A computer application which is used to analyse a large volume of data, in an efficient manner, which has tensor flow at the abck end.

3. PLAN OF PRESENTATION
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4.  High-level neural networks API, written in
Python
 Models described in Python code. No
separate models config files
 Allows for easy and fast prototyping
(through user friendliness, modularity, and
extensibility)
 Runs seamlessly on CPUs and GPUs

6. Keras is an Application Programme Interface
designed for human beings, not machines. It puts
user experience front and center. Keras follows
best practices for reducing cognitive load (amount
of working memory resources used): it offers
consistent & simple APIs, it minimizes the
number of user actions required for common use
cases, and it provides clear and actionable
feedback upon user error.

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9. Buildasimplemodel
In Keras, you assemble layers to build models. A model is (usually) a
graphof layers.
Themost commontype of model isastack of layers: the model.
Tobuildasimple, fully-connected network (i.e., amulti-layerperceptron):

10. Asimple, fully-connected network

11. Inputdata
YoucantrainkerasmodelsdirectlyonRmatricesandarrays(possiblycreatedfrom
(possiblycreatedfromR ).
Amodelisfittothetrainingdatausingthe method:

12.  Keras is a high-level API to build and train deep
learning models. It’s used for fast prototyping,
advanced research, and production, with three key
advantages:
User friendly – Keras has a simple, consistent
interface optimized for common use cases. It provides
clear and actionable feedback for user errors.
Modular and composable – Keras models are made
by connecting configurable building blocks together,
with few restrictions.
Easy to extend – Write custom building blocks to
express new ideas for research. Create new layers,
loss functions, and develop state-of-the-art models.