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keras python

keras python - using models

This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies used in deep learning. In addition, this course will help you understand the importance of deep learning techniques. You will examine simple models like perceptron before learning more complex yet powerful deep learning models. The course will provide hands-on practical knowledge of how to code simple and complex deep learning models in NumPy, a powerful Python library and Keras, a cutting-edge library for deep learning in Python. You can test your knowledge with the quizzes that are provided at the end of every lesson and coding challenges that will help you gain a higher understanding. By the end of the course, you should have a general understanding of the basics in deep learning and you will be equipped with the right tools to learn more advanced concepts.

A Beginner's Guide to Deep Learning

## The letter classification dataset
The dataset consists of pixel values for generating A, B, and C along with their labels. This is a **multiclass classification problem** because we have to predict the probability of the letter A, B, or C, given the pixel configuration.

> 📝 Note: A multiclass classification problem requires the labels to be one-hot encoded. 

> 📝 **One-hot encoding** is used to quantify categorical data, i.e., data having multiple categories. It generates a vector with the length equal to the number of categories in the data set.  If a data point belongs to the $i^{th}$ category, then the indices of this vector are assigned the value 0 except for the $i^{th}$ index, which is assigned a value of 1. This helps track the categories in a numerically meaningful way.


### Dataset exploration
Explore the dataset.


# The letter classification dataset
The dataset consists of pixel values for generating A, B, and C along with their labels. This is a **multiclass classification problem** because we have to predict the probability of the letter A, B, or C, given the pixel configuration.

> 📝 Note: A multiclass classification problem requires the labels to be one-hot encoded. 

> 📝 **One-hot encoding** is used to quantify categorical data, i.e., data having multiple categories. It generates a vector with the length equal to the number of categories in the data set.  If a data point belongs to the $i^{th}$ category, then the indices of this vector are assigned the value 0 except for the $i^{th}$ index, which is assigned a value of 1. This helps track the categories in a numerically meaningful way.


## Dataset exploration
Explore the dataset.


This lessons focuses on exploration and preprocessing of the letter classification dataset.

✨Before We Begin

✨Introduction to Deep Learning

✨Simple Perceptron Models in NumPY

✨Towards Deep Neural Networks in NumPY

🖥️ Project: Build a Letter Classification Model

Deep Learning in NumPy Exam

✨Building Deep Learning Models with Keras

✨Fine-tune Keras Model

🖥️ Project: Build a Digit Recognition Model

✨Conclusion

Deep Learning in Keras Exam

Introduction to the Letter Classification Data Set

The letter classification dataset

Dataset exploration