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Neural Networks News
Medium
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How I Learned Neural Networks Part 1: The Basics with Tensor Flow Playground
- A hyperparameter tuning exercise was completed in Tensor Flow Playground, adjusting several parameters in order to find the optimal model that provided the best results for key metrics in evaluating our model.
- Activation Function, Learning Rate, Network Size, Regularization and Regularization Rate were adjusted through experimentation.
- A basic understanding of the fundamental mathematical concepts involved in neural networks was gained through a simple model on Tensor Flow Playground.
- Activation Functions are introduced to deal with non-linearity in neurons so as to make the network capable of learning more complex patterns.
- During the training process, the machine guides the weights and biases in each neuron towards an ideal solution that predicts output closely matching the dataset by implementing mechanisms like Gradient Descent and Back Propagation.
- Neural networks are powerful machine learning implementations capable of learning a wide variety of datasets.
- The next installment of the series will delve deeper in creating models with more complex datasets, focusing on computer vision with practical applications.
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Medium
1w
313
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Link Prediction in GNNs Made Easy- Deep Graph Library (DGL)
- Link prediction in Graph Neural Networks (GNNs) is made easy with Deep Graph Library (DGL). DGL is used to solve the binary classification problem of predicting the likelihood of a link between two nodes in a graph using the Cora Dataset. Four graph objects are created, representing different sets of edges, and features of the input node are passed through two GraphSAGE layers before being returned as the output feature. The apply_edges function in DGL produces new edge features based on the original node/edge features, which are then used to compute a new edge feature named ‘score’ by taking the dot product between the source node feature and destination node feature.
- The trained model is used to compute binary cross-entropy loss between predicted scores and ground truth labels for positive and negative edges. This is followed by computing the Area Under the Receiver Operating Characteristic Curve (ROC AUC) score using predicted scores and labels concatenated into NumPy arrays. This score is then used for evaluation.
- The author recommends treating the pairs of nodes as another graph, describing the pairs of nodes with an edge, and using the positive graph and the negative graph for computation. The MLPPredictor module predicts edge scores using an MLP architecture, and the DotPredictor can also be used and selected by uncommenting this line (and commenting out the MLPPredictor line).The code segment is responsible for the training loop and evaluation step.
- The article also shares a Python class named GraphSAGE, which inherits from nn.Module, representing the entire GraphSAGE model that passes features of input nodes using first and second SAGEConv layers to produce output features. The forward method is used to implement how input data flows through the model.
- The author introduces DGL, a library that simplifies deep learning on graphs with built-in data loaders and preprocessing functions for standard graph datasets. It also has built-in Graph Neural Network (GNN) modules.
- The article serves as a tutorial on how to use DGL to solve the link prediction task as a binary classification problem and evaluate the performance of the model that uses Cora Dataset and various GNN modules including GraphSAGE.
- The author suggests checking out the Udemy course, The Ultimate Graph Neural Network Course, to learn GNNs from the basics to advanced concepts.
- The article also shares some discount coupons for the author's courses on The3rdAI.
- The article provides useful insights and information on how to implement and evaluate link prediction in GNNs using DGL and Cora Dataset.
- The article also provides links and snippets of code for readers and learners to explore further.
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Medium
1w
301
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Design a Multi-Layer Perceptron (MLP) Neural Network for Classification
- A multilayer perceptron provides the nuance required to solve more complex problem and find patterns in data that are not linearly separable.
- The hidden layers in a multilayer perceptron use a non-linear activation function, such as the Sigmoid function.
- Backpropagation is not used in this case, and instead, cross entropy loss is leveraged as the objective function for optimization.
- The MLP design for classification with Python involves importing and preprocessing the data, computing each layer of the network, minimizing the objective function to find optimized weights, and generating predictions.
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Medium
1w
205
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Demystifying Neural Networks: From Theoretical Foundations to Practical Applications
- Neural networks consist of neurons that are interconnected in layers for processing and transmitting information. The input layer receives data, hidden layers perform actual processing, and the output layer provides the final decision. Neurons sum up weighted inputs, pass them through activation functions to introduce non-linearity, and learn complex patterns.
- Sigmoid is a popular activation function because it is continuous, differentiable, and outputs values in the range (0, 1). Overcoming the limitations of binary outputs, sigmoid handles probabilities and works well in instances where binary outputs are insufficient. One limitation of the sigmoid is that its output slows during negative input.
- A perceptron is a single neuron in a neural network, and it has adjustable parameters. It calculates the weighted sum of inputs and outputs using activation functions to produce an output. Multilayered perceptrons (MLP) consist of more than one layer of neurons, which enhances the network's ability to model complex relationships.
- One of the challenges with neural network training is overfitting, where the model performs well on training data but poorly on unseen data. Techniques to mitigate overfitting include Regularization and Dropout.
- To demonstrate the practical application of neural networks, the author developed a project using Fast.ai to identify broken headphones from images. The project lifecycle includes data preparation, where images load from a directory, training the pre-trained ResNet34 model, and fine-tuning it. The model is then trained and optimized.
- Neural networks have boundless opportunities in various domains, such as personalized medicine, advanced robotics, and natural language processing. Whether it is through improving consumer products, enhancing medical diagnostics, or revolutionizing data processing, neural networks are paving the way for an era where artificial intelligence is not just a helper, but a transformative force across industries.
- The journey through the realms of neural networks is highly challenging but rewarding. Data scientists and other enthusiasts who understand their fundamental workings and explore hands-on applications can leverage these models to solve complex problems and drive innovation across various domains.
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Hackernoon
1w
142
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Adaptive Graph Neural Networks for Cosmological Data Generalization: Acknowledgements and Disclosure
- This work proposes the use of adaptive graph neural networks (GNNs) to generalize cosmological analyses across different surveys and probes.
- The authors present their results on performing inference in mock data, showing that adaptive GNNs achieve competitive performance on several cosmological parameters.
- Furthermore, they present precision-recall curves, comparing different methods, and showing that the proposed method is effective in predicting rare events.
- The authors adopt a simulation-based approach for testing, using a variety of cosmological data, including dark matter simulations and surveys of galaxies and cosmic microwave background radiation.
- The authors discuss the importance of creating an equitable and inclusive research environment; they acknowledge the importance of the Deep Skies Lab, a community of experts and collaborators, which facilitated an environment of open discussion, idea generation, and collaboration.
- This manuscript has been supported by Fermi Research Alliance and the EU Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement.
- The authors assigned themselves with different roles, including methodology, software design, writing, and visualization.
- A list of 49 references related to domain adaptation, graph neural networks, and cosmology is included in the manuscript.
- This work contributes to the development of adaptive GNNs and offers new opportunities for analyzing cosmological data, particularly when probing rare events.
- The authors suggest that this work could be extended in the future to include data from additional simulation sets or other cosmological surveys.
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Medium
1w
214
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Music Genre Classification: A Machine Learning Exercise
- Classification problems that deal with a target variable with a vast number of categories is complex using machine learning.
- The music genre dataset we ran EDA has 114,000 tracks and over 100 different genres.
- Reducing similar genres to group them for this analysis process was done using the Hierarchical Clustering method to make the classification model more effective.
- After reduction, data was split into target and explanatory variable before the train-test split.
- The ensemble learning method uses multiple decision trees to achieve high accuracy in classification.
- Prediction of the model are obtained as probability distributions for each song belonging to each genre.
- The k-Nearest Neighbor model uses the genre of their 10 nearest neighbors in the training data for classification.
- Voting Classifier model combines k-Nearest Neighbors, Logistic Regression, Decision Tree, and Support Vector Machine predictions for better results.
- The results of the final ensemble model is not ideal due to poor results on individual models like logistic regression and SVC models.
- Genres with unique characteristics were easier to classify than ones that are broad with no standard sound features.
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Hackernoon
1w
398
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Adaptive Graph Neural Networks for Cosmological Data Generalization: Conclusions
- We propose and demonstrate a method for unsupervised domain adaptation (DA) for cosmological inference with Graph Neural Networks (GNNs).
- The method utilizes an MMD-based loss to enable domain-invariant encoding of features by the GNN.
- DA-GNNs reduce prediction error and improve uncertainty estimates, enhancing cross-domain robustness.
- However, cross-domain accuracy remains worse compared to single-domain performance, and more flexible approaches like adversarial-based DA techniques may yield better results.
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Medium
1w
319
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Gradient Descent and Cost Function in Neural Networks
- The cost function is used to identify the accuracy of a neural network's predictions.
- The cost is calculated as the sum of the squares of the differences between wrong and correct predictions.
- Minimizing the cost function is necessary for a better and more accurate neural network.
- The negative gradient is taken to reduce the cost function and determine the direction for adjustment.
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Hackernoon
1w
105
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Adaptive Graph Neural Networks for Cosmological Data Generalization: Results
- The paper presents the results of using Adaptive Graph Neural Networks (GNN) for generalization of cosmological data.
- The study shows that without the MMD loss, the model encodes samples in different regions of the latent space, leading to the fragility of the regressor.
- However, with the use of DA-GNN (Domain-Adaptive GNN), the samples are correctly encoded in a domain-invariant way, ensuring better prediction of cosmological parameters.
- The results are compared with previous works, with slightly better results in the same domain and slightly worse results in cross-domain tests.
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Hackernoon
1w
50
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Adaptive Graph Neural Networks for Cosmological Data Generalization: Data and Methods
- The paper titled 'Adaptive Graph Neural Networks for Cosmological Data Generalization: Data and Methods' is authored by Andrea Roncoli, Aleksandra Ciprijanovi´c´, Maggie Voetberg, Francisco Villaescusa-Navarro, and Brian Nord.
- The paper discusses domain adaptation and the use of graph neural networks for generalizing cosmological data.
- The authors performed experiments using the NVIDIA A100 40GB GPU and utilized PyTorch Geometric and Optuna library for model implementation and hyperparameter search.
- The CAMELS dataset and relevant links for further information are also provided in the paper.
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Medium
1w
252
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9 AI Tools to Build Websites and Landing Pages: Revolutionizing Web Design
- Wix ADI (Artificial Design Intelligence) is a game-changer for website building.
- Grid is an AI-powered website builder that uses machine learning to analyze design principles and create visually pleasing websites.
- Firedrop is an AI chatbot-based website builder that guides users through the entire website creation process.
- Bookmark is an AI website builder that combines artificial intelligence with human assistance.
- Adobe Sensei is an AI and machine learning platform that enhances the capabilities of Adobe’s creative tools, including website design software like Adobe XD.
- The Grid is an AI-driven website builder that uses machine learning to analyze user content and generate unique, responsive website designs.
- Elementor is a popular AI-powered plugin for WordPress that simplifies the process of building landing pages.
- Canva incorporates AI elements to make website design accessible to non-designers.
- Sketch2React is an AI tool that simplifies the process of converting design files from Sketch (a popular design software) into interactive, code-based websites.
- AI tools automate repetitive and time-consuming tasks, allowing designers and developers to focus on creativity and strategic aspects of web design.
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Medium
1w
283
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Neural Networks: A Roadmap for Beginners
- A neural network is like a robot's brain that learns from examples to recognize shapes.
- Neurons in a neural network are tiny decision-making units that help the network figure things out.
- Key components of a neural network include input, processing unit, and output.
- Different types of loss functions are used in machine learning for various problem types.
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Medium
1w
85
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Everything You Need to Know About Lllama 3 8B & 70B
- Meta AI has released the third iteration of its Llama series, Llama 3. Llama 3 is a game-changer in the world of AI, with an exceptional contextual understanding that effortlessly handles complex tasks. Refined post-training processes reduce false refusal rates, improve response alignment, and enhance the model's ability to generate more accurate responses with more relevant interactions.
- Llama 3 uses 128K tokens that effectively encode language, substantially improving its performance. The model doubles its predecessor's capacity, with an 8K context length and trained sequences of 8,192 tokens, using a mask to prevent self-attention from crossing document boundaries.
- With over 5% of the training data consisting of high-quality non-English content covering 30 languages, Llama 3 is well-prepared for multilingual use cases. The 8-billion and 70-billion parameter models of Llama 3 have demonstrated impressive performance in their scale.
- Meta's Llama 3 is now available as a highly intelligent and accessible AI assistant across Meta's ecosystem, including Facebook, Instagram, WhatsApp, Messenger, and the web. The AI assistant enables users to ask questions, learn, create, and connect with the things that matter to them more efficiently. Meta also plans to test multimodal Meta AI on their Ray-Ban Meta smart glasses.
- Meta has updated its Responsible Use Guide (RUG) to ensure responsible use and prevent misuse by bad actors as Meta AI puts GPT-4-level power into users' hands. Advanced trust and safety tools such as Llama Guard 2 are used to ensure appropriate model usage, while Code Shield and Cybersec Eval 2 address AI safety concerns, including prompt injection vulnerabilities.
- As the Llama series continues to evolve and expand, it will be fascinating to see how these models are applied and what new possibilities they unlock in the realm of artificial intelligence. The impact of these models goes beyond improving Meta's products and includes the potential to drive significant progress in crucial fields like science and healthcare.
- Meta has heavily invested in developing cutting-edge AI models that they are responsibly open-sourcing. The company seeks to provide better, safer products, accelerate innovation, and foster a healthier market by making the world's most advanced AI accessible to everyone.
- Meta continues to train even more powerful models, with over 400 billion parameters currently in training. Llama 3's high-quality dataset sets the stage for remarkable advancements in AI technology, as data plays a crucial role in model training.
- To ensure its AI technology leads to better products and advancements that could benefit humanity, Meta empowers users and developers to explore and build innovative products and experiences by releasing Meta AI as a free-to-use, ChatGPT-like assistant.
- With continuous updates and refinements to safety measures, Meta AI states it will maintain its integrity, stay ahead of potential threats and maintain user trust as their models become smarter.
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Medium
1w
38
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Fine-Tuning Precision: The Science of Neural Network Quantization
- Quantization involves the conversion of continuous numerical values, such as those found in the parameters and activations of neural networks, into discrete representations.
- Quantization helps mapping a broad range of real numbers onto a smaller set of discrete values.
- Neural networks often comprise millions to billions of parameters, making them computationally expensive to train, deploy, and execute, particularly on resource constrained devices.
- Quantizing neural network parameters, we can dramatically reduce their memory requirements and computational overhead associated with these models.
- Quantization can be classified into two main types: uniform and non-uniform. Uniform quantization involves dividing the input space into evenly spaced intervals, while non-uniform quantization allows for more flexible mappings.
- Quantization can target different levels including weights, activations, or the entire network.
- Post-Training Quantization (PTQ) quantizes the neural network after it has been trained, while Quantization-Aware Training (QAT) integrates quantization into the training process itself.
- Quantization-aware training tends to yield better results in terms of accuracy retention as it simulates the effects of quantization during training, allowing better adaption of the model to constraints.
- Quantization represents a critical advancement in the field of artificial intelligence, enabling the widespread adoption of AI in diverse real-world applications, driving innovation and progress in the field.
- Ongoing research aims to mitigate the accuracy loss associated with quantization and strike a balance between precision and efficiency.
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Medium
1w
17
Explainable AI: Why is It the Talk of the Town Right Now?
- Explainable AI (XAI) aims to shed light on the inner workings of AI models, enabling users to understand and trust the output and outcomes generated by machine learning models.
- Machine learning algorithms can become black boxes, leading to a lack of transparency and making the reliability, fairness, and credibility of AI systems questionable.
- XAI offers comprehensible justifications for the decisions AI systems make, promoting credibility, accountability, and acceptance.
- Explanations from XAI can enable us to understand and address biases, detect and mitigate errors, provide audit-ready explanations, and empower humans to optimize models effectively.
- Lack of understandability is one of the primary issues facing XAI researchers, along with difficulties in achieving balance between performance and clarity.
- Explanations must be tailored to user-specific needs, promoting a broader understanding of AI’s inner workings.
- Future XAI developments may involve ‘XAI-by-Design,’ which embeds explication techniques directly into AI model architectures, making models more inherently transparent.
- Counterfactual explanations will provide insights into the causal structure underlying the model’s decisions, allowing XAI systems to not only answer ‘what’ but also ‘why.’
- Regulations requiring explainability for certain AI applications will help drive the development of effective XAI techniques, and integrating AI with human expertise is a promising area for the future.
- Kanerika is a leading technology consulting firm specializing in AI, Machine Learning, and Generative AI, with expertise in delivering high-quality, value-driven AI solutions.
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