Deep Learning

Many factors influenced the rise of AI and the launch of the fourth technological revolution. However, one primary invention that accelerated the process was the ability to transform images into information. This breakthrough paved the way for transforming videos, texts, and audio into information, resulting in advancements such as driverless cars, bots, and automation that almost match human abilities. This workshop focuses on the algorithms behind this technological breakthrough, making AI a reality and allowing you to apply deep learning and Sequential Deep Learning algorithms to solve new, challenging problems. 

• Learn the mathematics behind Deep Learning.
• Explore the logic of optimization with Gradient Descent.
• Dissect components of neural networks.
• Adjust hyperparameters of algorithms to optimize cost functions.
• Explore the architecture of main deep learning networks.
• Improve the accuracy of Classification and Estimation.
• Establish knowledge in:

                    - Image classification

                    - Face recognition, and

                    - Object detection. 

• Apply Sentiment Analysis.

Day 1:

- Algebra and Calculus 

Day 2: 

- Gradient Descent

- Perceptron Algorithm

Day 3:

- Feedforward Neural Networks

Day 4: 

- Convolutional Neural Networks

Day 5:

- Recurrent Neural Networks 

- LSTM and GRU

- Comprehensive colored PPT booklet.

- Neurons, Hidden layers, Synapsis, ...

- Weights, Scores, ...

- Activation functions: Sigmoid, TanH, ...

- SoftMax rule

- Feed Forward of information

- Backpropagation

- Convolution windows, MaxReLu, ...

- TensorFlow coding applications 

This hands-on workshop dives deep into the rapidly evolving field of Generative and Sequential Deep Learning, focusing on the theory, applications, and practical implementation of models that can autonomously create data. Participants will explore how generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models work, and how they transform industries, from art and media to healthcare and business analytics. It will also delve into Recurrent networks and their LSTM-empowered alternatives. This workshop is designed for data scientists, machine learning engineers, AI enthusiasts, and developers who have a basic understanding of deep learning and want to expand their expertise in generative models. Attendees should have experience with Python and be familiar with common machine learning frameworks like TensorFlow.

• Gain a solid understanding of generative deep learning models' fundamental concepts and theories, including GANs, VAEs, and RNN and LSTM sequential models.
• Learn how to build and utilize these generative models effectively.
• Master the techniques for training generative models, covering loss functions, optimization strategies, and stability issues.
• Explore practical applications of generative models, such as image synthesis and text generation.
• Develop the skills to identify and troubleshoot common issues encountered during the training of generative models.
• Stay informed about the latest advancements and trends in generative deep learning research.

Day 1:

- Recurrent Neural Networks 

- LSTM and GRU

Day 2:

-  Autoencoders.

Day 3: 

-  Variational Autoencoders (VAE).

Day 4:

- Generative Adversarial Networks (GAN).

- Comprehensive colored PPT booklet.

- Cell state: forget, convey, ...

- Encoders and decoders

- Latent Space

- Auto Encoders Vs. Variational Auto Encoders

- Generators vs. Discriminators

- Objective Functions / Mode Collapse / Training approaches

This intensive 5-day training program offers hands-on experience in using Python for object and face detection.

Participants will learn detection techniques, explore deep learning models, and implement detection systems with popular libraries.

By the end of the training, participants will have a solid foundation in object and face detection, which will empower them to create their own AI-powered applications.

• Understand the fundamentals of object and face detection, including both traditional methods and deep learning-based approaches.
• Explore popular libraries like OpenCV, Dlib and deep learning frameworks like TensorFlow and PyTorch.
• Implement real-world applications through hands-on projects focusing on detecting objects and faces in images and videos.
• Optimize detection performance by applying techniques that enhance the accuracy and efficiency of detection models.
• Deploy detection models by learning how to integrate these systems into real-world applications.

Day 1:

-  Introduction to Histogram Models and Image Processing Techniques.

Day 2:

-  Exploring Libraries like OpenCV, YOLOv8, and Dlib.

Day 3: 

-  Face Recognition.

Day 4:

-  Flask App and APIs with Web App Development.

Day 5:

-  Working with Live Camera Frames and Real-Time Detections.

- Understanding digital images, including pixels, color spaces, formats, and histogram models for intensity distribution.

- Applying image filtering techniques, such as blurring, sharpening, and edge detection, with hands-on implementation using OpenCV.

- Practical object and face detection implementation using OpenCV, YOLOv8, and Dlib.

- Introduction to Flask: Creating RESTful APIs for AI applications.

- Integrating object and face detection models with Flask APIs.

- Basics of frontend development: Displaying detection results in a web application.

- Processing video streams for real-time object and face detection applications.