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.
Neural network architecture, tensors, gradients, and backpropagation provide the technical base for the rest of the program. Participants implement models in PyTorch and TensorFlow, apply optimization and regularization on real datasets, and build a sales prediction model tied to a business scenario.
Computer vision applications in industry rely on models that can classify, localize, and inspect visual inputs with consistency. The section uses quality control in automotive as a case study, covers transfer learning with ResNet and EfficientNet, and applies YOLO and Faster R-CNN in an inspection project.
When data arrives as ordered observations, sequence modeling becomes the central problem. This section works through RNN concepts and the practical use of LSTM and GRU, then applies them to time series analysis and market trend prediction where temporal dependencies affect results.
Transformer-based systems have reshaped language processing and many automation scenarios. Participants examine the Transformer architecture, review BERT, GPT, and newer models, and work through fine-tuning strategies for task-specific use in process automation and enterprise workflows.
Generative models introduce a different class of deep learning tasks centered on content creation. The section compares GAN and VAE approaches, then moves to image generation and editing workflows, ending with how these methods are applied in design and production contexts.
From experimentation to production, deep learning requires disciplined operational processes. The focus here is on experiment tracking, deployment pipelines, model monitoring and updates, along with methods for controlling infrastructure spend without weakening reliability or delivery speed.
Training efficiency becomes critical when models grow and deployment targets vary. This section covers acceleration techniques, quantization and compression, distributed training, and adaptation to edge devices so that deep learning systems meet runtime, memory, and throughput requirements.
Business use cases anchor this part of the training in concrete implementation work. Participants build solutions for product recognition, customer sentiment analysis, machine failure prediction, and document workflow automation, with attention to data flow, model selection, and operational constraints.
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.
