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.

Dockerfile.tar.gz

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


## Exploratory data analysis
We have two features X1 and X2 and a label. Each feature has ten data points and the label is 0 or 1. Make a decision boundary that separates the two classes.
Let's look at the #concept=Dataset#dataset.#concept#


> 📝 Note: Remember, we can only apply the perceptron algorithm if the data is linearly separable. For this, we need to draw the points on the graph to visualize the data.

Draw the data points on the #concept=EDA#graph.#concept#





 


# Exploratory data analysis
We have two features X1 and X2 and a label. Each feature has ten data points and the label is 0 or 1. Make a decision boundary that separates the two classes.
Let's look at the #concept=Dataset#dataset.#concept#


> 📝 Note: Remember, we can only apply the perceptron algorithm if the data is linearly separable. For this, we need to draw the points on the graph to visualize the data.

Draw the data points on the #concept=EDA#graph.#concept#





 

Learn to code the perceptron trick using the Stochastic Gradient Descent.

✨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

Gradient Descent: The Stochastic Update

Exploratory data analysis