It reduces the need for large labeled datasets

 It always improves test accuracy regardless of the task

It eliminates the need for any fine-tuning

 It prevents overfitting entirely

{'deep': 1, 'learning': 2, 'I': 3, 'love': 4}

{'learning': 1, 'deep': 2, 'love': 3, 'I': 4}

{'I': 1, 'love': 2, 'deep': 3, 'learning': 4}

{'i': 1, 'love': 2, 'deep': 3, 'learning': 4}

It classifies input data

It acts as a loss function

It discriminates between real and fake samples

It generates new data samples

Freezes all layers

Loads the classifier head from ImageNet

Trains from scratch

Removes the final classification layers

Self-Supervised Learning

Reinforcement Learning

Unsupervised Learning

Few-Shot Learning

 Embedding, classification, and next sentence embeddings

Token, segment, and position embeddings

Convolution, pooling, and recurrent embeddings

 Recurrent, feedforward, and attention embeddings

 It randomly reinitializes the base model weights

It freezes the base model layers to retain pre-learned features

It allows all layers to be retrained

It deletes all pretrained layers

(None, 28, 28)

(None, 28, 28, 1)

(None, 100)

(None, 784)

It is used to train other machine learning models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks.

It is used to diagnose and treat diseases

It is used to generate text, translate languages, and write different kinds of creative content.

 It is used to solve many natural language processing tasks, such as question answering, text classification, and natural language inference.

(64, 3)

(1, 64, 64, 3)

 (64, 64, 3)

(3, 64, 64)