Deep Learning Specialization Course Notes
Deep Learning Specialization (overview 5 Courses)
Note: These are my personal notes which I prepared during deep learning specialization taught by AI guru Andrew NG. I have used diagrams and code snippets from the course videos whenever needed but following The Honor Code. Please note that most of the places I am not following the exact mathematical symbol and notation, instead just using plain English name this is for just to save some time, also please note that this is a personal diary made during course and I guess a bit lengthy too, and in any form doesn't replace the content and learning process one follows during course including quiz, programming assignments, project etc. It is a great course and I encourage you to take it.
What you will learn at the end of the specialization:
Neural Networks and Deep Learning: This course gives foundations of neural networks and deep learning. How to build and train. At the end of this course, we'll in position to recognize cat so will make a cat recognizer.
Improving Deep Neural Networks - Hyperparameter Tuning, Regularization and Optimization: Hyperparameter Tuning, Regularization and Optimization: In this course, we'll learn about practical aspects of the NN. Now you have made NN/deep network so the focus is on how to make it perform well. We'll fine tune various things like hyperparamater tuning, regularization algorithms and optimization algorithms like RMSProp, Adam etc. So this course helps greatly in making model perform well.
Structuring your Machine Learning Project: In this course, we'll learn how to structure machine leaning projects. It is observed that strategy for machine learning projects has been changed a lot in deep learning era. For example the way you divide data in train/test/dev set has been changed in era of deep learning also whether train and test data comes from the same distributions etc.? we'll also learn about end-to-end deep learning. The material in this course is relatively unique.
Convolutional Neural Networks(CNN): CNN is often applied in images mainly in computer vision problems. In this course, we'll learn about how to make these models using CNN's.
Natural Language Processing-Building Sequence Models: In this course, we'll learn about algorithms like Recurrent Neural Network (RNN's), LSTM (Long Short-Term Memory) and learn how to apply them with the sequence of data like natural language processing, speech recognition, music generation etc.
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Deep learning Specialization completion certificate: https://www.coursera.org/account/accomplishments/specialization/WVPVCUMH94YS
Deep Learning Specialization: https://www.deeplearning.ai/