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

A neuron calculates the "weighted sum" of its inputs and adds a bias.

Now, the predicted value can be anything from -∞ to +∞. The neuron does not know the bounds of the value. So, how do we decide whether the neuron should fire or not? Predict whether the class label is positive or negative (in this case, we have two classes). Thus, add an activation function to make the neural network decide whether a neuron should be activated or not. Hence the given name, "activation functions". 




# Types of activation functions
The following section discusses several different types of activation functions that are used in deep learning:

## Step function
The first thing that comes to mind is: How about a threshold-based activation function? We saw this activation function when we learned about perceptrons [here](https://www.educative.io/collection/page/4998094290681856/6042323658997760/5568494276968448). 

If the predicted value ($y$) of the output is above a certain value(threshold), then declare it activated.


**Advantages**

In a binary classification problem, where you want to specify whether the class is "yes" or "no," a step function would do the job. 

**Disadvantages**

The problem with a step function is that it does not cater to multi-value outputs. For example, it cannot support classifying the inputs into one of several categories. 

Learn about different activation functions.

✨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

Activation Functions

Types of activation functions

Step function