My specialization “Neural Networks and Deep Learning” (deeplearning.ai) has been a fascinating deep dive into Artificial Intelligence. Speech recognition, music synthesis, machine translation, natural language processing, car detection and image segmentation (for autonomous driving) are among the applications in the Python Coding Labs. After 17 inspiring lectures (1-2h each), 17 Quizzes, 25 Coding Labs (3+ hours each: Python, including NumPy, TensorFlow, Keras API), I earned my certification in these courses:
1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Networks
5. Sequence Models (including Transformer Architecture)
These courses (Coursera.com) meet the highest standards of academic teaching. Andrew Ng is a deeply knowledgeable and motivating lecturer. AI real-life applications and powerful layout of Python-Notebooks (.ipynb) make the coding labs very appealing. As a mathematician, I am intrigued by how linear algebra is applied to optimize the performance of neural networks through tensor calculations instead of nested loops (allowing for parallel processing). Overall, this has been a steep learning curve into the world of AI that absolutely was worth the effort!
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