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ML News
Medium
3h
121
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Turning $100M Worth of Bitcoin, USDT, Ethereum and Other Cryptocurrencies Token Into $1B Using…
- A crypto strategist has revealed a plan to turn $100m worth of cryptocurrencies into $1bn through Bitcoin grid bots reducing liquidation risks.
- With an initial capital investment of $100m in February 2024 the final return will depend on the needs of the owner of the capital.
- Hitable Cryptocurrencies Investment and Bitcoin OTC desk offers investment management and OTC sales.
- Current returns show $5m in USDT with 78.20BTC and 7ETH.
- Bitcoin has undergone negative ROI, BTC went down 7.5%, since the last update on March 21st.
- The strategy uses grid bots to reduce liquidation risks and the tracker plan is updated regularly.
- The market is seeing billions in liquidations, and while the strategy has suffered it has done less so than most others, with reduced risk to conventional leverage trading practices.
- The power of compounding is significant but emotional discipline is required for continued success.
- The crypto bull market is far from over, yet cycles vary and adjustments will be required to adapt along the way.
- Regular monthly updates on the progress of the grid bot plan can be found on the owner's Medium compte or Twitter feed.
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Medium
1h
76
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Predicting Depression: Analyzing Demographic and Lifestyle Factors using Logistic Regression
- We install and load the NHANES package to access the dataset.
- Subset the dataset to include individuals aged 30 and above.
- Create a binary outcome variable, Depressed_Yes, based on the Depressed variable.
- Fit a logistic regression model to predict depression using selected predictors.
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Amazon
1h
283
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How Dialog Axiata used Amazon SageMaker to scale ML models in production with AI Factory and reduced customer churn within 3 months
- Dialog Axiata implemented a churn prediction model to reduce customer churn based on demographic and network usage data of customers using Amazon SageMaker
- The model was trained using nearly 100 features across ten distinct areas to predict churn 45 days in advance
- The model uses two essential models - a CatBoost model and an ensemble model trained to capture additional insights and patterns
- Both the training and inference pipelines are run three times per month to maintain accurate predictions and reduce customer churn rates
- Features are sourced from Amazon SageMaker Feature Store, to accurately identify customers at a higher risk of churning
- The churn prediction solution implemented by Dialog Axiata resulted in a substantial reduction in month-over-month gross churn rates within a span of 5 months
- The AI Factory framework and SageMaker were instrumental in designing and deploying the end-to-end AI/ML system life cycle in the AWS cloud environment
- Dialog Axiata's MLOps process involves using SageMaker as their core ML platform to perform feature engineering, and train and deploy models in production
- Dialog Axiata witnessed remarkable business outcomes, showcasing the power of innovative solutions and the seamless integration of AI technologies
- The churn prediction model developed by Dialog Axiata underscores the crucial role of predictive analytics in staying ahead in the competitive telecom industry
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Medium
1h
200
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Exploring Wage Determinants with Regression Model
- A linear regression model, model.1a, is constructed to predict wages based on age, gender, education level, marital status, and geographical indicators.
- The model is evaluated using the summary() function, providing statistical information and diagnostic measures.
- The top predictors identified from the analysis are age, female, education level, marital status, metropolitan residence, South, and Midwest.
- Model performance is assessed using the predict() function and calculating the Root Mean Squared Error (RMSE) to measure the deviation between observed and predicted wage values.
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Medium
3h
183
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Machine Learning
- Machine Learning (ML) is a part of Artificial Intelligence (AI) that involves creating algorithms to learn and adapt from data without being explicitly programmed.
- Unlike traditional programming that follows a set of logical rules and algorithms defined by the developer, ML algorithms operate through a training process where they identify patterns and adjust parameters to minimize the error between predicted and actual results in training data.
- ML has various applications, from pattern recognition in images and texts to financial forecasting and medical diagnoses.
- Machine Learning is everywhere, shaping interactions in our daily life like YouTube recommendations, social media newsfeed, etc.
- Supervised learning is used in problems where labeled data examples are provided and the model learns the relationship between inputs and outputs, while Unsupervised learning is used to identify patterns in the data without the guidance of known outputs.
- Reinforcement learning involves learning from interactions with a dynamic environment and the focus is to learn a policy that maximizes the accumulated reward over time.
- Privacy, data security, and model interpretability are among the main ethical concerns with Machine Learning models that must be addressed responsibly during the process of development and application.
- A thorough process of data collection, preprocessing, model selection, training, evaluation, and validation is a crucial step for ensuring the effectiveness of ML models in solving real-world problems.
- Various libraries and models in Python such as Linear Regression, K-Means Clustering, and Decision Tree are commonly used to demonstrate ML techniques and concepts.
- Identifying and mitigating bias in data and algorithms is crucial to ensure fair and equitable results while promoting trust and acceptance of these technologies.
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Medium
3h
209
Best Research on Facial Expression Recognition(FER) part4(Computer Vision + Artificial…
- Dynamic Facial Expression Recognition (DFER) has received significant interest for enabling empathic and human-compatible technologies.
- Multimodal emotion recognition based on audio and video data is being explored to improve DFER models' robustness towards real-world applications.
- Advancements in self-supervised learning (SSL) and adapting pre-trained static models are being utilized in multimodal DFER.
- This research work proposes adapting SSL-pre-trained disjoint unimodal encoders to improve multimodal DFER performance and achieves state-of-the-art results on DFER benchmarks.
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Medium
3h
286
TF-IDF Vectorization
- Term Frequency (TF) indicates how often a word appears in a document relative to the total number of words in that document. Example: If a document containing 100 words mentions “apple” 5 times, then the TF for “apple” in that document is 5/100 = 0.05.
- Inverse Document Frequency (IDF) gauges how significant a word is across all the documents in your collection. Example: If “apple” appears in 100 out of 1,000 documents, then the IDF for “apple” is log(1000/100) = log(10) = 1.
- TF-IDF is the product of TF and IDF, indicating how important a word is in a document compared to its importance across all documents. Example: If the TF for “apple” in a document is 0.05 and the IDF for “apple” is 1, then the TF-IDF score for “apple” in that document is 0.05 * 1 = 0.05.
- TF-IDF vectorization converts each document into a set of numbers, where each number reflects the significance of a word in that document. Example: Let’s say we have three documents: 1. Document 1: Discusses apples and bananas. 2. Document 2: Talks about apples and oranges. 3. Document 3: Focuses on bananas and oranges. Assuming our vocabulary includes only “apple”, “banana”, and “orange”, the TF-IDF vectors for these documents might look like this: — Document 1: [0.05, 0.05, 0] (since it mainly covers apples and bananas) — Document 2: [0.05, 0, 0.05] (as it mainly discusses apples and oranges) — Document 3: [0, 0.05, 0.05] (since it mainly addresses bananas and oranges).
- TF-IDF vectorization aids in understanding word significance in documents, whether for categorization, retrieving information, or pinpointing essential keywords.
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Medium
3h
207
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The Future of AI: The Transformer and How We Got There
- AI is being integrated into every sector possible, powered by the chatbot- Transformer, which has been around since 2017, and broke the field at the time. The biggest implication of the technology has to do with the field of Natural Language Processing(NLP).
- Recurrent Neural Networks (RNNs) introduced in 1985, is a type of neural network that processes sequential data by storing information across time-steps.
- Long-Short Term Memory networks (LSTM) is a type of RNN that was specifically made to solve the vanishing gradient problem. LSTMs are computationally more intensive and sequential in nature which can hinder their performance.
- The transformer architecture introduced a mechanism known as self-attention that relates different positions of a sequence to compute a representation of that sequence.
- The transformer architecture is the first of its kind to utilize parallelization when analyzing sequential data, allowing it to process huge corpora of text quickly and efficiently.
- Transformers have limitations and complexities such as expense and concerns over excessive carbon footprints.
- By being able to “see” all the data at once, the Transformer architecture allows human-computer interaction in a way never seen before.
- This groundbreaking discovery has accelerated the machine learning field, transforming the field of Natural Language Processing and allowing for human-computer interaction in a way never seen before.
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Marktechpost
3h
221
MaRDIFlow: Automating Metadata Abstraction for Enhanced Reproducibility in Computational Workflows
- The integration of data-intensive computational studies is vital across scientific disciplines.
- Researchers from the Max Planck Institute for Dynamics of Complex Technical Systems introduce MaRDIFlow, a robust computational framework aiming to automate metadata abstraction within an ontology of mathematical objects.
- MaRDIFlow's design principle revolves around treating components as abstract objects defined by their input-output behavior and metadata.
- The current version of MaRDIFlow serves as a command-line tool, allowing users to manage workflow components as abstract objects based on input-output behavior.
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Medium
3h
57
Insurance Prediction Machine Learning
- The project aimed to disrupt the insurance industry by integrating machine learning models to enhance the accuracy of insurance predictions.
- Using Python, a machine learning model was built to predict insurance costs based on user demographics and health information.
- Challenges included dealing with incomplete and imbalanced datasets, which were addressed through data imputation techniques and balancing methods.
- The model achieved impressive accuracy, surpassing traditional statistical approaches and opening possibilities for further refinement.
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Medium
3h
118
Best Research on Facial Expression Recognition(FER) part3(Computer Vision + Artificial…
- Researchers propose emotion-centered generative replay (ECgr) method for facial expression recognition.
- ECgr integrates synthetic images from generative adversarial networks and a quality assurance algorithm.
- The method enables CNNs to retain past knowledge while learning new tasks, improving performance.
- Another study focuses on multi-task multi-modal self-supervised learning for facial expression recognition.
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Medium
3h
192
Best Research on Facial Expression Recognition(FER) part2(Computer Vision + Artificial…
- Two research papers on Facial Expression Recognition (FER) have been presented.
- The first paper introduces the Ada-DF framework, which addresses FER through label distribution learning paradigm.
- The second paper proposes the LEAF framework, which focuses on semi-supervised FER by coordinating expression-relevant representations and pseudo-labels.
- Both frameworks outperform existing methods in their respective areas and offer potential advancements in FER.
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Medium
3h
229
Best Research on Facial Expression Recognition(FER) part1(Computer Vision + Artificial…
- This research paper focuses on the Open-set Video-based Facial Expression Recognition (OV-FER) task.
- The goal is to identify both known and unknown human facial expressions not encountered during training.
- The proposed approach, Human Expression-Sensitive Prompting (HESP), enhances CLIP's ability to effectively model video-based facial expression details.
- Experimental results show that HESP significantly improves CLIP's performance and outperforms other state-of-the-art methods.
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Medium
4h
199
Enhancing Business Engagement: Advanced AI and LLM for Detoxifying and Moderating Hate Speech in…
- Online platforms face significant challenges posed by toxic comments which can seriously undermine user experience.
- Recognizing the critical nature of this issue, AI-driven content moderation is crucial.
- The FLAN-T5 model is a crucial component in boosting AI-driven content moderation, thanks to its ability for few-shot learning.
- FLAN-T5’s design makes it highly versatile, and capable of handling different languages and dialects.
- 1. Select and organize the data for training language models.
- 2. Load previously fine-tuned PEFT model for efficient deployment in real-world scenarios.
- 3. Integrate additional configurations, adapter, for model fine-tuning on specific tasks.
- 4. Establish a baseline for Proximal Policy Optimization (PPO) training by creating a frozen copy of the PPO model.
- 5. Fine-tune AI-driven content with the reinforcement learning framework, using toxicity classifier.
- The AI model developed detoxifies online content, fostering healthier and more engaging digital communities.
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Medium
4h
193
Image Credit: Medium
Can You Rely on GPT-3 in AI Development Projects?
- The GPT-3 language model family includes several models with different properties and applications.
- Ada is suitable for machines with limited resources and simple NLP tasks.
- Babbage is balanced in terms of complexity and resource consumption, suitable for products that don't require extremely high accuracy.
- Curie is a resource-demanding model that handles complex language tasks with high accuracy.
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