Duration
Deep Learning with Laravel
5 days
Overview
In recent years, deep learning has revolutionized various industries by providing solutions to complex problems such as image recognition, natural language processing, and predictive analytics. As developers, integrating these powerful machine learning capabilities into our applications can significantly enhance their functionality and user experience.
This course aims to bridge the gap between deep learning and web development by leveraging the capabilities of Laravel, a robust PHP framework, and Rubix ML, a comprehensive machine learning library for PHP.
Course Objectives
By the end of this course, you will be able to:
Understand the Basics of Laravel:
Get familiar with the Laravel framework, its features, and how it can be used to build modern web applications.
Learn the Fundamentals of Rubix ML:
Gain insights into Rubix ML, a powerful machine learning library for PHP, and
understand how to use it for building and training machine learning models.
Develop and Train a Deep Learning Model:
Learn how to create, train, and save a deep learning model using Rubix ML.
Integrate Machine Learning Models with Laravel:
Implement API endpoints in Laravel to handle predictions and integrate the trained
deep learning model into your Laravel application.
Prerequisite
Understanding of basic Laravel Framework. Sound understanding of AI/ML and Web Programming.
Module 1: Introduction to Laravel
- Overview of Laravel
- Introduction to Laravel framework
- Key features and benefits of using Laravel
- Setting up a Laravel development environment
- Introduction to Rubix ML
- What is Rubix ML?
- Key features of Rubix ML
- Installing Rubix ML in a Laravel project
Module 2: Setting Up the Environment
- Installing Laravel
- Step-by-step guide to installing Laravel via Composer
- Creating a new Laravel project
- Basic directory structure of a Laravel application
- Installing Rubix ML
- Installing Rubix ML using Composer
- Overview of Rubix ML dependencies and their installation
Module 3: Data Preparation
- Understanding the Dataset
- Types of datasets suitable for deep learning
- Formatting and structuring data for Rubix ML
- Loading and Preprocessing Data
- Loading datasets using Rubix ML’s data extractors
- Data preprocessing techniques (e.g., normalization, handling missing values)
- Practical examples of data preprocessing in PHP
Module 4: Building the Deep Learning Model
- Introduction to Neural Networks
- Basic concepts of neural networks
- Overview of layers, activation functions, and optimization algorithms
- Implementing the Model
- Setting up a feed-forward neural network using Rubix ML
- Defining layers, activation functions, and cost functions
- Training the model with a sample dataset
- Saving and Loading Models
- Persisting trained models using Rubix ML’s persisters
- Saving models to the filesystem
- Loading models for inference
Module 5: Integrating Deep Learning with Laravel
- Creating the Model Training Script
- Writing a PHP script to train the deep learning model
- Command-line execution of the training script
- Best practices for organizing machine learning scripts
- Creating a Prediction Controller
- Setting up a Laravel controller for handling predictions
- Loading the trained model within the controller
- Making predictions based on user input
- Defining API Routes
- Setting up API routes in Laravel for prediction requests
- Handling HTTP POST requests with JSON payloads
- Returning prediction results as JSON responses
Module 6: Testing and Deployment
- Testing the API
- Tools for testing RESTful APIs (e.g., Postman, CURL)
- Writing unit tests for the prediction endpoint
- Best practices for testing machine learning APIs
- Case Study
- Case studies of deep learning applications with Laravel
- Integrating other machine learning models and techniques
- Future trends in PHP-based machine learning
- Conclusion
- Q&A session and feedback collection
Programme scope, delivery arrangements and suitability are confirmed during enquiry.