TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka

Machine learning is
a complex discipline but implementing machine
learning models is far less daunting and difficult than it used to be. Thanks to machine learning Frameworks
such as Google’s TensorFlow that ease the process of acquiring data, training model,
solving predictions and refining future
results. Created by the Google brain team tensorflow
is an open source library for numerical computation and large scale machine learning.
Tensorflow bundles together a study of machine learning and deep learning models and algorithms and make
them useful by way of common metaphor who will use machine learning
and all of its products to improve the search engine
the translation image captioning or the recommendations to give
you a concrete example, Google users can experience
a faster and more refined search with artificial intelligence. If the user types a keyword
in the search bar Google provides a recommendation about
what could be the next world not as a flow is being used
by a lot of Companies in the industries and
to name a few first let’s start with Airbnb, the leading Global
Online Marketplace and Hospitality service. The Airbnb ingenious
and data science team applies machine learning using tensorflow to
classify the images and detect objects at scale helping to improve
the guest experience and we talked about the healthcare industry
using tensorflow GE Healthcare is training a neural network
to identify specific anatomic during the brain MRI exam
to help improve speed and reliability now PayPal
is using it as a flow to stay at The Cutting Edge of fraud detection using tensorflow deep
trance for Learning and Generator modeling PayPal has been able to recognize complex fraud patterns to
increase fraud decline accuracy while improving the experience
of legitimate users through increased Precision
in identification. China mobile is using tensorflow
to improve their success rate of the network element
cut overs Channel while has created
a deep Fist amusing tensorflow that can automatically predicts
the cut over time window verify log operations and detect Network anomalies and this has already
successfully supported the world’s largest relocation
of hundreds of millions iot HSS. Let’s talk about
the tensorflow feature. What makes it stand out
from the other competition. So Tessa flow offers
multiple level of abstractions, so you can choose
the right one for your needs. You can build and train models by using
the high-level Kira’s API, which makes getting started with tensorflow and machine
learning very very easy. If you need more flexibility
Iker execution allows for immediate iteration
and intuitive debugging when you enable eager execution, you will be executing
tensorflow kernels immediately rather than constructing graphs that will be executed
later know it provides a direct path to protection whether it’s on servos
the S devices or the web tensorflow lets
you train and deploy your model. Really no matter what language or the platform you
are using you can build and train the state-of-the-art
models without sacrificing speed or performance. That’s the flow gives
you the flexibility and the control with features
like Kira’s functional API and model subclassing
APA for creation of complex topologies. There’s a flow also
supports an ecosystem of powerful add on libraries and models to experiment
with the tisza flows name directly derived from
its core framework. It does a flow all
the computation involves tensor. So a tensor is a vector
or a matrix of n Dimensions that represents the type all
the operations are conducted inside a graph and the graph
is set of a computation that takes place successively. Each operation is
called an open note and are connected to each other. There’s a flow allows
the developers to create a data flow graphs
which are structures that describe how the data move
through a graph or a series of processing nodes. Each node in the graph represents a mathematical
operation and each connection or Edge between the notes is
a multi-dimensional data array or tensile test flow
provides all of this for the programmer by way of the Python language
by then is easy to learn and work with and provides
convenient ways to express how high-level abstraction
can be coupled together notes and the tensor in the tensorflow
our python objects. And there’s a flow
applications are themselves quite an application. Now the actual math operations
however are not performed in Python the libraries of transformation data available
through tears flow are written as high performance C++ binaries
python just directs the traffic between the pieces and provides high level
programming attraction to hook them together now
building a new rail. It works cannot get
any more simpler. Usually any machine learning or deep learning process
has some similar steps, but in this case of terms
of flow it is so simple any typical machine learning
life Lord any process has some of the steps like collection of data set than building
the model training the network evaluating the model and then predicting the outcome
in case of tensorflow. Most of the time is occupied
in the collection of data set now building a model
requires only a few lines of code training. The network is just a single
line evaluating the network or the model itself is a single
line of code and predicting. The model is also a single line
of code now training a neural network cannot get
any more easier than this and that is why it is
the flow remains at the top when compared to
the other competitors. Now, that’s a for competes with a slew of other machine
learning Frameworks like python or C. NT K and M. And next these are
the three major Frameworks that address many
of the same needs now pie torch in addition
to being built in Python and as many other similarities to tensorflow the hardware
accelerated components under the hood a highly
interactive development model that allows for Designing as you go work and many useful
components are already included. Now PyTorch is
generally a better choice for fast development of projects that need to be up
and running in a short time but tensorflow wins out
for larger projects and more complex workflows CNTK the Microsoft
cognitive toolkit, like tensorflow uses
a graph structure to describe the data flow, but it focuses more on creating deep learning
neural network siente que handles many neural network jobs
faster and has a broader set of apis for python C++ C sharp and Java but C NT K
isn’t currently as easy to learn or deployed as tensorflow. No talking about Apache MXNet adopted by Amazon as a premier
deep learning framework on AWS can scale almost linearly across multiple gpus
and multiple machine. It also supports a broad range of languages API
like python C++. scala R JavaScript
Julia and go although its native a parent
as pleasant to work with as tensorflow. It is also in the
market another thing that gives tensorflow Edge over
other competitors is the fact that it is open source and has
a huge Community Support that not only provides
researchers a way to build new models, but also a platform
to interact with others that face some issues if we talk about a simple
program in terms of flow. So any program basically
consists of a construction phase and then an execution phase
the construction phase where you build a graph
and execution phase is where you need
to evaluate the graph and then create a session then
initialize all the variables. So as you can see
here in the example of geometric sequencing it
is so easy to execute and if this is also hard for you, there’s a float
2.0 the latest release makes this even easier to code it
has eager execution by default which makes things so
much simpler and easier. So as you can see
with either X You should our program has a strong
to a few lines of code. So guys, that’s it
for the session. I hope you got to know
tensorflow what exactly it is and how useful it is go ahead and create your own deep learning models
and see for yourself. What an incredible framework
this is till then thank you and happy learning. I hope you have enjoyed
listening to this video. Please be kind enough to like it and you can comment any
of your doubts and queries and we will reply them at the earliest do look out
for more videos in our playlist And subscribe to Edureka channel to learn more. Happy learning.


  1. edureka!

    May 6, 2019 at 1:37 pm

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Tensorflow Course curriculum, Visit our Website: http://bit.ly/2r6pJuI

  2. Doing Something Different

    July 30, 2019 at 5:03 pm

    Wow! I learned a lot about tf

  3. Henry Verkin

    October 3, 2019 at 8:26 am


  4. International Scholar Pooh

    February 15, 2020 at 9:17 am


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