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How does an AI work? Explained very simply
- Artificial intelligence (AI) works by using sensors to gather input data.
- AI models, such as artificial neural networks (ANN), mimic neurons in the brain.
- Weights in the ANN determine the importance of input features for prediction calculations.
- The AI learns and improves by adjusting weights through backpropagation and minimizing loss.
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Validation Strategies in Machine Learning: Critical Analysis of Cross-Validation Techniques and…
- Cross validation is one of the most critical approaches for model validation.
- Inadequate validation can lead to unreliable model performance measures.
- Different data splitting techniques have different impacts on model validation.
- Proper train-test split methods are crucial to avoid biased model evaluation.
- K-fold cross validation is a robust solution for model validation.
- Stratified sampling is critical to evaluation when dealing with imbalanced datasets.
- Time series validation requires specialized techniques to preserve temporal relationships.
- Dataset size and data type are crucial in selecting a suitable validation strategy.
- Monitoring validation metrics at different splits helps ensure model stability and generalization.
- Future research should focus on automated validation selection, domain knowledge integration, and more effective ways of handling complex data.
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AI Guru: Empowering India’s Future with Accessible AI and Machine Learning Education
- AI Guru is an AI and Machine Learning education platform in India.
- Their mission is to democratize AI education and make it accessible to all.
- They offer courses designed for the Indian learner to bridge the gap between theory and practical application.
- By empowering individuals to master AI and ML, they aim to unlock career opportunities and drive innovation in India.
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Top 5 UK Tech Trends to Transform Your Business in 2024
- AI and ML play an essential role in transforming businesses by providing predictive analytics to real-time data processing.
- Kuchoriya Techsoft creates AI-powered solutions that help businesses make precise, informed decisions and provides personalized customer insights.
- Cybersecurity and data privacy are top priorities for UK businesses, and Kuchoriya Techsoft's cybersecurity solutions are designed to safeguard customer data and ensure compliance with regulations.
- Sustainable technology is being implemented across sectors to improve efficiency while minimizing carbon footprints.
- Kuchoriya Techsoft is committed to building eco-friendly applications, energy-efficient software, and implementing IoT-powered smart systems.
- 5G networks transform how businesses operate with seamless Internet of Things (IoT) integration and real-time data processing.
- Kuchoriya Techsoft integrates 5G-powered IoT solutions that facilitate remote diagnostics and enable seamless asset tracking and inventory management.
- Remote work solutions drive efficiency and productivity in a world where physical location is no longer a constraint.
- Kuchoriya Techsoft offers remote work platforms that prioritize secure access, collaborative functionality, and high productivity.
- Adapting to the convergence of AI, cybersecurity, sustainability, 5G, and remote work technologies is a strategic imperative for businesses that want to thrive in a competitive market.
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PawSense: Transforming Animal Shelters Through the Power of Artificial Intelligence
- PawSense is a tech startup using artificial intelligence to improve animal shelters.
- They leverage machine learning, computer vision, and data analytics.
- PawSense aims to reduce euthanasia rates, boost adoptions, and make shelters more efficient.
- Their platform includes predictive analysis, adoption matching, and health monitoring systems.
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Neural Networks in Finance: Transforming the Future of Investment Management
- Neural networks have revolutionized the financial services and investment industry, providing a competitive advantage.
- Various types of neural networks are used in finance for tasks such as asset management, trading, and risk assessment.
- Current applications of neural networks in finance include predicting stock prices, detecting fraud, and enhancing accuracy and efficiency.
- Future advancements in neural networks for finance include expanding capabilities, improving portfolio management, and making investments accessible to retail investors.
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Current and Future Use of Neural Networks in Healthcare
- Neural networks, modeled after the human brain, are being used in healthcare for diagnostics, treatment, and surgery.
- Types of neural networks used in healthcare include Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, and Deep Belief Networks.
- Uses of neural networks in healthcare include improving accuracy in medical imaging and diagnostics, predictive analytics for patient outcomes, drug discovery and development, and personalized treatment planning.
- Upcoming innovations expected with neural networks include precision medicine, real-time health monitoring, enhanced robotic surgery, and mental health and cognitive therapy.
- Challenges with neural networks in healthcare include data privacy, bias and fairness, and accountability and transparency.
- Ensuring diversity in training data helps mitigate biased outcomes, fostering fairer AI-driven healthcare solutions.
- Regulatory guidelines are important to ensure that healthcare providers, developers and regulators are accountable for AI-assisted decisions.
- Neural networks are enhancing diagnostic capabilities, supporting personalized medicine, and aiding drug discovery in healthcare.
- As AI technology advances in healthcare, we can anticipate even greater potential, with neural networks playing an increasingly critical role in areas like preventive care, real-time monitoring, and mental health.
- Data privacy, bias and fairness, and accountability and transparency will continue to be key challenges for responsible and inclusive AI development in healthcare.
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Backpropagation in Neural Networks for developers
- The article talks about the difficulty of implementing a neural network from scratch using OOP and C# without using python libraries.
- Developers need to understand the math behind neural networks and linear algebra to implement them.
- Backpropagation is difficult to understand and implement, despite being explained as simple in many resources.
- The article proceeds to provide a step-by-step explanation of backpropagation in neural networks.
- It begins by explaining the calculation of the loss and how to find the derivative of the loss function, which is used to update weights and biases.
- The article then goes on to show how the chain rule can be used to calculate the derivative of the loss function for any weight in the neural network.
- It explains how to calculate the derivative for the special weight that connects the input layer to the hidden layer.
- The article also explains how to calculate the derivative for bias, using the same approach as weights.
- The article aims to help developers understand the math behind backpropagation and provide a guide to implementing a neural network from scratch.
- It also recommends taking a break if struggling with the math and encourages developers to keep coding.
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Types of Neural Networks
- Neural networks are a type of artificial intelligence that work similarly to the human brain.
- There are different types of neural networks, each with its unique characteristics and applications.
- Some types include Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Generative Adversarial Networks (GANs), Radial Basis Function Networks (RBFNs), Autoencoders, and Transformer Networks.
- Each type of neural network has specific strengths and is used for various tasks such as image classification, facial recognition, text prediction, language translation, fraud detection, data compression, and more.
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Beyond the Curve: The Magic of Sigmoid + Code
- The AND gate is a basic digital logic gate that implements logical conjunction (∧) from mathematical logic.
- The activation function in an artificial neural network (ANN) plays a critical role in determining the output of a neuron.
- Activation functions like ReLU, Sigmoid, and Tanh introduce non-linearities, enabling the network to learn complex patterns.
- Activation functions like Sigmoid and Tanh are differentiable, meaning that their derivatives can be used in backpropagation to update the network’s weights during training.
- The sigmoid function is one of the most well-known and widely used for classification tasks.
- Sigmoid function is especially valuable in binary classification tasks, where we predict between two classes (e.g., yes/no, 0/1).
- The sigmoid function as we can see is an s-shaped curve. For any value of x the sigmoid function will output a value between 0 and 1.
- This compression property makes sigmoid useful for models that require output in a [0, 1] range, especially for binary classification.
- While sigmoid can work well as an output activation in binary classification, it’s generally not recommended for hidden layers in deep networks.
- For hidden layers, the hyperbolic tangent (tanh) function is often preferred over sigmoid.
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A gental Introduction to Neural Networks
- A neural network is a machine learning algorithm that uses interconnected nodes to process data and solve complex problems.
- Neural networks are inspired by the structure of the human brain and consist of artificial neurons connected in layers.
- Neural networks excel at tasks such as finding patterns, making decisions from large amounts of data, and adapting to complex tasks.
- They have a learning process where the connections between nodes are adjusted based on the data they process.
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Are We on the Verge of True Artificial General Intelligence?
- Achieving true Artificial General Intelligence (AGI), the ability to acquire knowledge without explicit training, remains a challenge for current AI architectures.
- Current AI systems can only simulate creativity and general intelligence on a surface level, struggling to make connections between unrelated domains and lacking the ability to generalize.
- Developing AGI requires massive computational resources and conceptual breakthroughs in understanding the brain and human reasoning.
- While progress has been made, it is likely still a decade or more away from creating a minimal AGI that can function across domains like humans.
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Graph Neural Networks: The Future of Complex Data Analysis
- Graph Neural Networks, or GNNs, are a new approach to analyzing complex, highly interrelated data sets.
- Unlike conventional ML and deep learning techniques, GNNs allow for the expression of relationships and interactions within the data.
- GNNs have gained significant popularity in both AI research and industry applications as they focus on building relations and structures within data.
- Intelligence is not just about objects, but also about connections and relations, which GNNs excel at capturing.
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Exploring AI: How to Apply Artificial Intelligence in Your Business
- Every business can benefit from incorporating Artificial Intelligence (AI), whether it’s enhancing a product, streamlining a process, or improving decision-making.
- AI can be used to solve complex engineering design problems, in decision-making processes that rely on empirical data, in combinatorial optimization, and in scheduling and resource management.
- The Feed-Forward Back-Propagation Neural Network (FFBPNN) method is the most versatile, mimicking the way our brains learn and processing information in layers of “neurons” that are connected in specific ways.
- AI has the potential to unlock significant value across all industries, and is becoming more accessible to businesses of all sizes.
- AI’s applications can seem daunting, however, it's more accessible than you might think, and doesn't require significant technical investments.
- Training datasets are provided during the AI network creation process, and the network is trained how to process input and produce output.
- Thousands of iterations create a neural network model that gradually improves ability to predict correct output, converging toward the desired solution.
- AI concepts will continue to be explored in future articles, without delving too deeply into the mathematics, to enable ease of implementation for users.
- Bhairav Thakkar, Founder at Softdof seeks to connect with other professionals wishing to discuss and exchange thoughts on AI and its applications.
- Scheduling and resource management are the key areas for which AI can be applied, enabling effective optimization, reducing inefficiencies, and saving time.
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Build Neural Network from Scratch in Python
- This post covers how to build a simple neural network from scratch that can recognize handwritten digits from the MNIST dataset.
- The MNIST dataset contains 60,000 training images and 10,000 test images of handwritten digits (0–9).
- Each image is flattened into a 784-dimensional vector, which enables us to input directly into our neural network, which has 784 input neurons.
- Our neural network has three layers: an input layer (784 neurons), a hidden layer (10 neurons with ReLU activation), and an output layer (10 neurons with softmax activation).
- The learning process of a neural network involves forward propagation, activation functions, backward propagation, and gradient descent.
- ReLU is an activation function used to introduce non-linearity. It is defined as: ReLU outputs the input ZZ if it is positive, and zero otherwise.
- Softmax is an activation function applied to the output layer to interpret the model’s predictions as probabilities.
- Backward propagation calculates how much each weight and bias contributed to the error in the model’s predictions.
- After calculating the gradients, we use gradient descent to update the parameters, moving in the direction that reduces the network’s error.
- This post demonstrated how to build a simple neural network from scratch in Python to classify MNIST handwritten digits.
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