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Keras library for deep learning with Machine Learning
An expert level Practical Guide to Tuning Deep Learning Models with Keras for Data Scientists and ML in detail
This Course Includes
udemy
3.7 (28 reviews )
6 total hours 
english
Online - Self Paced
course
Udemy
About Keras library for deep learning with Machine Learning
Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deep learning Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible.
This course provides a comprehensive expert level details in deep learning(Keras). We start by a brief recap of the most common concepts found in machine learning. Then, we introduce neural networks and the optimization techniques to train them. We’ll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, we present two types of neural architecture: convolutional and recurrent neural networks
In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras -- one of the easiest and most powerful machine learning tools out there.
In this course we will get started with Keras, where we'll compare with TensorFlow to make it easier to understand, and to build your knowledge upon itself. By connecting new information with existing knowledge, you'll form stronger connections in your brain on all of this valuable tech content. You'll learn where and how to use Keras. By the end of this course you'll have such a solid grasp you can add all of these technologies as qualifications on your resume, LinkedIn profile, or personal website.
Also we will learn to build a basic image recognition model and much much more.
What You Will Learn?
- You'll be able to build deep learning models using Keras.
- You'll learn how to evaluate the performance of neural networks built using Keras.
- You'll understand how to tune Keras layers on different network topologies..
- Knowledge of code of several neural networks from the ground up in Python using Keras.
- To distinguish which practical applications can benefit from deep learning.
- To train and run models in the cloud using a GPU.
- To build, train and use fully connected, convolutional and recurrent neural networks.
- To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
- To install and use Python and Keras to build deep learning models.
- To understand and code Convolutional Neural Networks as well as graph-based deep models involving residual connections and inception modules.
- To build a simple image recognition project using the CIFAR-10 library.
- To build Handwritten digit recognition with advanced MNIST.
- To Build Image recognition with CIFAR-100.