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Neural Networks News
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Generative AI series: Getting started with the tech for Non-AI people
- Generative AI can generate various types of data, such as audio, code, images, text, simulations, 3D objects, and videos.
- Neural Network models are the foundation of Generative AI, designed to mimic the processing power of the human brain.
- Transformer models, like GPT-3 and LaMDA, are neural networks that excel at tasks like transduction or transformation of input sequences into output sequences.
- GAN (Generative Adversarial Network) architecture comprises two models - Generator and Discriminator, which work together to generate plausible outputs based on original datasets.
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Analyticsinsight
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How to Write Neural Networks in R
- Neural networks in R are powerful tools for advanced data analysis and machine learning.
- Neural networks consist of interconnected nodes that process input data and generate predictions.
- To write neural networks in R, set up the necessary environment and libraries like keras and tensorflow.
- Follow the steps of data preparation, model architecture, compiling, training, and evaluation for building robust neural network models.
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Medium
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Unveiling the Dominance of Data Science in Today’s Market.
- Data science serves as the conduit through which organizations can extract meaningful insights from vast volumes of data, enabling informed decision-making and strategic planning.
- Data science fuels the development of cutting-edge technologies that redefine the way we interact with the world, driving innovation across diverse sectors.
- By analyzing customer preferences, behavior patterns, and feedback, businesses can create bespoke products and services that resonate with their target audience, enhancing customer experiences.
- Data science equips organizations with the tools and techniques to identify potential risks, anticipate market fluctuations, and devise proactive strategies to mitigate adverse impacts, mitigating risks and uncertainties.
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Medium
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Adaptive Learning Rate Scheduling: Optimizing Training in Deep Networks
- Adaptive learning rate scheduling techniques have gained prominence for optimizing training in deep networks.
- Learning rate controls parameter updates in the optimization algorithm.
- Adaptive scheduling methods dynamically adjust the learning rate based on feedback during training.
- Popular techniques include learning rate schedulers, adaptive optimization algorithms, cyclical learning rate policies, and learning rate range tests.
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Semiengineering
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Optimizing Event-Based Neural Network Processing For A Neuromorphic Architecture
- A new technical paper titled “Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration” was published.
- The paper discusses the challenges in reducing the overhead of event-driven processing and increasing the mapping efficiency of near/in-memory computing in neuromorphic processors.
- The authors propose spike-grouping to reduce energy and latency in event-driven processing, and the event-driven depth-first convolution to increase area efficiency and latency in convolutional neural networks (CNNs).
- The proposed optimizations result in significant improvements in energy efficiency, latency, and area efficiency compared to state-of-the-art neuromorphic processors.
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Medium
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Complex dynamics of anisotropy and intrinsic dimensions have been discovered in transformers
- Anisotropy and intrinsic dimensions have been discovered in transformers.
- Anisotropy measures the stretch and heterogeneity of the embedding space.
- Local intrinsic dimension characterizes the shape and complexity of a neighborhood.
- Experiments showed patterns in anisotropy and intrinsic dimension during training.
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Medium
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Recurrent Neural Network Alternatives: Deep Learning (Week 09)
- Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) are alternative solutions to Recurrent Neural Networks (RNN).
- GRU has a more efficient architecture for processing sequential data and requires fewer parameters to be updated.
- LSTM utilizes Back Propagation Through Time (BPTT) to update weights and improve performance.
- Bi-Directional LSTM achieved a test accuracy of 87.48%.
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Medium
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Architex — Community Owned Liquidity Program
- Architex is launching a community-owned liquidity program to enhance decentralized liquidity.
- Users can stake LP tokens in mining pools to earn rewards in real-yield and ARCX tokens.
- The program follows game theory concepts to create a positive liquidity loop.
- Architex will offer up to 100% APY in LP Mining Pools for 60 days, starting on April 1st, 2024.
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Medium
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Building Blocks for AI Part 3: Neural Networks
- Artificial Neural Networks (ANNs) mimic the structure and function of the human brain.
- Neurons in ANNs receive inputs, calculate weighted sums, add bias, apply activation functions, and produce outputs.
- Neurons in ANNs are organized into layers, and learning is achieved through backpropagation and gradient descent.
- Neural networks consist of input layer, hidden layers, and output layer, learning by adjusting weights to minimize prediction errors.
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Medium
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TensorFlow Comprehensive Training
- TensorFlow is an open-source machine learning framework developed by Google Brain Team.
- It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models efficiently.
- TensorFlow offers both high-level APIs like Keras for rapid development and low-level APIs like TensorFlow Core for advanced customization.
- It is suitable for both beginners and experienced practitioners in the field of machine learning.
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Medium
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WindSmart: Offshore Wind Turbine Maintenance with Neural Networks and Gaussian Mixture Models
- WindSmart is a machine learning solution that aims to improve offshore wind turbine maintenance.
- The global installed offshore wind capacity is projected to increase 15-fold by 2050.
- WindSmart focuses on reducing maintenance costs for offshore wind turbines through early anomaly detection.
- The solution utilizes neural networks and Gaussian mixture models to predict failures and improve precision.
- Future work includes refining data modeling and exploring supervised machine learning techniques.
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Medium
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Future-Proof Resilience : A Connected Landscape
- The risk landscape for smart devices is rapidly growing, with over 40 million devices being attacked, highlighting the need for effective security measures.
- Deep tech research is focused on addressing these threats and preventing future attacks.
- Foundation models are crucial for mitigating threats, enabling anomaly detection, threat prediction, and task automation.
- There is a need for innovative solutions that can handle security in constrained environments, such as IoT and onboard electronic systems.
- Users should advocate for the adoption of foundation models and support ongoing research and development in this field.
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Medium
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Generative AI: A Brushstroke of Creation in the Canvas of Tomorrow
- The roots of generative AI stretch back to the 1960s with the dawn of neural networks.
- Generative Adversarial Networks (GANs) revolutionized generative AI in the 2010s.
- Generative AI has diverse applications in various fields.
- Ethical considerations and responsible development are crucial for the future of generative AI.
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Towards Data Science
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Implementing Simple Neural Network Backpropagation from Scratch
- The XOR gate problem involves learning a simple pattern of relationships between inputs and outputs that a neural network can capture.
- A 2-layer fully connected neural network is used as an example to solve the XOR gate problem.
- The network structure consists of an input layer with 2 nodes, a hidden layer with 4 nodes, and an output layer with 1 node.
- The training data is represented by a matrix of size (2,4), and each example has 2 inputs.
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Medium
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How does LLM work on AI platforms
- LLMs, such as GPT, go through a pre-training process where they learn to anticipate the next word in a sequence based on context.
- The Transformer architecture, with feedforward neural networks and self-attention processes, forms the foundation of LLMs.
- LLMs can be fine-tuned on specific tasks by providing labeled data to improve their performance.
- Once trained, LLMs can generate text by sampling from the vocabulary distribution conditioned on the input text.
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