Description

Master Deep Learning from the ground up and build modern AI applications using TensorFlow, PyTorch, Transformers, Large Language Models (LLMs), and Generative AI. This comprehensive, project-based program takes you from neural network fundamentals to cutting-edge technologies like GPT, Diffusion Models, Hugging Face, and MLOps. Learn through real-world projects and deploy production-ready deep learning applications.

What You’ll Learn

✔ Understand the foundations of Deep Learning and Neural Networks.

✔ Master Linear Algebra, Calculus, Probability, and Gradient Descent for AI.

✔ Build Neural Networks from scratch using forward and backpropagation.

✔ Develop Deep Learning models using TensorFlow & Keras.

✔ Train custom models using PyTorch and Autograd.

✔ Build powerful Computer Vision models using CNNs and ResNet.

✔ Solve sequence problems using RNN, LSTM, and GRU.

✔ Understand Transformers, Self-Attention, and modern NLP architectures.

✔ Build AI applications using GPT, BERT, T5, LLaMA, Prompt Engineering, LoRA, and RAG.

✔ Create Generative AI applications using GANs, VAEs, Autoencoders, and Diffusion Models.

✔ Learn Transfer Learning, Vision Transformers (ViT), Graph Neural Networks (GNN), and Explainable AI.

✔ Deploy Deep Learning applications using Streamlit, Gradio, Hugging Face, Docker, ONNX, and MLOps.

This course emphasizes hands-on implementation through practical projects including image classification, sentiment analysis, translation systems, chatbot development, image generation, transfer learning, and complete production-ready deployment. Students gain industry-ready experience with modern Deep Learning frameworks and AI tools.