Forbs announces the launch of the Miss AI contest🤖
AI-powered influencers can now compete in the world's first AI-powered beauty pageant.
Judges will evaluate contestants on their appearance, technical skills and online presence.
The winner will receive a cash prize of $5,000, promotion on the platform and PR support of another $5,000.💰
Virtual winners will be announced on May 10, with an online awards ceremony to take place later this month.
The commission includes entrepreneurs and a beauty pageant historian.
Contestants must be 100% created using artificial intelligence, no restrictions on the tools used. 🤔
AI-powered influencers can now compete in the world's first AI-powered beauty pageant.
Judges will evaluate contestants on their appearance, technical skills and online presence.
The winner will receive a cash prize of $5,000, promotion on the platform and PR support of another $5,000.💰
Virtual winners will be announced on May 10, with an online awards ceremony to take place later this month.
The commission includes entrepreneurs and a beauty pageant historian.
Contestants must be 100% created using artificial intelligence, no restrictions on the tools used. 🤔
🔰 27 NOTORIOUS ALTERNATES OF CHATGPT - CAN BEAT MOST OF IT'S FEATURES 🔰
1. https://chat.tgbot.co
2. https://chat.theb.ai
3. https://freechatgpt.chat
4. https://fastgpt.app
5. https://freegpt.cc
6. https://freegpt.one
7. https://chatgpt.ddiu.me
8. https://chat.ninvfeng.xyz
9. https://www.aitoolgpt.com
10 https://www.chatsverse.xyz
11. https://chatforai.com
12. https://desk.im
13. https://ai.ls
14. https://ai.ci
15. https://talk.xiu.ee
16. https://www.scyu.app
17. https://www.chat2ai.cn
18. https://aigcfun.com
19. https://chat.forchange.cn
20. https://94gpt.com
21. https://chat.yqcloud.top
22. https://ai117.com
23. https://chat.zecoba.cn
24. https://ai.yiios.com
25. https://xc.com
26. https://chat.paoying.net
27. https://ai-toolbox.codefuture.top
1. https://chat.tgbot.co
2. https://chat.theb.ai
3. https://freechatgpt.chat
4. https://fastgpt.app
5. https://freegpt.cc
6. https://freegpt.one
7. https://chatgpt.ddiu.me
8. https://chat.ninvfeng.xyz
9. https://www.aitoolgpt.com
10 https://www.chatsverse.xyz
11. https://chatforai.com
12. https://desk.im
13. https://ai.ls
14. https://ai.ci
15. https://talk.xiu.ee
16. https://www.scyu.app
17. https://www.chat2ai.cn
18. https://aigcfun.com
19. https://chat.forchange.cn
20. https://94gpt.com
21. https://chat.yqcloud.top
22. https://ai117.com
23. https://chat.zecoba.cn
24. https://ai.yiios.com
25. https://xc.com
26. https://chat.paoying.net
27. https://ai-toolbox.codefuture.top
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Factory of humanoid robots in China
Artificial intelligence 🤖
Artificial intelligence 🤖
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One of the most underrated features of GPT-4o is that it can now turn raster images into 3D.😁
The feature is not yet available - it will appear along with the voice assistant.
But designers can start licking their lips now.
But designers can start licking their lips now.
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🌟Join our discussion group 👉
https://t.iss.one/+KRTCexM7FOViM2Rl
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Getting into neural networks: a massive, detailed Deep Learning textbook has been released 💬
Inside you'll find everything essential about models: what transformers are, how image generation works, and much more. To reinforce the learning, there are 60 exercises in Python Notebook available on the website 🤖
https://udlbook.github.io/udlbook/
Inside you'll find everything essential about models: what transformers are, how image generation works, and much more. To reinforce the learning, there are 60 exercises in Python Notebook available on the website 🤖
https://udlbook.github.io/udlbook/
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Xiaomi has launched a factory with no humans, where only robots with neural networks work 🤖
You heard that right: there isn't a single human inside. The robots assemble phones, check their quality, and even clean up after themselves. The speed is insane — one phone per second! And these machines can work around the clock without breaks.
When neural networks take over our jobs, we won't even be able to get a job at the factory.⏰
You heard that right: there isn't a single human inside. The robots assemble phones, check their quality, and even clean up after themselves. The speed is insane — one phone per second! And these machines can work around the clock without breaks.
When neural networks take over our jobs, we won't even be able to get a job at the factory.⏰
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Bloomberg reports:
OpenAI's top management claims the company is "on the brink of achieving" systems that can solve basic tasks as well as a person with a doctorate-level education, but without access to any tools.
➡️ This is considered Level 2 in OpenAI's developed classification system, which currently places us at Level 1, transitioning chatbots into reasoners. Not just any reasoners, but those with doctorate-level education.
➡️ Level 3 is designated for "Agents" defined as systems capable of functioning autonomously for several days, achieving goals set by users.
➡️ Level 4 introduces the ability to produce scientific innovations, marking the gradual onset of exponential growth (FOOM).
➡️ Level 5, akin to "singularity," was humorously omitted from official classifications to perhaps keep stakeholders and governmental bodies at ease.
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Mastering Invideo AI - A Complete Guide to Text-to-Video https://www.udemy.com/course/mastering-invideo-ai-a-complete-guide-to-text-to-video/?couponCode=JULY_24 Free for limited time
Udemy
Mastering Invideo AI - A Complete Guide to Text-to-Video
Create videos with text prompts - Invideo AI - Type any topic and get a video with script, visuals, voiceover and music.
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Neural networks make your ordinary dreams at a temperature of 37 Celsius.
Look like Michael Bay-level transitions 😄
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☝️ Midjourney 6.1 has been released 😊
➡️ This update has improved the rendering of hands, feet, bodies, plants, and animals, as well as textures, and reduced pixel artifacts.
➡️ Minor details in images, such as eyes and distant hands, have become more accurate and correct.
➡️ The new version includes 2x image and texture quality enhancers and provides approximately 25% faster processing of standard tasks.
➡️ The accuracy of text in images has been improved.
➡️ This update has improved the rendering of hands, feet, bodies, plants, and animals, as well as textures, and reduced pixel artifacts.
➡️ Minor details in images, such as eyes and distant hands, have become more accurate and correct.
➡️ The new version includes 2x image and texture quality enhancers and provides approximately 25% faster processing of standard tasks.
➡️ The accuracy of text in images has been improved.
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🧏⌨Data Science Project Ideas for Freshers
📎Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.
📎Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.
📎Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.
📎Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.
📎Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.
📎Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).
📎Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.
📎Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.
📎Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.
📎A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.
Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.
Follow us 👇👇👇
https://t.iss.one/Aitechnologylearning
📎Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.
📎Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.
📎Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.
📎Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.
📎Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.
📎Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).
📎Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.
📎Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.
📎Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.
📎A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.
Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.
Follow us 👇👇👇
https://t.iss.one/Aitechnologylearning
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📂Key Concepts for Machine Learning Interviews
🔖1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
🔖2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
🔖3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
🔖4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
🔖5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
🔖6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
🔖7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
🔖8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
🔖9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
🔖10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
🔖11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
🔖12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
🔖13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
🔖14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
🔖15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.
I have curated the best interview resources to crack Data Science Interviews
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🔖1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
🔖2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
🔖3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
🔖4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
🔖5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
🔖6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
🔖7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
🔖8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
🔖9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
🔖10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
🔖11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
🔖12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
🔖13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
🔖14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
🔖15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.
I have curated the best interview resources to crack Data Science Interviews
Follow us👇👇👇
https://t.iss.one/Aitechnologylearning
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