Design modern neural networks - You will learn how to build and train deep learning models from the ground up, including tensors, gradients and backpropagation, so you can design effective architectures on your own.
Code in PyTorch and TensorFlow - You will turn theory into working solutions by implementing models in PyTorch and TensorFlow on business-driven examples, helping you deliver practical AI solutions much faster.
Build computer vision systems - You will work with CNNs, transfer learning, YOLO and Faster R-CNN, which will let you create quality control and automated inspection systems for real industrial use cases.
Analyze sequential data - You will master RNN, LSTM and GRU models for sequences and time series, enabling you to forecast market trends, demand shifts and failure signals directly from data.
Deploy language models - You will understand the Transformer architecture and work with BERT and GPT, then fine-tune models for specific tasks to automate text analysis and document-heavy workflows.
Create Generative AI solutions - You will use GANs and VAEs in practice, learning how to generate and edit images for design and production scenarios instead of relying only on ready-made applications.
Work with production-ready ML - You will learn how to track experiments, deploy models, monitor quality and update models after release, so your deep learning solutions stay reliable in production settings.
Improve performance and cost - You will apply training acceleration, quantization, model compression and distributed training, so you can run deep learning efficiently both in the cloud and on edge devices.
Practical Python - You should be comfortable writing Python scripts, using functions, loops and common libraries, and understanding code structure so you can complete the hands-on exercises.
Machine learning basics - You should know core machine learning concepts such as training, validation, overfitting and evaluation metrics so you can move into deep learning without confusion.
Math for models - You should understand linear algebra and basic calculus, including vectors, matrices and derivatives, because they are needed to work with gradients and tensors.
Data handling - You should have experience preparing data for analysis, cleaning it and splitting it into datasets so you can focus on modeling instead of basic preprocessing work.
