Deep Learning

This is the stuff people are salivating for

Deep learning, the stuff that all the AI and machine learning are all focused at this point because it performs magic

Alot of content needs to be unpacked: Layers, Encoders, decoders, transformers, Attention layer, GPT-3, BERT, GANs, RNNs, LSTM, GRU, CNNs, Autoencoders, VAEs, GANs, and more.

Future projects

content marketing description maker -

import transformers

# load the GPT-3 model
model = transformers.GPT3Model.from_pretrained("gpt3")

# load the training data
data = load_data("sunbelt_rentals_content_marketing.txt")

# fine-tune the model on the training data
model.fine_tune(data, train_batch_size=8, learning_rate=1e-4)

# generate text using the fine-tuned model
generated_text = model.generate(prompt="Sunbelt Rentals offers a wide range of equipment for your construction needs.")

# use the generated text in your content marketing efforts
# ...

The craze for better understanding nlp from instruct and chat is reinforcement learning

import tensorflow as tf

# define the model
model = some_nlp_model()

# define the reinforcement learning objective
def reinforce_objective(generated_text):
  # evaluate the generated text using human feedback
  reward = evaluate_with_human_feedback(generated_text)
  # return the negative reward as the objective to maximize
  return -reward

# define the training loop
for epoch in range(num_epochs):
  # generate text using the model
  generated_text = model.generate(prompt="Some NLP prompt")
  # compute the reinforcement learning objective
  objective = reinforce_objective(generated_text)
  # update the model parameters to maximize the objective
  tf.keras.optimizers.SGD(learning_rate=1e-3).minimize(objective, model.variables)

# use the trained model to generate text
generated_text = model.generate(prompt="Another NLP prompt")

Reference material

  • Fast AI
  • HuggingFace
  • https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-deep-learning
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