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DEEP LEARNING ALL MODELS EXPLAINED FOR BEGINNERS
Published 10/2025
Duration: 31m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 263.92 MB
Genre: eLearning | Language: English
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.
Deep Learning is the heart of Artificial Intelligence, and mastering it opens doors toMachine 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
Who this course is for:
- Students exploring Artificial Intelligence and Deep Learning
- Developers aiming to understand modern AI architectures
More Info
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RapidGator
NitroFlare
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