GPU Instances

Understands, interprets, and generates human language in a way that is both meaningful and contextually relevant.
Thanks to models and algorithms specialized in:

• Text classification
• Machine translation
• Entailment prediction
• Named entity recognition
• Sequence-to-sequence, like BERT for text extraction
• Text similarity search, like BERT to find semantic similarities
• Language modeling

Converts spoken language into written text, facilitating the translation of verbal communication into machine-readable data.
Thanks to models and algorithms specialized in:

• Speaker recognition (using Fast Fourier Transform (FFT) and a 1D Convnet., as explained by Fabi Badine)
• Accent recognition
• KWS, keywords spotting used by many HCI (Human-computer interfaces) like Alexa or Siri
  (example: RNN-T on the Librispeech dataset.)

Generates new content, such as images, text, audio, code. It autonomously produces novel and coherent outputs, expanding the realm of AI-generated content beyond replication or prediction.
With models and algorithms specialized in:

• Image generation like Deep Dream that produces hallucination-like visuals as fchollet explains it
• Text generation with Transformers Models also called LLMs (Large Language Models) such as GPT2
• Code generation

Enables machines to interpret and understand visual information from the world, much like human vision.
Thanks to models and algorithms specialized in:

• Image classification (using BigTransfer(BiT) trained on public datasets, along with code in TF2, Jax and Pytorch as explained by Sayan Nat)
• Image segmentation
• 3D
• OCR
• Image enhancement
• Image and text
• vision models interpretability
• Image similarity search