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

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

# Problem statement
The _titanic dataset_ classifies survival or not survival given the features. This is the same code explained [here](https://www.educative.io/courses/beginners-guide-to-deep-learning/using-models).

The training data `train.csv` and the testing data `test.csv` file are provided.  A sample _model architecture_ is specified along with the _compile_ and the _fit_ method. The model performance is evaluated using the training data and the test data. The task is to hypertune the model parameters 🔨 so that we can achieve _at least_ 80% accuracy on training, validation, and test set.

> 📝 Note: There can be many solutions to the problem. We have provided one solution in the next lesson.

# Sample input
A sample model configuration is given.


# Sample output
Tuning of hyperparameters related to the network structure and during the network training to 
achieve at least 80% accuracy on the training, validation and test set.


Hypertune model parameters for the model provided in the lesson.

✨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

🍀 Challenge: Hypertune Model Parameters

Problem statement