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Neural Networks News
Medium
3w
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Introduction of Blog series OpenAI Essentials: A Guide to Personal AI Applications
- This blog series aims to explore the world of Artificial Intelligence and share knowledge and insights.
- The first episode provides a brief introduction to neural networks and explains how AI and machine learning work in the background.
- It discusses OpenAI and its model offerings that can be used.
- The series covers practical examples of applying the theory in practice and guides readers to build their own personal AI assistant.
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Medium
3w
118
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ML: Fine Tuning model — Optimize & Best case
- Building machine learning models is not the end of the chapter.
- Optimizing and refining the algorithm is crucial for composing the most effective model.
- Tools like Optuna enable data scientists to carefully tailor and adjust various parameters.
- By systematically tuning hyperparameters, Optuna enhances the performance and accuracy of machine learning models.
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Medium
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421
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Demystifying Recurrent Neural Networks: A Simplified Guide with a Step-by-Step Example
- Recurrent Neural Networks (RNNs) maintain an ongoing memory as they process input sequences step by step.
- RNNs update their internal state using a formula that considers both the current input and the previous state.
- A simple example of an RNN is provided, showcasing how the hidden state evolves based on the input sequence.
- RNNs are used in various applications, but challenges like vanishing gradients can be addressed with advanced architectures like LSTM networks.
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Medium
3w
140
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ML: Neural Networks using TensorFlow
- Neural networks are machine learning models inspired by biological neural networks.
- They consist of interconnected nodes that can learn tasks by analyzing data and adjusting connection weights.
- Neural networks are applicable to domains like image recognition, natural language processing, and predictive modeling.
- They are powerful methods capable of solving complex problems with high accuracy and efficiency.
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Medium
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286
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Q&A for Quantum Machine Learning on Graphs
- Quantum algorithms can be used for quantum graph embedding or quantum message passing in hybrid classical-quantum graph processing algorithms.
- Models can use neural networks for graph embedding and processing quantum circuit results, or only use quantum embeddings for downstream tasks.
- Models with specific structures need to be designed to address trainability issues.
- Benchmarking quantum machine learning models on graphs is challenging, and more research is needed.
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Medium
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277
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~Exploring the Potential Existence of Non-Neural Consciousness~
- This paper explores the potential existence of consciousness beyond traditional neural frameworks.
- Dr. Evelyn Moore's research with cellular slime mold and microtubule structures challenges conventional paradigms.
- Experimental observations reveal complex network connectivity and rudimentary memory in non-neural structures.
- The research suggests the role of quantum phenomena in biological processes and invites interdisciplinary collaboration.
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Medium
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198
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The Intricacies of Neural Networks: From Perceptrons to Deep Learning
- Perceptrons, the pioneers of neural networks, emerged in the late 1950s, enabling pattern recognition and artificial intelligence.
- Perceptrons can learn linear decision boundaries but require multilayer networks to handle complex patterns.
- The perceptron controversy in the 1960s questioned the capabilities of perceptrons, but neural networks continued to evolve.
- Deep learning and neural network architectures, such as CNNs, revolutionized AI, especially in computer vision tasks.
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Medium
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273
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Harnessing the Potential of Artificial Neural Networks: From Theory to Real-World Applications
- Artificial neural networks are computational models inspired by the structure and function of the human brain.
- Success of artificial neural networks lies in their ability to learn from data through a process called training.
- Deep learning refers to the training of neural networks with multiple hidden layers.
- Transfer learning leverages pre-trained neural network models on large datasets to solve new tasks with limited labeled data.
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Medium
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402
Deep Dive into PyTorch: The Versatile Tool for Deep Learning
- PyTorch is an open-source machine learning library known for its ease of use, flexibility, and dynamic computational graphing capabilities.
- It supports imperative and interactive computing models, making it intuitive for researchers and developers.
- PyTorch offers tensor computations with GPU acceleration and seamless CUDA integration for faster training processes.
- It competes with TensorFlow and is praised for its intuitive syntax and supportive community.
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Medium
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92
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Story of LSTM for complex thermo-fluid-mechanical process
- LSTM is a successful type of RNN that can remember long useful history.
- For complex thermo-fluid-mechanical processes, a robust LSTM model can be created using training data with unit step response and transient behavior.
- By plugging controllers (such as PID) into the LSTM model, optimization of controllers can be done in a simulated environment.
- Once optimized, these values can be used in real controllers, resulting in efficient behavior and waste avoidance.
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Medium
3w
420
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Two Non-Negotiables for Ethical Decision-Making in AI Integration
- AI integration in business operations is leading to the establishment of ethics departments.
- Governments struggle to keep pace with technological advancements, necessitating internal ethics oversight in businesses.
- Two universal principles for ethical AI integration are continuous innovation and preserving human agency.
- Businesses have a responsibility to stakeholders and society to ensure ethical AI integration for positive impact.
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Medium
3w
362
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Explainable AI and its importance
- Explainable AI (XAI) refers to methods and techniques that make AI models transparent, interpretable, and understandable to users.
- Importance of XAI: Transparency, Accountability, User Trust, Ethical Considerations.
- Methods to Achieve Explainability: Rule-based Explanation, Local Explanations, Feature Importance, Model Simplification.
- Applications of XAI: Healthcare, Finance, Autonomous Vehicles, Human Resources.
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Medium
4w
345
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Build End-to-End RAG Pipeline with Monitoring and Evaluation using Langchain, Azure AI Search…
- NeMo-Guardrails is an open-source toolkit for adding guardrails to LLM-based conversational systems
- Ragas is a framework to evaluate Retrieval Augmented Generation (RAG) pipelines
- Azure OpenAI Search provides access to models like GPT-4 and GPT-3.5-turbo
- The article discusses the design and components of an end-to-end RAG pipeline
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Medium
4w
337
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Demystifying Neural Network Learning: Supervised, Unsupervised & Reinforcement
- Neural networks employ three primary learning paradigms: supervised learning, unsupervised learning, and reinforcement learning.
- Supervised learning involves training the network on a labeled dataset, where the correct outputs are known.
- The network makes predictions and adjusts its parameters using techniques like back-propagation to minimize errors.
- Through repeated iterations, the network learns to make accurate predictions based on the labeled data.
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Analyticsinsight
4w
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Why is Reading Deep-Learning Books Essential?
- Reading deep learning books is essential for unlocking the power of deep learning and understanding its fundamentals, structure, and mathematical concepts.
- It provides in-depth knowledge of various deep learning algorithms such as CNNs, RNNs, and GANs, along with insights into their workings and limitations.
- Books often contain practical examples, code snippets, and exercises to enhance hands-on skills in applying deep learning to solve real-world tasks.
- By staying updated with the latest developments and best practices, reading deep learning books can contribute to career development in fields like data science, machine learning, and artificial intelligence.
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