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Neural Networks News
Medium
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The Rise of AI in Programming: What Developers Need to Know
- AI-driven tools are making debugging and testing processes more efficient.
- AI is enabling the creation of adaptive and efficient algorithms in various fields.
- Developers should grasp the basics of machine learning to gain an edge.
- The rise of AI in programming calls for developers to embrace lifelong learning and treat AI as an assistant.
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Cinema5D
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AI Tools for Filmmakers – Recap and Trends of 2024
- In 2024 new and emerging AI video generators for creating photorealistic moving images are readily available along with an extensive array of new AI-enhanced post-production capabilities and AI-based production applications.
- AI-powered movie production software has vastly improved as it now handles everything from storyboarding and visualizing your concept, to automated shoot prep.
- Throughout 2024, big tech names joined the AI race concerning video generators, improving the technology involved with their video generators to experience photorealistic moving images.
- Artificial intelligence advancements toward multi-modal capabilities in the field of film production, combining speed and efficiency, are anticipated this year.
- Amazon has launched a variety of generation models that utilize a text-to-video generator, viable for both charts and documents.
- The rise of AI-based movie-making is backed up by PRODUCER, an AI-based application targeting the boring, repetitive tasks of filmmaking to help during the creative process.
- The film industry's adoption of AI-enhanced features and tools has been viewed with scepticism, but there are positives if used solely for mundane repetitive tasks, providing more time for the creative process.
- Increased investment in AI by tech companies is causing negative outcomes like AI intrusion on other artists' property.
- As AI-based moviemaking speeds up and becomes more photorealistic, it makes it tough to distinguish which content is real from that generated by AI.
- There are a few problematic scenarios arising from the rapid advancement of AI-based movie-making that require industry-wide regulation.
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Medium
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370
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Neural Networks in Focus
- Neural networks use artificial neurons, or 'nodes,' to process information and make decisions.
- They are organized into layers and each connection between neurons has a weight.
- Neural networks learn from data and can make predictions on new, unseen data.
- They are already being used in various applications and continue to improve with more data.
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Medium
7d
262
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Solving Ordinary and Partial Differential Equations with Neural Networks(PINNs)
- An Ordinary Differential Equation (ODE) is a fundamental mathematical equation that relates a function to its derivatives.
- Partial Differential Equations (PDEs) have been critical in modeling physical phenomena in fields like thermodynamics, electrodynamics, wave dynamics, heat transfer, and many others.
- Computational methods aim to simulate intricate problems using PDEs and resolve them through numerical computation, developing accurate and efficient techniques to approximate solutions.
- Deep learning models can solve both ODEs and PDEs, converting them into the optimization problem by considering an independent function as an input to the neural network and the output as the dependent function variable of the given differential equation.
- The Physics-Informed Neural Network (PINN) method can be used for non-linear PDEs, such as Burgers' Equation, and provides data-driven solutions.
- In PINN, the problem is treated as a physical constraint problem with respect to neural networks from the physical world, where the loss function is determined based on backpropagation or forward propagation for minimizing the approximation functions.
- The Universal Approximation Theorem is a fundamental result in the field of ANN that states that certain types of neural networks can approximate specific functions to any desired degree of accuracy, enabling the network to learn complex patterns and relationships in data.
- The loss function quantifies how well or poorly a model is performing by calculating the difference between predicted and actual values, and guides the optimization process to enhance model accuracy.
- The backpropagation is a machine learning algorithm that trains neural networks by correcting errors, which is used to find the derivative of the function with respect to the input.
- The method of solving ODE and PDE using neural networks is a clever approach for simulating complex problems in the real world, which can not be addressed using traditional experimental or theoretical methods.
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Arstechnica
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AI helps ID paint chemistry of Berlin Wall murals
- A team of Italian scientists used spectroscopic analysis and machine learning to study paint chips from Berlin Wall fragments.
- Preservation of street art is challenging due to degradation and vandalism.
- Italian chemists developed a method using hydrogels to remove graffiti from vandalized murals in Florence.
- The lack of documentation and complex composition of modern painting materials pose challenges for conservators.
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Medium
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336
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What is GenAI?
- Generative AI is a subset of artificial intelligence that can produce original content like text, audio, images, video, or software code as a response to a user’s request or prompt.
- To create a foundation model that can support multiple gen AI applications, generative AI involves three phases - training, tuning, and retuning.
- Various methods can be used for tuning the generative AI like fine-tuning or reinforcement learning with human feedback (RLHF).
- Generative AI models rely on various large pre-trained machine learning models like foundation models (FMs) and large language models (LLMs) that are trained to develop deep patterns and relationships in data.
- Generative AI produces various types of content like text, images, video, audio, software code, and design and art.
- Generative AI offers several benefits like dynamic personalization, improved decision-making, constant availability, etc.
- Generative AI also has some limitations like security concerns, cost, limited creativity, etc.
- It possesses a black box problem, and enhancing interpretability and transparency is necessary to gain trust and adoption.
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Medium
2d
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Breaking Down Complex ML Models: Explaining LSTM, CNN, and Transformers for Beginners
- LSTM is a memory system for machines, helping them remember important information over time. It uses special 'gates' to decide what information to keep, update, or forget.
- CNNs analyze images by breaking them down into smaller pieces and looking for patterns. They use layers of filters to detect features and recognize objects.
- Transformers can understand entire documents at once, rather than reading word by word. They use attention mechanism to focus on important parts of the input.
- These models are used in various applications like recommendation systems and virtual assistants, making life easier and more connected.
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Medium
2d
27
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How Attention in Neural Networks Works (and Why It’s Amazing)
- Attention mechanisms in AI help machines determine what's important and make smarter decisions.
- Attention allows AI to focus on specific parts of input, like highlighting important words in a sentence or key details in an image.
- Attention scores are assigned to different parts, with higher scores indicating more focus.
- Attention helps AI handle complex inputs and improve accuracy in tasks like translation or understanding sentences.
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Medium
5d
110
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Stay Ahead of the Curve: Sales Predictions Made Simple
- NProcessors is a Windows-based desktop software that allows you to leverage an Artificial Intelligence solution for tasks like sales forecasting without requiring any prior experience with AI or programming.
- Using NProcessors SoothSayer, you can effortlessly create, train, and execute AI models to get precise predictions and make confident, data-driven decisions.
- NProcessors SoothSayer helps to simplify complex forecasting, making it easier to predict sales; instead of writing complex lines of code.
- When using NProcessors SoothSayer, focus on the top three performing methods rather than chasing perfect scores. Combining or averaging the results of multiple well-performing models can often provide more robust and reliable forecasts for your business.
- The Composite Score is a single value that consolidates the results of multiple evaluation metrics, offering an easy-to-compare ranking of forecasting models.
- Every forecasting method is automatically evaluated, and its Composite Score is calculated based on its performance across various metrics such as R-Squared, MAE, MSE, and RMSE.
- The Composite Score provided by NProcessors SoothSayer guides businesses towards informed decisions and saves time by delivering the insights you need.
- NProcessors SoothSayer is easy to use and ensures that the score reflects a holistic view of model quality, helping you identify models that not only fit the data well but also generalize effectively for future predictions.
- With NProcessors SoothSayer, You can explore the top-performing models, study their individual metric values, and even switch off under-performing methods to refine your analysis.
- Don’t wait — your business deserves the clarity and confidence that only NProcessors can deliver.
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Medium
7d
278
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What is Generative AI | Introduction to Generative AI | Generative AI Explained |
- Generative AI is a type of artificial intelligence that creates new content based on patterns and data.
- It operates using advanced machine learning techniques such as deep learning and neural networks.
- Applications of generative AI include content creation, design and art, healthcare, gaming, and education.
- Generative AI is a game-changer due to automation of creativity, scalability, and accessibility.
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Medium
1w
209
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Meta Just Released:Byte Latent Transformer: Eliminating Tokenization with Raw Byte Learning
- BLT is an AI architecture that works directly with raw bytes.
- It dynamically groups bytes into patches based on complexity.
- BLT offers groundbreaking benefits and practical applications.
- Scaling experiments show BLT's performance in compute-heavy settings.
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Medium
1w
89
The Transformative Impact of Artificial Intelligence in Medicine
- The application of AI in diagnostic medicine has revolutionized the analysis of medical imaging modalities, leading to early detection and intervention strategies.
- AI algorithms enable personalized medicine by leveraging genomic, phenotypic, and environmental data to develop tailored therapeutic interventions.
- AI has accelerated drug discovery and development by predicting protein-ligand interactions, identifying viable drug candidates, and simulating pharmacokinetic properties.
- AI technologies have enhanced remote monitoring and virtual care through wearable biosensors, real-time surveillance, and virtual health assistants.
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Medium
1w
115
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Simplify Your Training Workflow with train_time: A Tensorflow Callback for Tracking Training Time
- train_time is a TensorFlow callback designed to track training time and provide real-time estimates of how long each epoch will take and the total training time remaining.
- It aims to simplify the training workflow by offering a lightweight tool that focuses on solving a specific problem efficiently.
- You can easily integrate train_time into your Keras training pipeline and benefit from its real-time training time estimates.
- train_time can be especially useful for students, researchers, and engineers looking to streamline their machine learning workflows.
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Medium
2w
258
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Why Machines Learn: A Book Review and Summary
- Anil Ananthaswamy combining his background in electronics and computer engineering with his talent for making complex ideas accessible, wrote Why Machines Learn to explore how machine learning has evolved.
- It covers the mathematical and computational underpinnings of AI and its societal implications.
- The book begins with the foundational concept of machine learning: identifying and learning from patterns in data.
- Ananthaswamy introduces the concept of the perceptron—the first artificial neural network.
- The book illustrates how Bayes’s Theorem provides a systematic way to incorporate new data into existing models, laying the groundwork for probabilistic reasoning in machine learning.
- It introduces Voronoi diagrams, a method for dividing space into regions based on proximity to a set of points, and connects them to the workings of the k-nearest neighbor (k-NN) algorithm.
- This chapter introduces dimensionality reduction, a technique used to simplify datasets by reducing the number of features.
- This chapter explores innovations that emerged well after my college years, introducing me to powerful tools like kernel methods and support vector machines (SVMs).
- The chapter also explores the concept of receptive fields, the portions of an image that each neuron in a CNN is sensitive to.
- The epilogue dedicates to examining the capabilities and limitations of large language models (LLMs).
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Medium
2w
213
Day 16: Building Blocks of Neural Networks: Perceptrons and Multi-Layer Networks
- A perceptron is a decision-making unit for computers that takes inputs, weighs them, and outputs either a 1 or 0.
- Perceptrons can only solve simple problems and are not effective in classifying complex images.
- Multi-layer networks are teams of perceptrons working together and can solve more complex problems.
- Multi-layer networks consist of multiple layers, with each layer passing data to the next layer until a final decision is made.
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