Discover fundamental deep learning concepts, master coding models using NumPy and Keras, and test your knowledge with quizzes and coding challenges. Gain insights into effective deep learning techniques.

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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 first step for developing any model is reading and exploring the data.
This helps answer the questions below:

1. How many features does the data have?
2. Are there any missing data?
3. If the answer to question 2 is yes, then how should we eliminate missing data?


Take the example of the **titanic** dataset.
The columns of the dataset are given below:


The task is to determine the survival of a person given x features.

> 📝 Note: We will be using the `pandas` library in this lesson. Learn about the basics of pandas [here](https://www.educative.io/edpresso/what-is-pandas-in-python). 

The training and testing data are stored in the `train.csv` and the `test.csv` file. We should train the model on the training data and predict the performance of the model on the test data to examine how well the model generalizes on the test data.

> 📝 Note: The following lessons will use the **titanic** dataset for training. First, you will build the model, and then you will use the trained model.

Dive into the first step of the Keras workflow; Reading, exploring, and preprocessing the 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

Read and Explore the Data