Learning Deep Learning All Models Explained For Beginners

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Free Download Deep Learning All Models Explained For Beginners
Published 10/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 263.92 MB | Duration: 0h 31m
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 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 activationsLearn forward and backward propagationUnderstand gradient descent and how networks learn2. Deep Neural Networks (DNN):Explore deeper architectures for complex tasksUnderstand vanishing gradients and optimization techniquesLearn about normalization, dropout, and regularization3. Convolutional Neural Networks (CNN):Master image processing and computer vision fundamentalsUnderstand convolution, pooling, padding, and filtersBuild a CNN for image classification4. Recurrent Neural Networks (RNN) and LSTM:Learn how RNNs process sequential data like text or time seriesUnderstand vanishing gradient problemsExplore LSTM (Long Short-Term Memory) and GRU architectures5. Generative Adversarial Networks (GAN):Learn the architecture of Generator and DiscriminatorUnderstand how GANs generate realistic images and dataExplore popular variants like DCGAN and CycleGAN6. Transformers:Understand the attention mechanism and self-attentionLearn how Transformers revolutionized NLP and AIExplore the architecture used in GPT, BERT, and modern LLMs7. GPT (Generative Pre-Trained Transformer):Learn how GPT models understand and generate human-like textUnderstand tokenization, embeddings, and training methodologyExplore use cases in text generation, coding, and chatbots8. RCNN (Region-Based CNN):Learn object detection concepts and how RCNN locates multiple objectsExplore Fast RCNN, Faster RCNN, and Mask RCNNUnderstand bounding boxes and region proposals9. YOLO (You Only Look Once):Understand real-time object detectionLearn the YOLO architecture and how it's optimized for speed and accuracyExplore YOLOv8/YOLOv11 applications in tracking and surveillance10. Face Recognition Using Deep Learning:Learn how deep learning models detect and recognize facesUnderstand embeddings, feature extraction, and similarity measuresBuild a basic face recognition pipeline
Students exploring Artificial Intelligence and Deep Learning,Developers aiming to understand modern AI architectures
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