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.
After 3 weeks, you will:
- Understand industry best-practices for building deep learning applications.
- 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.
You will need to install the following Packages
- Numpy
- MatplotLib
- h5py
- PIL
- Scipy
- skLearn
- TensorFlow
You can donwload them using pip
pip install numpy h5py Pil scipy sklearn matplotlib
or conda
conda install numpy h5py Pil scipy sklearn matplotlib
In order to install TensorFlow head over to the TensorFlow site and follow the instructions