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  • Instructor
    Manaranjan Pradhan
  • Category
    Deep Learning using Python
  • Course Fees
    Quotation on request Rs.

DEEP LEARNING USING PYTHON

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.

We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced with working examples. You will also be introduced to H2O package to build a robust and scalable deep learning model. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

WHO SHOULD ATTEND

Irrespective of type of industry (retail, e-commerce, manufacturing, real estate & construction, telecom, hospitality, banking, healthcare, IT, supply chain &logistic, etc.); data forms the crux of decision making. This course is designed for graduates and post graduates who will venture into the corporate set up and will be assisting the management in various decision making process. This course is equally suited to hone up analytical skills and business acumen of mid-level and senior level corporate professionals trying to understand the nuances of data science and help them the machine learning techniques an efficient way to generate insights for customers which in turn optimizes the bottom line of organizations.

HARDWARE AND SOFTWARE

  1. Participants should bring their laptop (preferably Windows 7 or higher/ Mac OS installed).
  2. Operating System (any of the following):
  • Mac OS X with XQuartz
  • Windows (Version XP or later) is required.
  1. Minimum 8 GB RAM on the system is advisable. 16GB RAM is preferable.

INSTALLATIONS:

 

More about anaconda can be found at https://docs.anaconda.com. Participants are expected to resolve any installation issues of the software prior to the commencement of the session.

PRE-REQUISITE & COURSE DELIVERABLE

  1. Participants should have basic programming skills. Participants are expected to spend time with the code set as a home assignment to leverage the classroom training hours to the fullest.
  2. High speed internet connection will be provided at the training venue.
  3. Deliverable: Python code and dataset. Soft copy of the content being covered  (PDF file)

COURSE OUTLINE

Day 1: Understanding Deep Learning concepts and libraries

Session 1 – Introduction to Deep Learning

  • Understanding how machines learn
  • Understanding perceptron and multiplayer Neural Networks
  • Overview of Deep Learning Frameworks: Keras & Tensorflow

Session 2, 3 & 4 – Deep Dive into Deep Learning

  • Understanding Gradient Descent: Mini-batch and Stochastic
  • Overview Back Propagation, Cost Function & Optimizers
  • Writing a Deep Learning Algorithm from Scratch using Tensorflow
  • Developing a deep learning model using Keras
  • Developing a Regression & Classification Model using NN
  • NN Network Architecture & it’s hyper-parameters

Session 5 - Convolutions Neural Networks

  • Understanding Convolutions,  Filters, Max Pooling, Dropouts
  • Understanding Image Classification
  • Overview of different NN architectures – VGG Net, Lenet, googLenet
  • Building a CNN Model using Keras
  • Tensorboard for monitoring

Day 2: Understanding Deep Learning concepts and libraries

Session 1 - Convolutions Neural Networks

  • Understanding Convolutions,  Filters, Max Pooling, Dropouts
  • Understanding Image Classification
  • Overview of different NN architectures – VGG Net, Lenet, googLenet
  • Building a CNN Model using Keras
  • Tensorboard for monitoring

Session 2, 3 & 4: Sequence Models

  • Recurrent Neural Networks
  • Natural Language Processing and Word Embeddings
  • Sequence Models
  • Natural Language Processing - Building an RNN Model using Keras

Session 5: Model Evaluation

  • Creating Training, validation and Test Data Sets, Cross validations

 Understanding Evaluation Metrics: RMSE, R-square, Confusion Matrix, Precision, Recall, Accuracy etc.

COURSE SCHEDULE

Day 1: Understanding of Neural Networks, Deep Learning and Libraries

This day will be about underlying concepts of neural networks and CNN

Topic

Session

From

To

Introduction to Deep Learning

1

9 AM

10:15 AM

Deep dive into Deep Learning

2

10:30 AM

11:45 AM

Deep dive into Deep Learning…cont.

3

12:00 PM

1:15 PM

Deep dive into Deep Learning…cont.

4

2:15 PM

3:30 PM

Convolutions Neural Networks

5

3:45 PM

5:00 PM

 

Day 2: Advanced Concepts of Deep Learning and Applications

Application driven advanced concepts of RNN and model evaluation in Deep Learning

Topic

Session

From

To

Convolutions Neural Networks…cont.

1

9 AM

10:15 AM

Sequence Models

2

10:30 AM

11: 45 AM

Sequence Models…cont.

3

12:00 PM

1:15 PM

Sequence Models…cont.

4

2:15 PM

3:30 PM

Model Evaluation

5

3:45 PM

5:00 PM

 

Course Reviews

Average Rating:4.6

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Comments

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    23/06/2014

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    23/06/2014

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    John Doe says:
    23/06/2014

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    John Doe says:
    23/06/2014

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  • John Doe says:
    23/06/2014

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