Understand and build Deep Learning models for images, text and more using Python and Keras
What you'll learn
- To describe what Deep Learning is in a simple yet accurate way
- To explain how deep learning can be used to build predictive models
- To distinguish which practical applications can benefit from deep learning
- To install and use Python and Keras to build deep learning models
- To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
- To build, train and use fully connected, convolutional and recurrent neural networks
- To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
- To train and run models in the cloud using a GPU
- To estimate training costs for large models
- To re-use pre-trained models to shortcut training time and cost (transfer learning)
- Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators)
- Use of bash shell (or equivalent command prompt) and basic commands to copy and move files
- Basic knowledge of linear algebra (what is a vector, what is a matrix, how to calculate dot product)
- Use of ssh to connect to a cloud computer
This Deep Learning with Python and Keras course is created to supply a total intro to Deep Learning. It is targeted at newbies and intermediate developers and data scientists who recognize with Python and wish to comprehend and use Deep Learning methods to a range of issues.
We begin with an evaluation of Deep Learning applications and a wrap-up of Machine Learning tools and methods. We present Artificial Neural Networks and describe how they are trained to resolve Regression and Classification issues.
Over the remainder of the Deep Learning with Python and Keras course we present and describe numerous architectures consisting of Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we discuss both the theory and offer a lot of example applications.
This course is an excellent balance in between theory and practice. We do not avoid describing mathematical information and at the same time we supply workouts and sample code to use what you've simply found out.
The objective is to offer trainees with a strong structure, not simply theory, not simply scripting, however both. At the end of the Deep Learning with Python and Keras course you'll have the ability to acknowledge which issues can be fixed with Deep Learning, you'll have the ability to create and train a range of Neural Network models and you'll have the ability to utilize cloud computing to accelerate training and enhance your model's efficiency.
Who this course is for:
- Software engineers who wonder about data science and about the Deep Learning buzz and wish to get a much better understanding of it
- Data scientists who recognize with Machine Learning and wish to establish a strong fundamental understanding of deep learning
Created by Data Weekends, Jose Portilla, Francesco Mosconi
Last updated 6/2018
Size: 1.47 GB