Advanced Deep Learning Architectures Training Course

The course assumes participants already understand how a basic neural network is trained. It is aimed at data scientists, machine learning engineers, AI specialists, developers and researchers who have trained simple models and now want to work with deep architectures. Anyone new to neural networks is advised to start with the Neural Networks Training Course, which builds those foundations first. Day 1 then reviews what makes a network deep before the architecture days begin.

Day 1 explains what makes a network deep, including normalization, residual connections and modern optimizers. Day 2 is devoted to convolutional neural networks for image classification, object detection and segmentation, and Day 3 to recurrent networks with LSTM and GRU cells for text, time series and sensor data. Day 4 covers embeddings, attention, transformers and pre-trained and foundation models, and Day 5 introduces autoencoders, GANs, diffusion models and large language models at a conceptual level.

The two are designed as a sequence. The Neural Networks Training Course explains how networks learn, from neurons and activation functions through loss functions, gradient descent, backpropagation and regularization, ending with a network built and trained end to end. This course starts where that one stops: it assumes those basics and concentrates on deep architectures, namely CNNs, RNNs and LSTM, attention and transformers, transfer learning, generative models and deployment. Newcomers to neural networks should take the Neural Networks course first.

Each day pairs explanation with practice. Participants map business problems to deep learning approaches on Day 1, train a CNN for an image classification task on Day 2, forecast a time series with an LSTM network on Day 3, and fine-tune a pre-trained model for a text or image task on Day 4. The final workshop asks them to select, justify and plan the deployment of a deep learning architecture for a real business case.

Day 5 weighs architectures against data, accuracy, cost and latency trade-offs, so participants can justify why a CNN, sequence model, transformer or pre-trained model fits a given business problem and budget. It then covers deploying deep models through packaging, serving and monitoring, including model drift, and the risks of bias, limited explainability and data privacy. Participants leave able to outline a deployment plan and propose suitable controls for those risks.

The course is particularly relevant to government entities, banks, oil and gas and energy companies, manufacturers, healthcare providers, telecommunications operators, logistics providers and consulting firms whose data includes images, documents, free text, speech or sensor streams. Examples include visual inspection, document processing, forecasting and customer service. Technical leads, AI product owners, innovation managers and IT managers who choose between deep learning approaches or oversee their deployment also benefit from its focus on architecture choice.