The document provides an introduction to machine learning and neural networks. It defines key terms like neurons, non-linear systems, convolutional neural networks, and tensors. It explains that machine learning allows machines to achieve human intelligence through techniques like deep learning. It also discusses TensorFlow, an open source library for building and training neural networks, and how convolutional neural networks like Inception V3 work by running multiple convolution operations in parallel. Examples of applications using these techniques include virtual assistants, Google Photos, and Snapchat filters.

1. - An introduction to machine learning and neural networks
Ankur Nair

2. Before we start,
let’s familiarize
with these…
• Neuron – Neurons (Artificial) are elementary units in a
neural network. A neuron receives one or more inputs and
sums them to produce an output. These are designed to
mimic their biological counterparts.
• Non-linear system – Its a set of simultaneous equations in
which the unknowns (differential equations) appear as
variables of a polynomial of degree higher than one or in
the argument of a function which is not a polynomial of
degree one. Or in simpler terms the output is not
proportional to the input provided.
• Convolutional Neural Network – A network consisting of
many identical copies of the same neuron that uses a
weight tying.
• Tensor – N dimensional array.
• 1 Dimension – Vector
• 2 Dimension – Matrix
• N Dimension – Tensor

3. • ARTIFICIAL INTELLIGENCE: Machines in-built with Human Intelligence
• MACHINE LEARNING: Approach to achieve Machines with Human Brain
• DEEP LEARNING: Techniques to train the Machine’s Brain

4. TensorFlow is an open source software library for numerical computation using data
flow graphs. Nodes in the graph represent mathematical operations, while the graph
edges represent the multidimensional data arrays (tensors) communicated between
them.

6. How does it work?
The inspiration comes from the idea that you need to
make a decision as to what type of convolution you
want to make at each layer: Do you want a 3×3? Or a
5×5? And this can go on for quite a while.
Each convolution is executed in parallel and
concatenating the resulting feature maps before going
to the next layer.
The next layer is also an Inception module. Then each
of the convolution’s feature maps will be passed
through the mixture of convolutions of the current
layer. The idea is that you don’t need to know ahead of
time if it was better to do, for example, a 3×3 then a
5×5. Instead, just do all the convolutions and let the
model pick what’s best.

7. Inception V3
Model – In
simpler
terms

8. A Quick Demo

9. TensorFlow in Action
A Virtual assistant
capable of having 2
way communications.
Google Photos uses
the Google Cloud
Vision API to
intelligently associate
users’ photos to a
face, place or theme.
Snapchat filters uses
trained models of
thousands of sample
faces (training
models) to provide
all the cool filters.

10. Thank you!