This is a comprehensive module for learners who wish to learn deep learning techniques with practical examples.

Neural Networks: Beginners to Advanced

This beginner level and highly comprehensive module is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies that are used in deep learning. In addition, this module 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 module 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. By the end of the module, you should have a general understanding of the basics of deep learning, and you will be equipped with the right tools to learn more advanced concepts.

Deep Learning with Keras

## Problem statement

The IRIS data set is taken from the [UCI](https://archive.ics.uci.edu/ml/datasets/iris) repository of datasets. It has four features and a label that classifies the flower.

|sepal_length|	sepal_width	|petal_length	|petal_width|	species|
|-|-|-|-|-|
|6.7|	3.3	|5.7	|2.1|	Iris-setosa|
|6.4	|3.1	|5.5	|1.8	|Iris-setosa|
|4.9	|3.1	|1.5	|0.1	|Iris-virginca|
|5.5|	3.5	|1.3|	0.2|	Iris-virginca|


The flowers can belong to either of the following classes:

Find the optimal weights that can classify each flower: Iris-setosa and non-iris setosa.

The data is in `iris.csv` file. Read the data from the `csv` file and store the features and the labels as a NumPy array prior to applying the perceptron algorithm.

For preprocessing the dataset, Python's library `pandas` is the best choice.

>  📝 Note: We will not cover every detail of the `pandas` package but we will cover the basic preprocessing techniques.






# Problem statement

The IRIS data set is taken from the [UCI](https://archive.ics.uci.edu/ml/datasets/iris) repository of datasets. It has four features and a label that classifies the flower.

Classify flowers using the perceptron algorithm.

✨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

🍀 Challenge: Classification Using IRIS DataSet

Problem statement

sepal_length	sepal_width	petal_length	petal_width	species
6.7	3.3	5.7	2.1	Iris-setosa
6.4	3.1	5.5	1.8	Iris-setosa
4.9	3.1	1.5	0.1	Iris-virginca
5.5	3.5	1.3	0.2	Iris-virginca