TensorFlow Certification Training


TensorFlow Certification Training

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Devops 23-Feb-18 08:00
AWS 26-Feb-18 09:30

KernelSphere’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

Why learn Tensorflow?

TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

Who should go for this training?

KernelSphere Deep learning with Tensorflow course is designed for all those who want to learn Deep Leaning which would include understanding of Deep Learning methods, Neural Networks, Deep Learning uses Tensorflow, Restricted Boltzmann Machines (RBM) and Autoencoders.

The following professionals can go for this course:

  1. Developers aspiring to be a ‘Data Scientist’
  2. Analytics Managers who are leading a team of analysts
  3. Business Analysts who want to understand Deep Learning (ML) Techniques
  4. Information Architects who want to gain expertise in Predictive Analytics
  5. Professionals who want to captivate and analyze Big Data
  6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

What are the Prerequisites of this course?

Basic programming knowledge in Python

Concept of Arrays

Concepts about Machine Learning

KernelSphere offers you a complimentary self-paced course – A Module on Stats and Machine learning algorithms: Supervised and Unsupervised learning algorithms, once you have enrolled in Deep Learning with TensorFlow course.

Course Objectives

After the completion of this Deep Learning with TensorFlow course,  you should be able to:

  • Define Deep Learning
  • Express the motivation behind Deep Learning
  • Apply Analytical mathematics on the data
  • Choose between different Deep networks
  • Explain Neural networks
  • Train Neural networks
  • Discuss Backpropagation
  • Describe Autoencoders and varitional Autoencoders
  • Run a “Hello World” program in TensorFlow
  • Implement different Regression models
  • Describe Convolutional Neural Networks
  • Discuss the application of Convolutional Neural Networks
  • Discuss Recurrent Neural Networks
  • Describe Recursive Neural Tensor Network Theory
  • Implement Recursive Neural Network Model
  • Explain Unsupervised Learning
  • Discuss the applications of Unsupervised Learning
  • Explain Restricted Boltzmann Machine
  • Implement Collaborative Filtering with RBM
  • Define Autoencoders and discuss their Applications
  • Discuss Deep Belief Network

Introduction to Deep Learning

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • Discuss the idea behind Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go Deep
  • Real-Life use cases of Deep Learning
  • Scenarios where Deep Learning is applicable
  • The Math behind Machine Learning: Linear Algebra
  • Scalars
  • Vectors
  • Matrices
  • Tensors
  • Hyperplanes
  • The Math Behind Machine Learning: Statistics
  • Probability
  • Conditional Probabilities
  • Posterior Probability
  • Distributions
  • Samples vs Population
  • Resampling Methods
  • Selection Bias
  • Likelihood

Fundamental of Nural Network

  • Defining Neural Networks
  • The Biological Neuron
  • The Perceptron
  • Multi-Layer Feed-Forward Networks
  • Training Neural Networks
  • Backpropagation Learning
  • Gradient Descent
  • Stochastic Gradient Descent
  • Quasi-Newton Optimization Methods
  • Generative vs Discriminative Models
  • Activation Functions
  • Linear
  • Sigmoid
  • Tanh
  • Hard Tanh
  • Softmax
  • Rectified Linear
  • Loss Functions
  • Loss Function Notation
  • Loss Functions for Regression
  • Loss Functions for Classification
  • Loss Functions for Reconstruction
  • Hyperparameters
  • Learning Rate
  • Regularization
  • Momentum
  • Sparsity

Introduction to TensorFlow

  • What is TensorFlow?
  • Use of TensorFlow in Deep Learning
  • Working of TensorFlow
  • How to install Tensorflow
  • HelloWorld with TensorFlow
  • Running a Machine learning algorithms on TensorFlow

Convolutional Neural Networks (CNN)

  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks

Recurrent Neural Networks (RNN)

  • Intro to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model

Restricted Boltzmann Machine(RBM) and Autoencoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Variational Autoencoders
  • Deep Belief Network


AWS 20-Feb-18 10:30
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OpenStack 20-Feb-18 07:30
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LINUX 20-Feb-18 08:00
PYTHON 20-Feb-18 12:00
EMC-SAN 20-Feb-18 06:00
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Machine Learning 20-Feb-18 08:00
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PYTHON 20-Feb-18 18:00

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