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Deep Learning News
Medium
2h
69
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The Evolution of Artificial Intelligence: From Ancient Tools to Modern Marvels
- The journey of artificial intelligence starts with the abacus but has come a long way since then.
- The Mechanical Turk, presented in 1770, was an early example of 'artificial artificial intelligence.'
- During World War II, Alan Turing’s development of the Bombe machine significantly shortened the war and saved countless lives.
- The mid-20th century saw advancements with the creation of neural networks by Marvin Minsky and others.
- The late 20th and early 21st centuries brought AI to the limelight with significant public demonstrations of its capabilities.
- Today, AI is integrated into critical applications such as healthcare diagnostics and autonomous driving.
- Recent breakthroughs in AI include generative models, improved computer vision, advancements in healthcare and more.
- AI is expected to become even more integrated into our daily lives, transforming industries and offering new solutions to complex challenges.
- The evolution of AI has been a testament to human creativity and technological progress.
- As we stand on the brink of AI becoming an integral part of our daily lives, it's exciting to think about what the next chapters of this story will hold.
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Medium
8h
194
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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
8h
303
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
8h
219
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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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Medium
9h
241
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
11h
68
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Understanding the McCulloch-Pitts MCP-Neuron
- The MCP neuron, also known as a threshold logic unit, represents the simplest workflow or behavior of a biological neuron in a mathematical way.
- The MCP neuron captures the essential features of neural computation and consists of three primary components.
- By adjusting the weights and threshold values, MCP neurons can emulate Boolean logic functions such as AND, OR, and NOT.
- While the MCP neuron provided initial insights into neural computation, it possesses limitations that restrict its capability for solving complex problems.
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Medium
12h
119
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Research on Lipschitz continuous concept in Machine Learning part1
- Researchers address stability properties of the Hamilton — Jacobi — Bellman (HJB) equation in Lipschitz continuous optimal control problems for model-free reinforcement learning.
- They bridge the gap between Lipschitz continuous optimal control problems and classical optimal control problems, exploring stability and convergence rates of value functions.
- A generalized framework for Lipschitz continuous control problems is proposed, along with a new HJB-based reinforcement learning algorithm.
- The proposed method is compared with existing approaches using benchmark examples.
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Medium
13h
210
Research on Convergence rates in Machine Learning research part 8
- We consider Lévy processes that are approximated by compound Poisson processes and, correspondingly, BSDEs driven by Lévy processes that are approximated by BSDEs driven by their compound Poisson approximations.
- The rate of convergence of the Lévy processes depends on the Blumenthal — Getoor index of the process.
- We derive the rate of convergence for the BSDEs in the L2-norm and in the Wasserstein distance, and show that, in both cases, this equals the rate of convergence of the corresponding Lévy process, and thus is optimal.
- We introduce a comprehensive framework for analyzing convergence rates for infinite dimensional linear programming problems (LPs) within the context of the moment-sum-of-squares hierarchy.
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Medium
17h
25
Research on Convergence rates in Machine Learning research part4
- We consider the Dirichlet problem for elliptic systems with periodically distributed inclusions with significant contrast compared to the background media.
- A unified method is developed to quantify convergence rates as the periodicity of inclusions tends to zero and as the parameter approaches either zero or infinity.
- Based on the obtained convergence rates and a Campanato-type scheme, regularity estimates that are uniform in both periodicity and contrast are derived.
- The spectral difference method based on p-th order Raviart - Thomas space is studied for the scalar transport equation on regular triangular meshes.
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Medium
17h
272
Research on Convergence rates in Machine Learning research part3
- Non-linear versions of log-Sobolev inequalities provide global exponential long-time convergence to free energy minimizers.
- These versions do not hold when the free energy has critical points that are not global minimizers.
- The research extends the arguments and establishes local log-Sobolev inequalities for the granular media equation in certain cases.
- The analysis shows exponential convergence to stationary solutions in both mean-field and kinetic equations.
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Medium
17h
206
Research on Convergence rates in Machine Learning research part1
- A unified framework is proposed for approximating transfer operators in machine learning research.
- The framework leverages Monte Carlo sampling to approximate the operator on a finite-dimensional space.
- Convergence of the approximating operator and its spectrum is proven under non-restrictive conditions.
- The study also establishes the convergence relationship between continuous optimization and its discrete counterpart.
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Medium
18h
236
New Research on Large Language Models part7
- Large language models (LLMs) have the capability to reproduce various rhetorical styles and generate text expressing a wide range of sentiments.
- The low-cost availability of LLMs makes them powerful tools for manipulation and control, leading to underestimated societal harms.
- The rapid and unregulated adoption of LLMs has led to the pollution and uniformization of information environments.
- Emerging research areas such as real-time design of choice architectures, computational models of human agents and populations, and reinforcement learning combined with LLMs enable the development of systems for individual, social, and political control.
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Medium
20h
103
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AI in Health Care: Lung Cancer Prediction using Deep Learning.
- The use of deep learning in detecting and predicting lung cancer can revolutionize the way the disease is diagnosed and results in improved patient outcomes. The project discussed in this article utilizes deep learning for lung detection based on chest-scanned images.
- The dataset used for the project is publicly available and contains images in PNG format. It includes three types of chest cancer - adenocarcinoma, large cell carcinoma, and squamous cell carcinoma, along with a normal cell folder. The dataset is split into training, testing, and validation sets.
- The convolutional neural network (CNN) used in the model applies filters to the images and finds features. The model was built using TensorFlow and Keras and after 30 epochs, had a training accuracy of 0.99 and a validation accuracy of 0.88. However, the model was overfitting, which could be addressed by data augmentation and adjusting parameters such as the learning rate and batch size.
- The classification report was used to evaluate the performance of the model for each class - adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal. The precision, recall, support, and F1-score of the model for each class were explained in detail. The overall accuracy of the model was 0.61, which might be due to the small dataset size and an imbalanced dataset.
- The project also included creating a Streamlit web app for testing the model with input images and displaying the predicted output. The code for creating the user interface and running the web app on a local computer is available on the author's GitHub repository.
- The use of deep learning in lung cancer prediction can greatly increase the efficiency and accuracy of diagnosis. However, larger datasets and improvements to the model are necessary to generalize the model to new and unseen data.
- The project could be extended to include other types of cancer detection and prediction, which could have a significant positive impact on healthcare.
- The author provided their email and LinkedIn for readers to reach out for more information about the project.
- Overall, the project demonstrated the potential of deep learning and CNNs in lung cancer detection and provided insights into evaluating the performance of the model and creating a user interface for testing the model.
- The use of deep learning and other AI techniques in healthcare can contribute to the development of more efficient and accurate diagnosis and treatments, leading to improved patient outcomes and a better quality of life.
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Medium
21h
226
List of popular blog topics that attract a lot of readers.
- Blogs centered around fitness, nutrition, mental health, and well-being have a broad readership.
- Blogs focusing on the latest gadgets, software, apps, and tech news are highly popular.
- Travel blogs that provide destination guides, travel tips, and personal experiences often attract a large audience.
- Fashion and beauty blogs cater to readers interested in style, trends, makeup, skincare, and fashion advice.
- Blogs that offer advice on personal finance, budgeting, investing, and money-saving strategies have a wide readership.
- Food blogs featuring recipes, cooking techniques, and culinary adventures are immensely popular.
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Medium
1d
143
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Promote Pineal Guard Now!
- Pineal Guard can now be promoted on the Digistore24 affiliate network.
- The special NASA formula contains natural ingredients to help decalcify the pineal gland and strengthen intuition.
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- It is advised to purchase Pineal Guard soon due to limited stock.
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