Deep Learning All Models Explained For Beginners

DEEP LEARNING ALL MODELS EXPLAINED FOR BEGINNERS
Published 10/2025
Duration: 31m | .MP4 1920×1080 30 fps(r) | AAC, 44100 Hz, 2ch | 263.92 MB

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

What you’ll learn
– All major deep learning models
– Gain a solid conceptual understanding before diving into coding
– Designed for absolute beginners – no prior deep learning experience required
– Explains complex architectures in simple visual terms

Requirements
– Fundamental knowledge of machine learning

Description
Welcome to”Deep Learning All Models Explained for Beginners”- your ultimate guide to understanding the foundation and architecture of the most powerfulAI and Deep Learning modelsused in the world today.

This beginner-friendly course is designed forstudents, data science enthusiasts, and AI learnerswho want to truly understand how modern deep learning architectures work. Whether you want tobuild 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.

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

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

Who this course is for:
– Developers aiming to understand modern AI architectures
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