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Neural Networks News
Medium
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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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Medium
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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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Medium
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Exploring the Differences: AI, Machine Learning, Deep Learning, and Neural Networks
- AI is the broad field that focuses on creating intelligent machines that can perform tasks that require human-like intelligence.
- Machine learning (ML) is a technique that allows computers to learn from data without being explicitly programmed for each task.
- Deep learning (DL) is a specialized type of machine learning that uses complex structures called neural networks.
- Neural networks are specific models designed to mimic how our brains work.
- Deep learning models excel at complex tasks such as speech recognition and image classification.
- Deep learning has revolutionized the way we analyze large data sets, opening up new possibilities in a variety of fields.
- The more data a deep learning model is given the more accurate it becomes at recognizing patterns and making predictions.
- Traditional neural networks may struggle to handle large data sets, however, deep learning models excel in processing large amounts of data efficiently.
- Training a deep learning model requires a lot of data and resources, while traditional neural networks can be trained faster with smaller datasets.
- AI is the big picture of intelligent machines, while neural networks and deep learning are the smart tools that focus on interpreting and processing data.
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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Medium
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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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Hackernoon
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Predicting Links in Graphs with Graph Neural Networks and DGL.ai
- Link prediction is one of the fundamental tasks in graph analytics, involving the prediction of connections (or links) between nodes using Graph Neural Networks (GNNs). Constructing GNNs is made easier with Deep Graph Library (DGL.ai).
- We learn how to set up a project, preprocess data, build a model, and evaluate it for link prediction on the Twitch Social Network dataset from the Stanford Network Analysis Project (SNAP).
- GraphSAGE is specifically designed for GNNs to obtain node embeddings that capture both the structure and features of each node within the graph. Using GraphSAGE, we set up a three-convolutional-layer model with dropout enabled after each node feature update and a subsequent MLP predictor that outputs a probability.
- To reduce overfitting, we use binary cross-entropy with logits as the loss function and AUC as the metric to evaluate the model.
- We generate predictions for all possible pairs of nodes, allowing us to identify potential new connections and their probabilities.
- By using a relatively small dataset and DGL.ai, we show an effective way to build a link prediction model for graphs. As graphs scale up to millions or billions of nodes and edges, handling them requires more advanced solutions.
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