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

# The `fit` method
The fit step applies backpropagation on your data to update the weights. The main purpose is to show model performance on the training data as the model weights become updated. We can train or fit our model on our training data by calling the `fit()` method.

# Arguments for the `fit` method
The `fit` method takes in the training features `X` and the label `y` as an argument.
 



## Specifying epochs and batch size:

Training of the neural network occurs over epochs and each epoch is split into batches. We can specify `epochs` and `batch_size` arguments within the `fit` method.

- **`epochs`:**

  The `epochs` argument specifies the number of iterations for which we will train the dataset.

- **`batch_size`:**

  The `batch_size` argument specifies the number of dataset rows that are considered before the model weights are updated within each epoch.


> 📝 Note: These configurations can be selected on an experimental basis by hit and trial. We want to train the model so that it is good enough to map rows of input data to output classification. The model output will not always be accurate and the amount of error will be adjusted after a considerable number of iterations for a given model configuration. This is known as _model convergence_.

Learn about model fitting on the training dataset using Keras. 

✨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

Fitting a Model

The `fit` method

Arguments for the `fit` method

Deep Learning in NumPy Exam

Deep Learning in Keras Exam

Fitting a Model

The fit method

Arguments for the fit method

The `fit` method

Arguments for the `fit` method