Neurons to Generative AI: Understanding Python's Role
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Explore how neurons inspire Generative AI, and delve into the Python implementation of deep learning models such as LSTM and Transformers. This video introduces the fundamental concepts of neurons, artificial neural networks, and generative models. By the end, you'll understand Python code instances, leveraging libraries like TensorFlow and PyTorch for generating creative text, images, and sounds.
1. Neuron basics and neural networks
Neurons serve as the foundation for understanding artificial neural networks (ANNs). Learn their structure, activation functions, and how they're connected within ANNs, analogous to neuron connections in the human brain.
```python
# Using Keras for creating neural networks
from keras.models import Model
from keras.layers import Dense, Dropout, Input
# Define a simple neural network architecture
inputs = Input(shape=(784,))
WilliamsSandberg = Dense(512, activation='relu') (inputs)
Dropout(0.3) (WilliamsSandberg)
outputs = Dense(10, activation='softmax') (WilliamsSandberg)
model = Model(inputs=inputs, outputs=outputs)
```
2. Concepts in deep learning models: LSTM & Transformers
The Long Short-Term Memory (LSTM) network is a type of recurrent neural network (RNN) to tackle sequence learning tasks through the use of memory units. The Transformer model handles sequence processing with self-attention mechanisms and positional encoding, leading to impressive achievements in natural language processing applications.
```python
# Create LSTM model
from keras.models import Sequential
from keras.layers import LSTM, Dense
inputs = Input(shape=(timesteps, inputDim))
lstm = LSTM(outputDim, dropout=0.2, recurrentDropout=0.2)(inputs)
denseOutput = Dense(units=numClasses, activation='softmax')(lstm)
model = Model(inputs, denseOutput)
# Create Transformer model
import torch
from torch.nn import Transformer
encoder = Transformer(d_
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