Get insights from your text. Over the years, we've built many state-of-art Deep Learning models. Today, you can use them to get insights from unstructured text. And the best part: At @abacusai
, we cover your solution end-to-end. Bring your data, and we will do the rest!
@abacusai
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Extract Insights from Unstructured Text with Deep Learning Models
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Free Machine Learning Roadmap: Python, Google Crash Course, Coursera
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Machine Learning Roadmap: • Introduction to Python Programming (Udacity)
• Machine Learning Crash Course (Google)
• Machine Learning (Coursera) In that order. They are all free. In the next few days, we'll share many more resources. Stay tuned @abacusai
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Stop Using Excel for Model Metrics with Abacus AI
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Stop using Excel to write down your model metrics. Look at this screenshot: These are some of the metrics you can see using @abacusai
. In a quick snapshot, we put everything that matters right in front of you. -
Master Transformers for Text, Vision, and Speech with HuggingFace
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By the end of this course, you will know how to apply Transformers to (almost) any ML problem! This includes text, vision, and speech. And you'll do that using HuggingFace libraries. Stay tuned and follow @abacusai
! There's a lot more coming! -
Free HuggingFace AI/ML Course on Natural Language Processing and Transformers
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FREE AI/ML course you can't miss: The HuggingFace Course https://
huggingface.co/course/chapter
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… The hottest field in AI right now is Natural Language Processing. This is the place to start if you want to learn about Transformers. Read on for what to expect: -
Create Custom ChatGPT with Abacus AI Data Integration
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Create your own Custom ChatGPT Use @abacusai to: – Ingest data from different sources
– create embeddings using open AI or BERT
– use our vector-matching engine
– feed results to GPT-4 to create a custom chat experience -
Autoencoders as Popular Supervised Learning Methods
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10. Autoencoders are one of the most popular supervised learning methods. Make sure you follow @abacusai
! There's a lot more coming this month! -
Neural Networks: Accuracy, Autoencoders, and Convolutional Properties
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7. Your model's accuracy can't be used as a loss function to train a neural network. 8. An autoencoder is a neural network that automatically learns by using its inputs as the expected output. 9. Convolutional neural networks are translation invariant.
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Understanding Activation Functions: Softmax and Neural Network Neurons
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3. Softmax is an activation function that always returns the input value if positive and zeroes otherwise. 4. In neural networks, an activation function's most essential role is to decide whether a unit (or neuron) should fire.
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Learning Rate Impact and ReLU Activation Function Explained
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5. Using a learning rate that's too low will cause the training process to be very slow. 6. ReLU (or Rectified Linear Unit) is an activation function that, given an input vector, generates an output where the sum of the values in the vector is equal to one.