Gain insights into deep learning fundamentals, explore perceptron and advanced models, and discover practical coding in NumPy and Keras. Test your knowledge with quizzes and coding challenges.

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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

## What is the model's capacity?
Model capacity is the model's ability to capture the patterns in the data. 
The model's capacity is directly related to the terms "overfitting" and "underfitting".

## How to increase model capacity?

The model's capacity can be increased by increasing model complexity, i.e., adding the number of nodes in the hidden layer or adding hidden layers.


## Workflow for increasing model capacity

1. Start with a simple network.
2. Continue adding capacity until the validation score begins improving.

Here is an example to visualize this process:


# What is the model's capacity?
Model capacity is the model's ability to capture the patterns in the data. 
The model's capacity is directly related to the terms "overfitting" and "underfitting".

# How to increase model capacity?

The model's capacity can be increased by increasing model complexity, i.e., adding the number of nodes in the hidden layer or adding hidden layers.


# Workflow for increasing model capacity

1. Start with a simple network.
2. Continue adding capacity until the validation score begins improving.

Here is an example to visualize this process:


Examine another concept in fine-tuning Keras’ model called model capacity.

✨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

Anomaly Detection in Medical Images: Python, TF, and PyTorch

🖥️ Project: Build a Digit Recognition Model

✨Conclusion

Deep Learning in Keras Exam

Purchase Price Prediction for Used Cars Using Deep Learning

Model Capacity

What is the model’s capacity?

How to increase model capacity?