Tag: explained

Price: $36.99
(as of Dec 24,2024 17:07:46 UTC – Details)

ASIN ‏ : ‎ B09QNZBZMN
Publisher ‏ : ‎ Independently published (January 22, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 285 pages
ISBN-13 ‏ : ‎ 979-8406364208
Item Weight ‏ : ‎ 1.37 pounds
Dimensions ‏ : ‎ 8 x 0.65 x 10 inches

Artificial Intelligence and Deep Learning with Python: Every Line of Code Explained For Readers New to AI and New to Python

Are you interested in diving into the world of Artificial Intelligence and Deep Learning, but don’t know where to start? Python is a powerful programming language that is widely used in the field of AI and machine learning. In this post, we will break down and explain every line of code for those who are new to AI and new to Python.

First, let’s start with the basics. Python is a high-level, interpreted programming language that is easy to learn and versatile. It has a clean and simple syntax, making it a popular choice for beginners. In the field of AI and machine learning, Python is widely used due to its extensive libraries and frameworks such as TensorFlow, Keras, and Scikit-learn.

Now, let’s dive into some code. Below is a simple example of a neural network using TensorFlow, a popular deep learning library in Python.

import tensorflow as tf<br />
<br />
# Define the neural network architecture<br />
model = tf.keras.models.Sequential([<br />
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),<br />
    tf.keras.layers.Dense(64, activation='relu'),<br />
    tf.keras.layers.Dense(10, activation='softmax')<br />
])<br />
<br />
# Compile the model<br />
model.compile(optimizer='adam',<br />
              loss='sparse_categorical_crossentropy',<br />
              metrics=['accuracy'])<br />
<br />
# Load and preprocess the data<br />
mnist = tf.keras.datasets.mnist<br />
(x_train, y_train), (x_test, y_test) = mnist.load_data()<br />
x_train, x_test = x_train / 255.0, x_test / 255.0<br />
<br />
# Train the model<br />
model.fit(x_train, y_train, epochs=5)<br />
<br />
# Evaluate the model<br />
model.evaluate(x_test, y_test)<br />
```<br />
<br />
Let's break down the code step by step:<br />
<br />
1. We import the TensorFlow library, which is used for building and training neural networks.<br />
2. We define the architecture of the neural network using the Sequential model in Keras. This model consists of three dense layers with 64 units each and different activation functions.<br />
3. We compile the model by specifying the optimizer, loss function, and metrics for evaluation.<br />
4. We load and preprocess the MNIST dataset, which consists of handwritten digits.<br />
5. We train the model on the training data for 5 epochs.<br />
6. Finally, we evaluate the model on the test data to measure its performance.<br />
<br />
This is just a simple example to get you started with AI and deep learning in Python. By understanding and experimenting with each line of code, you can gain a better grasp of the concepts and techniques used in this exciting field.<br />
<br />
Stay tuned for more tutorials and explanations on AI and deep learning with Python!

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