🤖 Day 44 of #100DaysOfCode in Python: Advancing in Machine Learning with TensorFlow | by Elshad Karimov | Jan, 2024

Welcome to Day 44! Today, we continue our exploration of machine learning by diving into TensorFlow, an open-source library developed by the Google Brain team. TensorFlow is a powerful tool for creating and training complex machine learning models, particularly in the field of deep learning.

TensorFlow is a robust framework that allows developers to create sophisticated machine learning and deep learning models. It excels in handling large-scale neural networks with many layers, making it a go-to choice for tasks like image and speech recognition, natural language processing, and more.

  • Flexibility: TensorFlow’s flexible architecture allows you to deploy computation to one or more CPUs or GPUs, making it adaptable for various platforms.
  • Keras API: TensorFlow includes Keras, a high-level neural networks API that simplifies many aspects of creating and training neural networks.
  • Eager Execution: TensorFlow supports eager execution, making it more intuitive and simpler to work with.
  • Visualization: TensorBoard, a suite of visualization tools, helps you understand and debug your TensorFlow programs.

Using TensorFlow, you can build neural networks layer by layer. TensorFlow takes care of the underlying complex computations, letting you focus on the architecture of the network.

Install TensorFlow via pip and import it:

pip install tensorflow
import tensorflow as tf

Let’s create a basic neural network in TensorFlow:

# Define a Sequential model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)), # Input layer
tf.keras.layers.Dense(128, activation='relu'), # Hidden layer…

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