with reading 'Why Linear Algebra is the Language of Machine Learning' to kick off the learning journey.
- Follow GraphX Lab to stay updated on new content and learning resources.
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