Deep Learning All Models Explained for Beginners Course in 2026- Udemy 100% Free Course By DETServices
Deep Learning All Models Explained for Beginners
Learn the major Deep Learning architectures in a beginner-friendly and visual way — from ANN and DNN to CNN, RNN, LSTM, GAN, Transformers, GPT, RCNN, YOLO and Face Recognition.
📚 Course Details
- Course: DEEP LEARNING ALL MODELS EXPLAINED FOR BEGINNERS
- Subtitle: Deep Learning All Models Explained for Beginners (CNN, GPT, GAN, DNN, ANN, LSTM, Transformer, RCNN, YOLO)
- Platform: Udemy
- Instructor: ARUNNACHALAM SHANMUGARAAJAN
- Students: 1,000
- Language: English
- Captions: English [Auto]
- Last Updated: October 2025
- Level: Beginner Friendly
🎯 What You Will Learn
- Understand all major Deep Learning models.
- Build a strong conceptual understanding before diving into coding.
- Learn complex Deep Learning architectures using simple visual explanations.
- Understand how modern AI and Deep Learning models work at a conceptual level.
- Learn the architectures behind image classification, object detection, face recognition and text generation.
- Understand modern architectures such as Transformers and GPT.
🧠 Deep Learning Models Covered
1️⃣ Artificial Neural Networks (ANN)
Understand neurons, layers and activation functions. Learn forward propagation, backward propagation and gradient descent.
2️⃣ Deep Neural Networks (DNN)
Explore deeper neural network architectures, vanishing gradients, normalization, dropout and regularization.
3️⃣ Convolutional Neural Networks (CNN)
Learn convolution, pooling, padding and filters. Understand CNNs for image processing and image classification.
4️⃣ RNN & LSTM
Understand how Recurrent Neural Networks process sequential data such as text and time series. Explore LSTM and GRU architectures.
5️⃣ GAN
Learn the Generator and Discriminator architecture and how GANs can generate realistic images and data. Explore DCGAN and CycleGAN.
6️⃣ Transformers
Understand attention and self-attention mechanisms and why Transformers became fundamental to modern NLP and AI systems.
7️⃣ GPT
Learn tokenization, embeddings and GPT training concepts, along with applications such as text generation, coding and chatbots.
8️⃣ RCNN
Learn region-based object detection and explore Fast RCNN, Faster RCNN and Mask RCNN.
9️⃣ YOLO
Understand real-time object detection and how YOLO balances speed and accuracy, including YOLOv8 and YOLOv11 applications.
🔟 Face Recognition
Learn how Deep Learning models detect and recognize faces using embeddings, feature extraction and similarity measures.
🚀 Why Learn Deep Learning?
Deep Learning is at the heart of Artificial Intelligence and powers technologies used in computer vision, Natural Language Processing, robotics, autonomous systems and Generative AI.
This course focuses on understanding the architecture and working principles of major Deep Learning models in an easy-to-understand, step-by-step manner.
📝 Requirements
- Fundamental knowledge of Machine Learning
- No prior Deep Learning experience is required
- Suitable for students, developers, Data Science enthusiasts and AI learners
👨💻 Who Is This Course For?
- Students exploring Artificial Intelligence and Deep Learning
- Developers who want to understand modern AI architectures
- Beginners who want conceptual clarity before coding
- AI and Machine Learning learners interested in modern Deep Learning models
🚀 Start Learning Deep Learning
Understand the architecture behind the most important Deep Learning models — from ANN and CNN to GPT, Transformers, YOLO and modern Generative AI.
Build your conceptual foundation before moving into advanced Deep Learning implementation.
Check the current Udemy offer before enrolling.
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