☝️ 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.
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📎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://news.1rj.ru/str/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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https://news.1rj.ru/str/Aitechnologylearning
🔖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://news.1rj.ru/str/Aitechnologylearning
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Top YouTube Channels for Learning AI in 2024
1️⃣ Matt Wolfe : - https://www.youtube.com/@mreflow
2️⃣ AI Explained : - https://www.youtube.com/@aiexplained-official
3️⃣ Two Minute Papers :- https://www.youtube.com/@TwoMinutePapers
4️⃣ DeepLearningAI : - https://www.youtube.com/@Deeplearningai
5️⃣ The AI Advantage: - https://www.youtube.com/@aiadvantage
6️⃣ MattVidPro AI : - https://www.youtube.com/@MattVidPro
7️⃣ Sentdex :- https://www.youtube.com/@sentdex
https://news.1rj.ru/str/Aitechnologylearning
1️⃣ Matt Wolfe : - https://www.youtube.com/@mreflow
2️⃣ AI Explained : - https://www.youtube.com/@aiexplained-official
3️⃣ Two Minute Papers :- https://www.youtube.com/@TwoMinutePapers
4️⃣ DeepLearningAI : - https://www.youtube.com/@Deeplearningai
5️⃣ The AI Advantage: - https://www.youtube.com/@aiadvantage
6️⃣ MattVidPro AI : - https://www.youtube.com/@MattVidPro
7️⃣ Sentdex :- https://www.youtube.com/@sentdex
https://news.1rj.ru/str/Aitechnologylearning
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🔴 Blockchain development is a very high demanding and high paying skill. Join our new channel to learn
👇
https://news.1rj.ru/str/blockchaindv
👇
https://news.1rj.ru/str/blockchaindv
Ai updates- Artificial intelligence || AI DEVELOPERS & AI TECHNOLOGY || Chatgpt || Midjourney pinned «🔴 Blockchain development is a very high demanding and high paying skill. Join our new channel to learn 👇 https://news.1rj.ru/str/blockchaindv»
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Dario Amodei says $100 billion AI data centers will be built by 2027 and he is bullish about powerful AI happening soon because we are starting to reach PhD-level intelligence
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500 AI Machine Learning Projects list with code
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
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Build Your Own Robot Using Python 2024_241114_234909.pdf
11.5 MB
Keep learning and please share our channel with everyone. 😊
https://news.1rj.ru/str/Aitechnologylearning
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NEW: OpenAI is preparing to launch a computer using AI agent codenamed “Operator” that take actions on a person’s behalf through a browser, such as writing code or booking travel.
Staff told in all-hands management today it will be released in January. 🟥 Learn blockchain technology https://news.1rj.ru/str/blockchaindv
Staff told in all-hands management today it will be released in January. 🟥 Learn blockchain technology https://news.1rj.ru/str/blockchaindv
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Geoffrey Hinton warns that killer robots are coming soon. All governments that sell arms refuse to regulate military uses of AI.
Major arms manufacturers are eager to build killer robots, completely disregarding Asimov's first law of robotics.
🔴 Learn blockchain technology
https://news.1rj.ru/str/blockchaindv
Major arms manufacturers are eager to build killer robots, completely disregarding Asimov's first law of robotics.
🔴 Learn blockchain technology
https://news.1rj.ru/str/blockchaindv
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Jensen says now we have the necessary technology between all the computers and the latest generative AI tech, that the time has come for humanoid robotics.
He introduces the new GR00T workflows for humanoid robots at the AI Summit Japan.
Jensen says now we have the necessary technology between all the computers and the latest generative AI tech, that the time has come for humanoid robotics.
He introduces the new GR00T workflows for humanoid robots at the AI Summit Japan.
🔴 Learn blockchain development
https://news.1rj.ru/str/blockchaindv
He introduces the new GR00T workflows for humanoid robots at the AI Summit Japan.
Jensen says now we have the necessary technology between all the computers and the latest generative AI tech, that the time has come for humanoid robotics.
He introduces the new GR00T workflows for humanoid robots at the AI Summit Japan.
🔴 Learn blockchain development
https://news.1rj.ru/str/blockchaindv
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100 AI Tools to replace your tedious work:
1. Research
- ChatGPT
- YouChat
- Abacus
- Perplexity
- Copilot
- Gemini
2. Image
- Fotor
- Stability AI
- Midjourney
- Microsoft Designer
3. CopyWriting
- Rytr
- Copy AI
- Writesonic
- Adcreative AI
4. Writing
- Jasper
- HIX AI
- Jenny AI
- Textblaze
- Quillbot
5. Website
- 10Web
- Durable
- Framer
- Style AI
6. Video
- Klap
- Opus
- Eightify
- InVideo
- HeyGen
- Runway
- ImgCreator AI
- Morphstudio .xyz
7. Meeting
- Tldv
- Otter
- Noty AI
- Fireflies
8. SEO
- VidIQ
- Seona AI
- BlogSEO
- Keywrds ai
9. Chatbot
- Droxy
- Chatbase
- Mutual info
- Chatsimple
10. Presentation
- Decktopus
- Slides AI
- Gamma AI
- Designs AI
- Beautiful AI
11. Automation
- Make
- Zapier
- Xembly
- Bardeen
12. Prompts
- FlowGPT
- Alicent AI
- PromptBox
- Promptbase
- Snack Prompt
13. UI/UX
- Figma
- Uizard
- UiMagic
- Photoshop
14. Design
- Canva
- Flair AI
- Designify
- Clipdrop
- Autodraw
- Magician design
15. Logo Generator
- Looka
- Designs AI
- Brandmark
- Stockimg AI
- Namecheap
16. Audio
- Lovo ai
- Eleven labs
- Songburst AI
- Adobe Podcast
17. Marketing
- Pencil
- Ai-Ads
- AdCopy
- Simplified
- AdCreative
18. Startup
- Tome
- Ideas AI
- Namelix
- Pitchgrade
- Validator AI
19. Productivity
- Merlin
- Tinywow
- Notion AI
- Adobe Sensei
- Personal AI
20. Social media management
- Tapilo
- Typefully
- Hypefury
- TweetHunter
Save for later! 🔑
🔴 Learn blockchain development
https://news.1rj.ru/str/blockchaindv
1. Research
- ChatGPT
- YouChat
- Abacus
- Perplexity
- Copilot
- Gemini
2. Image
- Fotor
- Stability AI
- Midjourney
- Microsoft Designer
3. CopyWriting
- Rytr
- Copy AI
- Writesonic
- Adcreative AI
4. Writing
- Jasper
- HIX AI
- Jenny AI
- Textblaze
- Quillbot
5. Website
- 10Web
- Durable
- Framer
- Style AI
6. Video
- Klap
- Opus
- Eightify
- InVideo
- HeyGen
- Runway
- ImgCreator AI
- Morphstudio .xyz
7. Meeting
- Tldv
- Otter
- Noty AI
- Fireflies
8. SEO
- VidIQ
- Seona AI
- BlogSEO
- Keywrds ai
9. Chatbot
- Droxy
- Chatbase
- Mutual info
- Chatsimple
10. Presentation
- Decktopus
- Slides AI
- Gamma AI
- Designs AI
- Beautiful AI
11. Automation
- Make
- Zapier
- Xembly
- Bardeen
12. Prompts
- FlowGPT
- Alicent AI
- PromptBox
- Promptbase
- Snack Prompt
13. UI/UX
- Figma
- Uizard
- UiMagic
- Photoshop
14. Design
- Canva
- Flair AI
- Designify
- Clipdrop
- Autodraw
- Magician design
15. Logo Generator
- Looka
- Designs AI
- Brandmark
- Stockimg AI
- Namecheap
16. Audio
- Lovo ai
- Eleven labs
- Songburst AI
- Adobe Podcast
17. Marketing
- Pencil
- Ai-Ads
- AdCopy
- Simplified
- AdCreative
18. Startup
- Tome
- Ideas AI
- Namelix
- Pitchgrade
- Validator AI
19. Productivity
- Merlin
- Tinywow
- Notion AI
- Adobe Sensei
- Personal AI
20. Social media management
- Tapilo
- Typefully
- Hypefury
- TweetHunter
Save for later! 🔑
🔴 Learn blockchain development
https://news.1rj.ru/str/blockchaindv
👍9❤3👏2
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Garry Tan says Sam Altman has told him he wants to do a $1 trillion training run for an AI model, which could lead to understanding the nature of physics and solving engineering problems like nuclear fusion
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