Old vs New Drake Lyrics With AI

I use machine learning to identify old Drake lyric from the new Drake lyric with 86% accuracy! This shows that there is a difference between old and new Drake song lyrics. Now let’s find those differences and test what songs the model predicts as old and new Drake.

• **Skip to section 5 and 6 if not interested in the process and just the results.

This article is divided into 7 sections:

1. Data exploration, cleaning and processing

2. ML modelling using count vectorizer

3. ML modelling using Tfidf vectorizer

4. Parameter tuning on top models from 2 and 3

5. Analyzing the top 40 features from models to find the difference between old and new Drake.

6. Let’s see the model in action by predicting singles and mixtape songs as old or new Drake.

7. Sequence modelling using LSTM

#data-science #drake #classification #naturallanguageprocessing #machine-learning

What is GEEK

Buddha Community

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

• Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
• Slowing Down the Weight Norm Increase in Momentum-based Optimizers
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
• Coherent Reconstruction of Multiple Humans from a Single Image
• Variational Bayesian Monte Carlo with Noisy Likelihoods
• GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
• Are we done with ImageNet?
• How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
• Background Modeling via Uncertainty Estimation for Weakly-supervised Action Localization
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
• Learning a Unified Sample Weighting Network for Object Detection

Articles

• Deepfake Detection Challenge Results: An open initiative to advance AI
• Slightly Unnerving AI Produces Human Faces Out of Totally Pixelated Photos
• NeoML Released as TensorFlow Alternative
• Google Meet takes on Zoom with AI-powered noise cancellation
• Open Compound Domain Adaptation
• Dense pose for animal classes with transfer learning
• 10 Ways Enterprises Are Getting Results From AI Strategies
• An understanding of AI’s limitations is starting to sink in
• Why we switched from Flask to FastAPI for production machine learning
• Simple Transformers
• The key differences between rule-based AI and machine learning
• Deep learning-based surrogate models outperform simulators and could hasten scientific discoveries
• HANDS-ON GUIDE TO OBJECT DETECTION USING YOLO
• Artificial neural networks that mimic the flexibility and computing power of the brain
• MIT researchers train AI to predict how humans paint works of art
• What Do Machine Learning Engineers Do Anyway?
• AI Is Amazing But Complicated, And We Don’t Necessarily Need To Plunge In Headfirst
• Deep learning networks track up to 100 animals simultaneously
• Is artificial intelligence changing art?

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

Old vs New Drake Lyrics With AI

I use machine learning to identify old Drake lyric from the new Drake lyric with 86% accuracy! This shows that there is a difference between old and new Drake song lyrics. Now let’s find those differences and test what songs the model predicts as old and new Drake.

• **Skip to section 5 and 6 if not interested in the process and just the results.

This article is divided into 7 sections:

1. Data exploration, cleaning and processing

2. ML modelling using count vectorizer

3. ML modelling using Tfidf vectorizer

4. Parameter tuning on top models from 2 and 3

5. Analyzing the top 40 features from models to find the difference between old and new Drake.

6. Let’s see the model in action by predicting singles and mixtape songs as old or new Drake.

7. Sequence modelling using LSTM

#data-science #drake #classification #naturallanguageprocessing #machine-learning

Old vs New Drake Lyrics With AI

I use machine learning to identify old Drake lyric from the new Drake lyric with 86% accuracy! This shows that there is a difference between old and new Drake song lyrics. Now let’s find those differences and test what songs the model predicts as old and new Drake.

• **Skip to section 5 and 6 if not interested in the process and just the results.

This article is divided into 7 sections:

1. Data exploration, cleaning and processing

2. ML modelling using count vectorizer

3. ML modelling using Tfidf vectorizer

4. Parameter tuning on top models from 2 and 3

5. Analyzing the top 40 features from models to find the difference between old and new Drake.

6. Let’s see the model in action by predicting singles and mixtape songs as old or new Drake.

7. Sequence modelling using LSTM

#data-science #drake #classification #naturallanguageprocessing #machine-learning