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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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

# Artificial neural network (ANN)
An Artificial Neural Network (ANN) is composed of multiple perceptrons/ neurons at each layer. ANN is a feedforward neural network because inputs are only processed in the forward direction, i.e., each layer feeds its information to the layer next to it until the final layer is reached.

Each network consists of an input, hidden layers, and an output layer. Essentially, each layer learns the weights that best approximates the mapping between the input and the output.

## Applications
ANN can be used to solve problems related to:
- Tabular data
- Image data
- Text data


## Advantages 

ANN is capable of learning any non-linear functions through the use of activation functions which introduce non-linear properties to the network. These activation functions help the network learn any complex relationship between the features and the label. In the absence of an activation function, the network can only learn the linear relationship but can never learn complex relationships. 

Congratulations! You have covered the basics of Deep learning. Let's have a quick overview of what you learned and discussed. Let's discuss the options available for you with the new skills you have acquired.

✨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

🏆 A Quick Overview

Artificial neural network (ANN)

Applications

Advantages