Deep Learning Specialization on Coursera

These five courses build 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.

Course 1 : Neural Network And Deep Learning

You will learn the foundations of deep learning and will be able to apply it to your own application. Also you will:

• Understand the major technology trends driving Deep Learning
• Be able to build, train and apply fully connected deep neural networks
• Know how to implement efficient (vectorized) neural networks
• Understand the key parameters in a neural network's architecture

Course 2 : Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

You will get to learn "magic" of getting deep learning work well. You will understand what drives performance of the deep model, and be able to more systematically get good results. You will,# Understand industry best-practices for building deep learning applications. Also you will:

• Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
• Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
• Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
• Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization

Course 3 : 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 which provides "industry experience" that you might otherwise get only after years of ML work experience. Also you will:

• Understand how to diagnose errors in a machine learning system, and
• Be able to prioritize the most promising directions for reducing error
• Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human level performance
• Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course.

Course 4 : Convolutional Neural Networks

We will build convolutional neural networks and apply it to image data. Because of deep learning computer vision is working far better. You will build a convolutional neural network, including recent variations such as residual networks. Also you will:

• Understand how to build a convolutional neural network, including recent variations such as residual networks.
• Know how to apply convolutional networks to visual detection and recognition tasks.
• Know to use neural style transfer to generate art.
• Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

Course 5 : Sequence Models

You will build models for natural language, audio, and other sequence data. Also you will:

• Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
• Be able to apply sequence models to natural language problems, including text synthesis.
• Be able to apply sequence models to audio applications, including speech recognition and music synthesis.