Deep Learning

This Deep learning course will take you through the state of the art methods and techniques currently used by the industry stalwarts in data science applications. It’s holistic curriculum is well suited for the participants willing to make a career in the sphere of Deep learning. The course will catapult the participants ahead of the competition with the thorough theoretical and hands on experience they will gain over the six weeks live training. At the end of this course the participants will feel properly equipped to apply deep learning models in their respective fields of work and generate valuable insights to enable decision making to drive the business to greater heights.

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Training Highlights

Why this course ?

With the advent of high computation power and increasing amount of data, traditional machine learning models are not capable of producing highly accurate results. The traditional machine Learning models fail all the more when it comes to unstructured data. Deep learning has seen a rise in application is the last few years, and with new platforms coming in to make it more user friendly to implement, deep learning is the talk of the town. This comprehensive course will deep dive in the applications of neural nets and compare it with traditional models to showcase the strides that deep learning has taken over the previous approaches. It will enable the participants to understand every detail of neural nets from the very basic. The course has been crafted for professionals who would want to see the practical applications of deep learning and not just be satisfied with the theoretical aspects. Business oriented case studies will make the participants industry-ready by the end of the course. The duration of the course has been strategically set for 45 days so that the participants have enough time to complete the exercises and understand the Deep Learning concepts in depth. Training will be conducted over weekends so working professionals can easily attend this course.


Module 1

  • Advanced Machine Learning using python.
    • Linear regression
    • Under fitting and overfitting
    • Regularisation
    • Cross validation
    • Maximum likelihood estimation
    • Stochastic gradient descent
    • Hands on exercise

Module 2

  • Feed forward neural networks, Basics of TensorFlow , analogy and comparison with machine learning algorithms.
    • Loss functions

    • Gradient based learning

    • Activation function

    • Back propagation algorithm

    • Chain rule

    • Basics of tensorflow

    • Implementing back-propagation


  • Artificial neural networks and hands on with TensorFlow on a business case
      • Neural net architecture

      • Weight initialisation

      • Batch normalisation

      • Regularisation

      • Gradient noise

      • Momentum

      • Hands on exercise

Module 3

  • Understanding image data, convolutions neural networks and hands on with TensorFlow on a business case

    • Fully connected layer vs locally connected layer

    • Filters

    • Convolutional layer, pooling, dropout

    • Transfer learning

    • Hands on exercise

  • Recurrent neural networks and hands on with TenserFlow on a business case

    • Sequence

    • Modelling sequence data

    • Back propagation through time

    • Exploding gradient problem

    • Vector to sequence RNN

    • Bidirectional RNNs

    • Encoder decoder sequence to sequence RNN

    • Hands on exercise

Module 4

  • Long short term memory Vs Gated Recurrent Unit and hands on with TensorFlow On a business case
    • Idea behind LSTM

    • Forget gate

    • GRU

    • Project

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