This repository contains the work and assignments done during the Deep Learning Specialization on Coursera. It also contains my notes, which are complemented with the NYU-DLSP21 Deep Learning Course from Alfredo Canziani. Instructor: Andrew Ng
- 1 - C1 - Neural Networks and DeepLearning
- 2 - C2 - Improving Deep Neural Networks:Hyperparameter Tuning, Regularization and Optimization
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
You can verify it here
