The building blocks
Describe what a neural network is made of and why that structure can solve complex problems.






























A four-hour introduction to deep learning. It starts with the building blocks of neural networks and the mathematical principles of training, including the optimization algorithms deep learning relies on.
It then turns to the problems that appear in practice, such as overfitting and generalization, and to sequence modeling with RNNs and LSTMs for text, time series and audio. A final module covers common interview questions.
Describe what a neural network is made of and why that structure can solve complex problems.
Follow the mathematics behind training and use the core optimization algorithms of deep learning.
Recognise common training challenges and apply techniques that prevent overfitting and support good generalization.
Use RNNs and LSTMs to work with sequential data such as text, time series and audio.
What deep learning is within artificial intelligence, and the building blocks of the neural networks that solve complex problems.
How a neural network learns, the mathematical principles behind training, and the main optimization algorithms in deep learning.
Techniques for building accurate networks, addressing common training challenges and preventing overfitting so that models generalize.
Recurrent neural networks and long short-term memory networks for sequential data such as text, time series and audio.
A closing module on the essential concepts and the common questions that come up in deep learning interviews.

Instructor · LUNARTECH
Tatev brings industry experience, advanced AI/ML degrees and a reputation as a thought leader in data science to LUNARTECH. She holds Bachelor’s and Master’s degrees in STEM, and her publications in top journals and presentations at international conferences showcase her expertise.
Start with the building blocks, then the mathematics and optimization behind training.
Recognise common training challenges, then use techniques that prevent overfitting.
Apply RNNs and LSTMs to text, time series and audio.
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Every role uses all five modules. Here’s where each one leans hardest, and where its work ends up.
Trains neural networks and diagnoses overfitting. Picks the optimizer that suits the problem.
Training, Performance
Machine learning teams
Brings neural networks into analysis work. Checks that a model generalizes beyond its training data.
Foundations, Performance
Machine learning teams, Time-series analysis
Starts from the mathematics of how networks learn. Reads new papers against the basics.
Foundations, Training
Research
Models text and audio as sequences. Chooses between an RNN and an LSTM.
Sequences, Training
Language and text, Audio
Trains neural networks and diagnoses overfitting. Picks the optimizer that suits the problem.
Over 10,000 students trust LUNARTECH
Understand systems all the way down. Build the ones others rely on.
Understand what the data says. Build models that hold up.
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Updated November 2026.
Need help? These answers cover most of what people ask before they start.
Yes. One module covers recurrent neural networks and LSTMs for sequential data such as text, time series and audio.
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Four hours from the building blocks of deep learning to sequence models and common interview questions. Begin with a free lesson, or enroll in the full course.