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 - 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

# Steps for building a deep learning model
The Keras workflow has the following steps to develop a deep learning model:




## 1. Read and explore the data
  This step requires reading and exploring the given dataset, preprocessing it (if required), and splitting the input features and the output label from the given dataset. These input features define _the number of nodes in the input layer_ of the Keras model. 
  

## 2. Specify architecture
  This step specifies the model architecture. For example:
   - _The number of layers_
   - _Nodes per layer_
   - _Activation function in each layer_

## 3. Compile the model
 The model compilation step involves:
   - _Specifying the loss function_
   - *Defining the optimization parameters*

## 4. Fit the model
 The model fitting helps train the model on the dataset. In short, it will begin the _optimization of model weights using backpropagation_.

## 5. Evaluate the model
The model evaluation step helps evaluate the model on the given dataset. The dataset can be a training set or a test set. This step _evaluates the model performance_.

## 6. Make predictions
The model trained on the training dataset can be used to make predictions on the unseen data. In short, given a testing dataset, we can _use the trained model to make predictions_.

Learn about Keras workflow for building a deep learning model.

✨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

Keras Workflow

Steps for building a deep learning model

1. Read and explore the data