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Deep Learning News
Medium
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Understanding Euler-Maruyama method part5(Machine Learning future)
- The article discusses the use of Euler-Maruyama schemes for approximating the invariant measure of a stochastic differential equation.
- Two schemes with decreasing step sizes are considered, one using i.i.d. α-stable distributed random variables as innovations, and the other using i.i.d. Pareto distributed random variables.
- The convergence rate in Wasserstein-1 distance is analyzed for both schemes.
- The theorems indicate that the convergence rate for SDEs driven by an α-stable Lévy process has a similar behavior to the unadjusted Langevin algorithm with additive innovations.
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Medium
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From Smooth Chatting to Precise Execution: differences between “chat” and “instruct” modes in…
- The 'chat' mode in large language models (LLMs) is designed for engaging in conversational interactions, simulating human-like dialogue.
- The 'instruct' mode in large language models is designed for executing specific user instructions accurately and effectively.
- The choice between chat and instruct modes significantly impacts the effectiveness of the application in various fields.
- By strategically choosing the appropriate mode based on specific needs, organizations can optimize user experience and operational efficiency.
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Medium
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Understanding Euler-Maruyama method part3(Machine Learning future)
- This manuscript examines nonlinear stochastic fractional neutral integro-differential equations with weakly singular kernels.
- Existence, uniqueness, and continuous dependence on the initial value of the true solution are established.
- The Euler-Maruyama method is developed for the numerical solution of the equation and its strong convergence is proven.
- The accurate convergence rate of the method is determined under global Lipschitz conditions and linear growth conditions.
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Medium
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Understanding Euler-Maruyama method part1(Machine Learning future)
- In this paper, the stability equivalence problem for stochastic differential delay equations is investigated under G-framework.
- The authors prove the equivalence of practical exponential stability in p-th moment sense among stochastic differential delay equations driven by G-Brownian motion, the auxiliary stochastic differential equations driven by G-Brownian motion, and their corresponding Euler-Maruyama methods.
- Another paper proposes an adaptive numerical method for stochastic delay differential equations (SDDEs) with a non-global Lipschitz drift term and a non-constant delay.
- The method adapts the step size based on the growth of the drift term and addresses the estimation of the delay term to overcome numerical challenges.
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Medium
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256
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Are You Feeling Undervalued? You Deserve Better! Why Loyalty Doesn't Always Pay (Off)
- Loyalty in the workplace is often one-sided and transactional
- Job hopping can lead to higher pay raises and faster wage growth
- Loyal employees are sometimes taken advantage of by companies
- Companies don't always reward loyalty with meaningful benefits
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Medium
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Understanding Critical branching processes part15(Machine Learning 2024)
- For any branching process, the total number of events triggered over all generations exhibits a super-linear dependence at criticality.
- In branching processes with power law distributed fertilities, the exponent of the super-linear law is identical to the exponent of the fertility distribution.
- For standard branching processes without power law distribution, the total number of events follows a different scaling law.
- The mean total number of events differs from the typical behavior represented by the super-linear dependence.
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Medium
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309
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Theory to practice: a daily challenge in bridging the gap
- Bridging the gap between theory and practice is a daily challenge in teaching math.
- Students often understand the theory behind math concepts but struggle to apply them in practical situations.
- Teachers must help students concretize abstract ideas and demonstrate the practicality of math in daily life.
- Teachers need to be flexible, adaptable, and constantly learn from student responses to improve their teaching methods.
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Medium
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Self-Driving Cars: The AI Brains Behind the Wheel
- Autonomous vehicles rely on various sensors including cameras, radars, lidars, ultrasonic sensors, and IMUs to detect and monitor traffic flow, estimate distances and observe their environment.
- Artificial intelligence (AI) systems are the key to self-driving cars performing four core functions including perception, prediction, planning, and control.
- High-definition maps are used to determine accurate locations and plan routes for autonomous vehicles safely.
- Vehicle-to-everything (V2X) communication, which includes V2V, V2I and V2P communications, is crucial for enabling advanced driving automation and smart mobility.
- Safety and public acceptance are the top concern while cost is another major challenge to autonomous vehicle technology implementation.
- As autonomous vehicle companies and policymakers overcome these challenges, a safe, affordable, and trusted autonomous vehicle ecosystem will emerge, ushering in a new era of intelligent transportation.
- Self-driving technology is progressing fast. However, there are still challenges around standardization, security, and deployment at scale.
- AI can handle the complexity of driving without input from humans.
- The rapid pace of AI advancement means autonomous cars will soon hit the streets, but it may take a generational shift in mindset before fully autonomous cars become mainstream.
- Autonomous vehicles' sensors and AI must work together with V2X communication and detailed maps for precise and safe autonomous driving.
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Nvidia
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Needle-Moving AI Research Trains Surgical Robots in Simulation
- A collaboration between NVIDIA and academic researchers has developed ORBIT-Surgical, a simulation framework to train surgical robots.
- The framework supports maneuvers inspired by laparoscopic procedures and reduces surgeons' cognitive load.
- Using NVIDIA Isaac Sim and reinforcement learning algorithms, the researchers trained a digital twin in simulation to transfer skills to a physical robot.
- ORBIT-Surgical introduces benchmark tasks for surgical training and enables high-fidelity synthetic data generation for training AI models.
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Medium
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10 Game-Changing Habits to Solve of Your 90% Problems
- Reflect on three things you're grateful for each day. It shifts focus from problems to blessings.
- Truly listen to others without interrupting. It fosters better understanding and stronger relationships.
- Identify the most important tasks each day and tackle them first. It prevents feeling overwhelmed and increases productivity.
- Learn to say no when necessary. It protects your time and energy for things that truly matter.
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Medium
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173
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A Beginners Guide to PyTorch
- PyTorch is an open-source library for building neural networks.
- PyTorch is more Python-friendly and intuitive compared to TensorFlow.
- Basic operations such as tensor initialization, indexing, and arithmetic are explained.
- A basic PyTorch neural network is built, and training using DataLoader, loss function, and optimizer is covered.
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Medium
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Supercharge Your Learning: Easy Tricks to Stay Motivated and Learn Better.
- Turn Learning into a Game: Break your tasks into small challenges and reward yourself when you complete them.
- Picture Your Goals: Imagine yourself reaching your goals to stay motivated.
- Take Things One Step at a Time: Break big tasks into smaller, manageable pieces.
- Find Friends to Study With: Work together with classmates to stay motivated.
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Medium
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Exploring Deep Learning: Utilizing Convolutional Neural Networks (CNN) for Household Item…
- In this article, the author explores how Convolutional Neural Networks (CNNs) can be used to bring artificial intelligence into the realm of household item recognition.
- CNNs have revolutionized the way we approach tasks such as image recognition and classification.
- CNNs can autonomously classify various household items depicted in images, paving the way for a multitude of applications in image processing.
- The article explains the CNN architecture which is divided into 2 parts: Feature Extraction Layer and Fully-Connected Layer (MLP).
- The Feature Extraction Layer consist of Convolutional Layer and Pooling Layer. Convolutional Layer comprises multiple learnable filters or kernels, which are small-sized matrices.
- The Fully-Connected Layer consists of neurons arranged in a fully-connected manner and learns the relationships between these features and the corresponding class labels.
- The article also explains the data preparation process, data augmentation, and model training.
- Regularization and data augmentation are two effective techniques for combating overfitting in CNN models.
- By incorporating regularization and data augmentation techniques, CNN models can learn meaningful and generalizable features from the training data.
- By using CNN models to classify products from unseen images, the model can learn and make better predictions.
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Medium
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275
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Music Recommender System Using Python
- This project focuses on building a music recommender system using Python.
- The project utilizes the spotify_millsongdata dataset from Kaggle, consisting of 1 million song records.
- The data is preprocessed by removing unnecessary characters, converting text to lowercase, and using the PorterStemmer algorithm for word normalization.
- The TF-IDF approach is employed to convert the text into numerical vectors and calculate cosine similarity between songs.
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Medium
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FIRST PRINCIPLES
- The purpose of the creation of the universe.
- How questions actually function.
- The definition of consciousness and materialism.
- Exploring the relationship between spirituality and materialistic lifestyle.
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