Get The Best TensorFlow Online Training at Mindmajix

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TensorFlow Agenda

• Introduction To TensorFlow
• Introduction To Deep Learning
• Fundamentals Of Neural Networks
• Fundamentals Of Deep Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Restricted Boltzmann Machine(RBM) And Autoencoders

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What is TensorFlow?

• ? TensorFlow is a multipurpose open source
  so2ware library for numerical computation using
  data flow graphs. It has been designed with deep
  learning in mind but it is applicable to a much
  wider range of problems.
• ? But what does it actually do? TensorFlow
  provides primitives for defining functions on
  tensors and automatically computing their
  derivatives.

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But whats a Tensor?

• Formally, tensors are multilinear maps from
  vector spaces to the real numbers ( vector space,
  and dual space)
• A scalar is a tensor ( )
• A vector is a tensor ( )
• A matrix is a tensor ( )
• Common to have fixed basis, so a tensor can be
  represented as a multidimensional array of
  numbers.

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Introduction To Deep Learning

• Deep Learning is machine learning technique that
  learns features and tasks directly from data.
• Data can be images, text, or sound.

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What is Artificial Intelligence?

• Every aspect of learning or any other features
  of intelligence can in principle be so precisely
  described that a machine can be made to simulate
  it. An attempt will be made to find how to make
  machine us language, form abstractions and
  concepts, solve kinds of problems now reserved
  for humans, and improve themselves.

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Why Artificial Intelligence
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Limitations of Machine Learning

• There are a few key limitations of machine
  learning approaches that impact on their
  usefulness for certain tasks, as well as their
  ability to function in real-world environments.
  Machine learning algorithms function very well on
  tasks related to familiar data from a training
  set. Limitations tend to surface when the
  algorithm tries to incorporate new data. As these
  systems advance, they are quickly becoming better
  at categorizing familiar data and performing
  tasks such as image or speech recognition.

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The Math behind Machine Learning Linear
Algebra ScalarsVectorsMatricesTensorsHyperplan
es The Math Behind Machine Learning
Statistics ProbabilityConditional
ProbabilitiesPosterior ProbabilityDistributions
Samples vs PopulationResampling
MethodsSelection BiasLikelihood
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Defining Neural Networks

• Neural networks are a set of algorithms, modeled
  loosely after the human brain, that are designed
  to recognize patterns. They interpret sensory
  data through a kind of machine perception,
  labeling or clustering raw input. The patterns
  they recognize are numerical, contained in
  vectors, into which all real-world data, be it
  images, sound, text or time series, must be
  translated.
• 
  (OR)
• A neural network is a series of algorithms that
  attempts to identify underlying relationships in
  a set of data by using a process that mimics the
  way the human brain operates. Neural networks
  have the ability to adapt to changing input so
  the network produces the best possible result
  without the need to redesign the output criteria.
  The concept of neural networks is rapidly
  increasing in popularity in the area of
  developing  trading systems.

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Deep Learning

• The important innovation in deep learning is a
  system that learns categories incrementally
  through its hidden layer architecture, defining
  low-level categories like letters before moving
  on to higher level categories such as words. In
  the example of image recognition this means
  identifying light/dark areas before categorising
  lines and then shapes to allow face recognition.
  Each neuron or node in the network represents one
  aspect of the whole and together they provide a
  full representation of the image. Each node or
  hidden layer is given a weight that represents
  the strength of its relationship with the output
  and as the model develops the weights are
  adjusted.

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• LAYER 1 Algorithm first learns to recognise
  pixels and then edges and shapes
• LAYER 2 Learns to identify more complex shapes
  and features like eyes and mouths
• LAYER 3 Learns which shapes and objects can be
  used to identify a human face

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Convolution Neural Network
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Recurrent Neural Network Model
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Why Recurrent Neural Network
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Long-Short Term Memory(LSTM)
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Output Gate
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Forget Gate
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Input Gate
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LSTM
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Simplified LSTM
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Restricted Boltzmann Machine
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Restricted Boltzmann Machine
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