Deep Learning Training
Deep Learning powers the breakthroughs of the modern AI era. This advanced course covers neural network theory, CNNs for vision, RNNs/LSTMs for sequences, transformers for language and GANs/diffusion for generation. You will train large models on cloud GPUs and finish with a polished Deep Learning portfolio.
Deep Learning powers the breakthroughs of the modern AI era. This advanced course covers neural network theory, CNNs for vision, RNNs/LSTMs for sequences, transformers for language and GANs/diffusion for generation. You will train large models on cloud GPUs and finish with a polished Deep Learning portfolio.
Syllabus
- Neural Network Foundations
- Optimization & Regularization
- Convolutional Neural Networks
- RNNs / LSTMs / GRUs
- Transformers & Attention
- GANs & Diffusion Models
- Transfer Learning
- GPU & Cloud Training
What you will learn
- Train CNNs and transformers from scratch
- Fine-tune models with transfer learning
- Build a generative model end-to-end
- Deploy DL models to production
Tools you will use
- TensorFlow
- PyTorch
- Keras
- Hugging Face
- CUDA / Colab Pro
Projects
- Object detection on custom dataset
- Sentiment analysis with transformers
- Image generation with diffusion
- Speech recognition mini-project
Career paths
- Deep Learning Engineer
- Computer Vision Engineer
- NLP Engineer
- Research Engineer
Who should join
- ML practitioners
- Research aspirants
- Engineers building advanced AI products
Prerequisites
- Basic Python helpful (we cover from scratch)
- High-school math
- Curiosity for problem-solving
Certificate
AI specialization certificate with deployed real-world AI projects in your portfolio.
Syllabus
- Neural Network Foundations
- Optimization & Regularization
- Convolutional Neural Networks
- RNNs / LSTMs / GRUs
- Transformers & Attention
- GANs & Diffusion Models
- Transfer Learning
- GPU & Cloud Training
Learning outcomes
- Train CNNs and transformers from scratch
- Fine-tune models with transfer learning
- Build a generative model end-to-end
- Deploy DL models to production
Tools
- TensorFlow
- PyTorch
- Keras
- Hugging Face
- CUDA / Colab Pro
Projects
- Object detection on custom dataset
- Sentiment analysis with transformers
- Image generation with diffusion
- Speech recognition mini-project
Career paths
- Deep Learning Engineer
- Computer Vision Engineer
- NLP Engineer
- Research Engineer
Who should join
- ML practitioners
- Research aspirants
- Engineers building advanced AI products
Prerequisites
- Basic Python helpful (we cover from scratch)
- High-school math
- Curiosity for problem-solving
Frequently asked questions
Do I need my own GPU?
No — we provide cloud GPU access (Colab Pro) for the entire training period.
What will I be able to do by the end?
By the end you can: Train CNNs and transformers from scratch; Fine-tune models with transfer learning; Build a generative model end-to-end; Deploy DL models to production.
Who is this course designed for?
It is designed for: ML practitioners; Research aspirants; Engineers building advanced AI products.