Sep 21, 2020

Deep Learning - Coursera

After I finished the Machine Learning and I was so excited about all the new algorithms that I learn in this course I took the Deep Learning Specialization, a series of 5 courses that will cover almost all the algorithms, models and best practices from the current machine learning current literature.

In these five courses, I learned the foundations of Deep Learning, understood how to build neural networks, and learned how to lead successful machine learning projects. I learned about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. The corses have practical examples and assignments in Python and in TensorFlow.

The 5 courses are:

  1. Neural Networks and Deep Learning

In this course, you learn the foundations of deep learning. You will build, train and apply fully connected deep neural networks, you will know how to implement efficient (vectorized) neural networks, you will understand the key parameters in a neural network’s architecture

  1. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

This course will teach you the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.

  1. Structuring Machine Learning Projects

You will learn how to build a successful machine learning project. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. This provides “industry experience” that you might otherwise get only after years of ML work experience.

  1. Convolutional Neural Networks

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just a few years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

  1. Sequence Models

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working really good, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

It is not much to say more, I enjoyed all the courses and I recommend this specialization to anyone.

Note: I copied all Jupyter Notebooks from the class and I am still review them. Be aware that the labs are not accessible anymore after you finish your subscription.