PRE TRAINED DEEP LEARNING MODELS

Pretrained models refer to machine learning models that have been trained on large amounts of data and are then made available for use without the need for further training on specific tasks. Here is a list of popular pretrained models across different domains:

Natural Language Processing (NLP) Models:

1. BERT (Bidirectional Encoder Representations from Transformers):

Variants: BERT base, BERT large, e.t.c

Developed by Google, BERT has achieved state-of-the-art performance on various NLP tasks.

2. GPT (Generative Pretrained Transformer):

Variants: GPT, GPT-2, GPT-3, etc.

Developed by OpenAI, GPT models use transformer-based architectures for language generation and understanding tasks.

3. RoBERTa (Robustly optimized BERT approach):

• Similar to BERT but with modifications to training objectives and hyperparameters for improved performance.

4. XLNet:

• An autoregressive language model that combines autoregressive and autoencoding methods.

5. DistilBERT:

• A distilled version of BERT that retains much of its language understanding capabilities but with fewer parameters and faster inference.

6. T5 (Text-To-Text Transfer Transformer):

• Developed by Google, T5 is a transformer-based model that converts all NLP tasks into a text-to-text format.

Computer Vision Models:

1. ResNet (Residual Neural Network):

Variants: ResNet-50, ResNet-101, etc.

• Deep convolutional neural network architectures known for their effectiveness in image classification and feature extraction.

2. VGG (Visual Geometry Group):

• Variants: VGG-16, VGG-19, etc.
• Convolutional neural network architectures with a focus on depth and simplicity.

3. Inception (GoogLeNet):

• Variants: Inception V1, Inception V2, Inception V3, etc.
• Developed by Google, Inception models use multiple parallel convolutional layers to capture different aspects of the input image.

4. EfficientNet: A family of convolutional neural networks that achieve state-of-the-art accuracy with fewer parameters and computations.

Speech Recognition Models:

1. DeepSpeech: Developed by Mozilla, DeepSpeech is an open-source speech-to-text engine based on deep learning techniques.
2. Kaldi: An open-source toolkit for speech recognition that includes pretrained models and tools for building speech recognition systems.

Reinforcement Learning Models:

1. OpenAI Gym Models: Pretrained reinforcement learning models available through the OpenAI Gym environment, such as Atari games agents.

These are just a few examples of pretrained models across different domains. The availability of pretrained models continues to grow rapidly with advancements in deep learning research and open-source contributions.