DEEP LEARNING ALL MODELS EXPLAINED FOR BEGINNERS

Deep Learning All Models Explained for Beginners (CNN, GPT, GAN, DNN, ANN, LSTM, Transformer, RCNN, YOLO )

Description

Welcome to “Deep Learning All Models Explained for Beginners” — your ultimate guide to understanding the foundation and architecture of the most powerful AI and Deep Learning models used in the world today.

This beginner-friendly course is designed for students, data science enthusiasts, and AI learners who want to truly understand how modern deep learning architectures work. Whether you want to build image classifiers, detect objects, generate realistic images, recognize faces, or understand large language models like GPT, this course gives you the clarity and practical understanding you need.

Deep Learning is the heart of Artificial Intelligence, and mastering it opens doors to Machine Vision, NLP, Robotics, Autonomous Systems, and Generative AI. This course walks you through all the major deep learning models in an easy-to-understand, step-by-step manner.

1. Artificial Neural Networks (ANN):

• Understand the structure and working of neurons, layers, and activations

• Learn forward and backward propagation

• Understand gradient descent and how networks learn

2. Deep Neural Networks (DNN):

• Explore deeper architectures for complex tasks

• Understand vanishing gradients and optimization techniques

• Learn about normalization, dropout, and regularization

3. Convolutional Neural Networks (CNN):

• Master image processing and computer vision fundamentals

• Understand convolution, pooling, padding, and filters

• Build a CNN for image classification

4. Recurrent Neural Networks (RNN) and LSTM:

• Learn how RNNs process sequential data like text or time series

• Understand vanishing gradient problems

• Explore LSTM (Long Short-Term Memory) and GRU architectures

5. Generative Adversarial Networks (GAN):

• Learn the architecture of Generator and Discriminator

• Understand how GANs generate realistic images and data

• Explore popular variants like DCGAN and CycleGAN

6. Transformers:

• Understand the attention mechanism and self-attention

• Learn how Transformers revolutionized NLP and AI

• Explore the architecture used in GPT, BERT, and modern LLMs

7. GPT (Generative Pre-Trained Transformer):

• Learn how GPT models understand and generate human-like text

• Understand tokenization, embeddings, and training methodology

• Explore use cases in text generation, coding, and chatbots

8. RCNN (Region-Based CNN):

• Learn object detection concepts and how RCNN locates multiple objects

• Explore Fast RCNN, Faster RCNN, and Mask RCNN

• Understand bounding boxes and region proposals

9. YOLO (You Only Look Once):

• Understand real-time object detection

• Learn the YOLO architecture and how it’s optimized for speed and accuracy

• Explore YOLOv8/YOLOv11 applications in tracking and surveillance

10. Face Recognition Using Deep Learning:

• Learn how deep learning models detect and recognize faces

• Understand embeddings, feature extraction, and similarity measures

• Build a basic face recognition pipeline

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