[100% Off ] Deep Learning with Keras

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Deep Learning with Keras 

Deep
Learning & Keras concepts, model, layers, modules. Build a Neural Network
and Image Classification Model with Keras




About
the Course




Keras is an open-source
library of neural network components written in Python. Keras is capable of
running atop TensorFlow, Theano, PlaidML and others. The library was developed
to be modular and user-friendly. Keras enables fast experimentation through a
high level, user-friendly, modular and extensible API. Keras can also be run on
both CPU and GPU. Keras was developed and is maintained by Francois Chollet and
is part of the TensorFlow core, which makes it TensorFlow preferred high-level
API.

Comprised
of a library of commonly used machine learning components including objectives,
activation functions, and optimizers, Keras’ open-source platform also offers
support for recurrent and convolutional neural networks. Additionally, Keras
offers mobile platform development for users intending to implement deep
learning models on smartphones, both iOS and Android.

Keras
is essentially an API designed for machine learning and deep learning engineers
and follows best practices for reducing cognitive load. Keras offers consistent
& simple APIs, minimizes the number of user actions required for common use
cases, and provides clear & actionable error messages. It also supports
extensive documentation and developer guides.




It
is made user-friendly, extensible, and modular for facilitating faster
experimentation with deep neural networks. It not only supports Convolutional
Networks and Recurrent Networks individually but also their combination

 

Why do we need Machine
Learning libraries such as Keras?

Machine
learning uses a variety of math models and calculations to answer specific
questions about data. Examples of machine learning in action include detecting
spam emails, determining certain objects using computer vision, recognizing
speech, recommending products, and even predicting commodities values years in
the future.

The
calculations implicit in machine learning and deep learning are very
complicated to set up to ensure correct output (answers). A variety of machine
learning libraries have emerged to help navigate these complexities. With these
options, new folks can start getting into data science easily. Some of the most
popular machine learning libraries include:

·        
TensorFlow

·        
Keras

·        
sciKit learn

·        
Theano

·        
Microsoft Cognitive Toolkit (CNTK)

 

Uplatz provides this
comprehensive course on Deep Learning with Keras. This Keras course will help
you implement deep learning in Python, preprocess your data, model, build,
evaluate and optimize neural networks. The Keras training will teach you how to
use Keras, a neural network API written in Python. This Keras course will show
how the full implementation is done in code using Keras and Python. You will
learn how to organize data for training, build and train an artificial neural
network from scratch, build and fine-tune convolutional neural networks (CNNs),
implement fine-tuning and transfer learning, deploy models using both front-end
and back-end deployment techniques.

 

 

Deep Learning with Keras
– Course Syllabus

 

1. Introduction to Deep
Learning & Keras

·        
What is deep learning?

·        
What is ANN?

·        
Introduction to Keras

a) Overview of Keras

b) Features of Keras

c) Benefits of Keras

·        
Keras Installation

 

2. Keras – Models,
Layers and Modules

·        
Keras Models




a) Sequential Model

b) Functional API

·        
Keras Layers

a)
Dense Layers

b)
Dropout Layers

c)
Convolution Layers

d)
Pooling Layers

·        
Keras Modules

 

3. Keras – Model
Compilation, Evaluation and Prediction

·        
Loss

·        
Optimizer

·        
Metrics

·        
Compile the model

·        
Model Training

·        
Model Evaluation

·        
Model Prediction

 

4. Life-Cycle for Neural
Network Models in Keras

·        
Define Network

·        
Compile Network

·        
Fit Network

·        
Evaluate Network

·        
Make Predictions

 

5. Building our first
Neural Network with Keras

(Building
a Multilayer Perceptron neural network)

·        
Load Data

·        
Define Keras Model

·        
Compile Keras Model

·        
Fit Keras Model

·        
Evaluate Keras Model

·        
Make Predictions

 

6. Building Image
Classification Model with Keras

·        
What is Image Recognition (Classification)

·        
Convolutional Neural Network (CNN) & its layers

·        
Building Image Classification Model (step by step)

 

 

Key Features of Keras

·        
Keras is an API designed for humans

·        
Focus on user experience has always been a major part of Keras

·        
Large adoption in the industry

·        
Highly Flexible

·        
It is a multi backend and supports multi-platform, which helps
all the encoders come together for coding

·        
Research community present for Keras works amazingly with the
production community

·        
Easy to grasp all concepts

·        
It supports fast prototyping

·        
It seamlessly runs on CPU as well as GPU

·        
It provides the freedom to design any architecture, which then
later is utilized as an API for the project

·        
It is really very simple to get started with

·        
Easy production of models actually makes Keras special

·        
Easy to learn and use

 

 

What you Learn from this Course?




 

  • Introduction to Deep Learning and Neural Networks
  • Understand Deep Learning with Keras
  • Take a big step towards becoming a Deep Learning /
    Machine Learning engineer
  • Keras overview, features, benefits
  • Keras installation
  • Keras – Models, Layers and Modules
  • Keras Models – Sequential Model, Functional API
  • Keras Layers – Dense Layers, Dropout Layers,
    Convolution Layers, Pooling Layers
  • Keras Modules
  • Keras – Model Compilation, Evaluation and Prediction
  • Loss, Optimizer, Metrics, Compile the Model
  • Model Training, Model Evaluation, Model Prediction
  • Life-Cycle for Neural Network Models in Keras
  • Define Network, Compile Network, Fit Network, Evaluate
    Network, Make Predictions
  • Building your first Neural Network with Keras
  • Building a Multilayer Perceptron neural network
  • Building Image Classification Model with Keras
  • Convolutional Neural Network (CNN) & its layers

 

 

Who can Enroll in this
Course?

 

·        
Deep Learning /
Machine Learning Engineers

·        
Machine Learning
Researchers – NLP, Python, Deep Learning

·        
Data Scientists and
Machine Learning Scientists

·        
Newbies and Beginners
aspiring for a career in Machine Learning / Data Science / Deep Learning

·        
Head of Engineering
and Technical Leads

·        
Anyone who wants to
learn Deep Learning and Machine Learning

·        
Computer Vision
Researchers

·        
AI Deep Learning
Platform Leads

·        
Senior ML and Deep
Learning Scientists

·        
Senior Data
Consultants & Analytics Professionals

·        
Product Managers

·        
Artificial
Intelligence Program Leads

 

What
Requirement for this Course?

 

·        
Enthusiasm and
determination to make your mark on the world!

 

                                                                             

How can Learn Course
this Course?

1.   
Create
Account / Login on Udemy.com

2.   
Learn
Course by Enroll in this Course

         

Coupon Code for This Course:  KERAS_UPLATZ  

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