Hello, and welcome! In this video, we will provide a brief overview

of the structure and capabilities of the TensorFlow library. TensorFlow is an open source library developed

by the Google Brain Team. It’s an extremely versatile library, but it

was originally created for tasks that require heavy numerical computations. For this reason, TensorFlow was geared towards

the problem of machine learning, and deep neural networks. Due to a C C++ backend, TensorFlow is able

to run faster than pure Python code. The last thing we’ll mention here is that

a TensorFlow application uses a structure known as a data flow graph. We’ll cover this in more detail shortly. TensorFlow offers several advantages for an

application. It provides both a Python and a C++ API. But the Python API is more complete and it’s

generally easier to use. TensorFlow also has great compilation times

in comparison to the alternative deep learning libraries. And it supports CPUs, GPUs, and even distributed

processing in a cluster. TensorFlow’s structure is based on the execution

of a data flow graph. A data flow graph has two basic units. A node represents a mathematical operation,

and an edge represents a multi-dimensional array, known as a tensor. So this high-level abstraction reveals how

the data flows between operations. The standard usage is to build a graph and

then execute after the session is created, by using the ‘run’ and ‘eval’ operations. Since this would be difficult for interactive

environments like IPython and Jupyter notebooks, there’s an option to create interactive sessions

that run on demand. Once the graph is built, an inner loop is

written to drive computation. Inputs are fed into nodes through variables

or placeholders. You can take a look at how that might work

in the sample graph here. In TensorFlow, a graph will only run computations

after the creation of a session. TensorFlow’s flexible architecture allows

you to deploy computation on one or more CPUs, or GPUs, or in a desktop, server, or even

a mobile device. All of this can be done while only using a

single API. As we mentioned before, TensorFlow comes with

an easy to use Python interface to build and execute your computational graphs. It’s easy to play around and learn about machine

learning using the Data Scientist Workbench, or DSWB. The point is that you don’t need any special

hardware. You can scale up and develop models faster

with different implementations. So let’s briefly touch on why TensorFlow is

suited for deep learning applications. TensorFlow has built-in support for deep learning

and neural networks, so it’s easy to assemble a net, assign parameters, and run the training

process. It also has a collection of simple, trainable

mathematical functions that are useful for neural networks. And any gradient-based machine learning algorithm

will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. Due to the large collection of flexible tools,

TensorFlow is compatible with many variants of machine learning. As a quick overview, a neural network is a

machine learning model inspired by the brain. Data comes into an input layer, and flows

across to an output layer. The hidden layers in between are responsible

for running calculations. The simple neural network you see here is

known as a Multi-layer perceptron. By increasing the number of hidden layers,

we move from a shallow neural network, to a deep neural network. Deep neural networks are capable of significantly

more complex behavior than their shallow counterparts. Each node, or neuron as it’s called, processes

input using an activation function. There are many different functions like the

binary step,the Hyperbolic Tangent, And the logistic Function. The choice of activation function has a big

impact on the network’s behavior. TensorFlow provides a lot of flexibility because

it gives you control over the network’s structure and the functions used for processing. But TensorFlow can be used for more than just

neural networks. It can also be used to take a set of points

and apply a linear regression. In its most basic form, this is essentially

a ‘line of best fit’. And if a line isn’t suitable for your data, You can use TensorFlow to build non-linear

models as well. If you need to build a model to perform classification,

with TensorFlow, you can easily implement logistic regression. And these are just a few of the basic models

that can be implemented with TensorFlow. By now, you should have a basic understanding

of TensorFlow’s structure and its capabilities. Thank you for watching this video.

her voice is way too soothing

nice

The voice seems to be machine generated ! not human.

Can any1 brief about epoch,steps, batch size?

Tensorflow tee shirt. Hope you will like this.

https://teespring.com/shop/tensorflow-t-shirt?#pid=2&cid=576&sid=front

that popping sound at start

Excellent introduction!

She is the big data instructor in linuxacademy

thanks

Very well depicted introduction. Just pointing out a very small error. There is a mistake in figure at 2:12. The red rectangles will be placeholders and blue rectangles will be variables because weights and biases keep on updating (varying) through out the training process.

bla bla

The best introduction, I've ever found. Perfect. Thank You.

Python??

what is keras ? Please make a vid on that.

Dunder Mifflin this is Pam

Python is a baby's toy