Welcome to this comprehensive tutorial on deep learning with Keras. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. It is designed to enable fast experimentation with deep neural networks.
Overview
- Deep Learning: A subset of machine learning that involves neural networks with many layers.
- Keras: A Python library for building and training neural networks.
- Neural Networks: A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Getting Started
Before you dive into deep learning with Keras, make sure you have the following prerequisites:
- Basic knowledge of Python programming.
- Familiarity with machine learning concepts.
- Installation of TensorFlow and Keras.
Step-by-Step Guide
Install TensorFlow and Keras:
pip install tensorflow
Import Libraries:
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense
Create a Simple Neural Network:
model = Sequential() model.add(Dense(128, activation='relu', input_shape=(100,))) model.add(Dense(64, activation='relu')) model.add(Dense(10, 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=5)
Evaluate the Model:
model.evaluate(x_test, y_test)
Make Predictions:
predictions = model.predict(x_test)
Further Reading
For more in-depth information and tutorials on deep learning with Keras, we recommend checking out the following resources:
Deep Learning