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

3.50 (28 reviews)
Udemy
platform
English
language
Programming Languages
category
1,282
students
6 hours
content
Jun 2018
last update
$19.99
regular price

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

Description

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.


Content

Introduction

Introduction
Background and Foundation-Neural Networks
Installation 1
Installation 2
Installation 3
Installation 4

ConvNets

Lesson A
Lesson B
Lesson C
Lesson D
Lesson E

GAN

Theory
Lesson A
Lesson B

Example-Image classification using Convolutional Neural Networks in Keras

Lesson A
Lesson B

Word Embeddings

Lesson A
Lesson B
Lesson C
Lesson D
Lesson E
Lesson F

RNN with Keras

Lesson A
Lesson B-Theory
Lesson C
Lesson D

RNN -Cont.

Lesson E-1
Lesson E-2

Exercise section

ex 1

Keras-API

Lesson A
Lesson B
Lesson C
Lesson D
Lesson E
Lesson F

Concepts behind RL

Lecture A

Screenshots

Keras library for deep learning with Machine Learning - Screenshot_01Keras library for deep learning with Machine Learning - Screenshot_02Keras library for deep learning with Machine Learning - Screenshot_03Keras library for deep learning with Machine Learning - Screenshot_04

Reviews

Dean
July 21, 2018
There are far better Machine Learning and Deep Learning courses. From a education point of view, it is good to see different teaching procedures and ways of expressing Machine Learning and Deep Learning techniques. If this is one of your first attempts i feel you'd be suited with similar courses by others. If your looking for additional learning and small snips of extra information this may be worth sifting through.
David
July 15, 2018
Great ground-work content! I started from 0 and learned a lot of cool methods; I feel like I just need to keep practicing. Probably worth a re-watch after a month or two to solidify the basics
Wim
July 5, 2018
i went through the course two times -here is my feedback.... The course is very detailed in itself and very well elaborated. The explanation of each portion is given in slides ,this actually helps me but it might be disliked by others (depends on preference) There is a slight gap in animation and beautification of the slides but that doesn't impact the accurate/appropriate content of the course. It covers a huge portion of machine learning as well in the bonus chapter which is a big add...overall i would recommend this course if you are interested in this topic
Anna
June 30, 2018
the section Example-Image classification using Convolutional Neural Networks in Keras-was really helpful to me .Earlier I was not able to understand but this lecture actually helped me the same.The other helpful thing was the bonus section, it covered entire concepts related to machine learning like RNN etc.A bit of old styled lecture but is complete overall.Good one !
Xueming
June 27, 2018
so far the course is bad, only a few words on the slide, and a few lines of code, feel like he was reading the text book. The instructor said the code can be downloaded in the resource section. But no code can be found anywhere.
Rebecca
May 10, 2018
Unfinished and incomplete. The delivery is dry and boring -- slide after slide with hardly any connection in between. Red background and ugly white cutout figures. The very first lecture is through a PREVIEW of a powerpoint attachment open in the gmail inbox of the instructor -- I'm not joking! Wonder where all the 5 stars are coming from! Even the installation of Keras is explained through slides -- which don't even have the installation command in them! Just the output of the commands! I wouldn't recommend this course if you got it for free!
Julian
May 2, 2018
I don't yet understand whether I'm meant to be following the code examples. The quality of the text on the screen is poor.
Sohail
April 22, 2018
This course is simply awesome. Anyone get an understanding about deep learning within such a short time. This could be a starting point for deep learning enthusiastic persons.It gives perfect overview about deep learning.

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1643242
udemy ID
4/12/2018
course created date
4/19/2020
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