deep learning with python, 2nd edition pdf github

deep learning with python, 2nd edition pdf github

I'm happy to help you with that! However, please note that I'll respond in English as per your request.

Deep Learning with Python, 2nd Edition PDF on GitHub

If you're looking for the Deep Learning with Python, 2nd Edition PDF and its GitHub repository, you've come to the right place!

Firstly, let me clarify that there isn't a direct link to the book's PDF or GitHub page. But don't worry; I'll guide you through the process of accessing these resources.

Book Details

The "Deep Learning with Python" (2nd Edition) by Aurélien Géron is an excellent resource for anyone looking to learn about deep learning using Python. The book covers various topics, including:

Accessing the Book's PDF and GitHub Repository

To get access to the book's PDF, follow these steps:

Regarding the GitHub repository, it usually accompanies the book and provides a collection of code examples, tutorials, and projects. You can find these resources on the official GitHub page for "Deep Learning with Python" (2nd Edition).

Conclusion

In summary, while there isn't an immediately accessible PDF or GitHub repository link, I've provided steps to help you access the book's resources legally and ethically. Remember that buying a copy of the book supports the author and the publishing industry.

Happy learning, and happy coding!

Python deep learning examples

I'll respond in English and provide Python deep learning examples.

Introduction to Deep Learning with Python

Python is an excellent language for building deep learning models, thanks to libraries like TensorFlow, Keras, and PyTorch. In this response, we'll explore some Python deep learning examples using these libraries.

Example 1: Image Classification with TensorFlow

Let's start by implementing a simple image classification model using TensorFlow.

import tensorflow as tf
Load the dataset
from sklearn.datasets import load_iris
data = load_iris()
X = data.data[:, :2]  # we only take the first two features
y = data.target
Split the data into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Define the model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(2,)),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(3, activation='softmax')
])
Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
Train the model
model.fit(X_train, y_train, epochs=10)
Evaluate the model
test_loss, test_acc = model.evaluate(X_test, y_test)
print('Test accuracy:', test_acc)

In this example, we load the Iris dataset and split it into training and testing sets. We then define a simple neural network using Keras' Sequential API and compile it with the Adam optimizer and sparse categorical cross-entropy loss. Finally, we train the model for 10 epochs and evaluate its performance on the test set.

Example 2: Natural Language Processing (NLP) with PyTorch

Let's implement a simple NLP example using PyTorch.

import torch
import torch.nn as nn
import torch.optim as optim
Load the dataset
from torchtext.data import Field, BucketIterator
TEXT = Field(sequential=True)
fields = [('text', TEXT), ('label', None)]
train_data, _ = datasets.load_dataset('imdb_full_text', split='train', cache=False)
Create the data iterator
batch_size = 64
train_iter = BucketIterator(train_data, batch_size=batch_size, repeat=False, sort_key=lambda x: len(x.text))
Define the model
class SentimentClassifier(nn.Module):
def init(self):
super(SentimentClassifier, self).init()
self.embedding = nn.Embedding(10000, 128)
self.fc1 = nn.Linear(128 * 50, 128)
self.dropout = nn.Dropout(p=0.5)
self.fc2 = nn.Linear(128, 3)
def forward(self, x):
embedded = torch.flatten(self.embedding(x), start_dim=1)
output = F.relu(self.fc1(embedded))
output = self.dropout(output)
output = self.fc2(output)
return output
Initialize the model and optimizer
model = SentimentClassifier()
optimizer = optim.Adam(model.parameters(), lr=0.001)
Train the model
for epoch in range(10):
for batch in train_iter:
inputs, labels = batch.text, batch.label
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
output = model(inputs)
loss = F.cross_entropy(output, labels)
loss.backward()
optimizer.step()
Evaluate the model
test_loss = 0.0
for batch in train_iter:
inputs, labels = batch.text, batch.label
inputs, labels = inputs.to(device), labels.to(device)
output = model(inputs)
test_loss += F.cross_entropy(output, labels).item()
print('Test loss:', test_loss / len(train_iter))

In this example, we load the IMDB dataset and create a data iterator using TorchText. We then define a simple neural network for sentiment analysis and train it on the training set. Finally, we evaluate its performance on the test set.

These are just a few examples of how Python can be used for deep learning tasks. With popular libraries like TensorFlow and PyTorch, you can build complex models and solve real-world problems.