Deep Learning Training in Hyderabad

Become master in data scientist with our Prime Training.
750 ratings
4.6/5
Course Duration

30 Days

Training Options

Classroom / Online

deep learning course

Course Overview

Tools Covered

R Programming Language, Python, Jupyter, Keras, H2o, IBM Watson

Pre-requisites

  • Solid understanding of Machine learning
  • Passion for AI Tools

What you will learn

  • AI Methods: Image data, Speech data, Text Data
  • Deep learning with Keras, Tenserflow & IBM Watson

5 Reasons to Join

Syllabus

Customized syllabus based on industry needs.

Real time

Real time industry experienced trainers.

Limited

Limited students per batch.

Resume

Resume building Interview preparation.

Placement

Dedicated placement support.

Training Options

Classroom Training

  • Customized curriculum as per industry needs.
  • Training by real-time professionals.
  • The best classroom and Lab infrastructure.
  • Student friendly staff and management.
  • 365 Days placement support with partner clients.

Online Training

  • Live demonstration of of features and practicals.
  • Get LMS access of each online training session that you attend through GotoMeeting.
  • Gain guidance on certification.
  • Attend a Free Demo before signing up.





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    Part 1 – Artificial Neural Networks

    All below sections will be implemented with tensorflow and keras, Programming knowledge of tensorflow and keras will be given during model buildings.

    Section 1

    1. Artificial Neural networks Intuition

    2. Plan of Attack

    3. The Neuron

    4. The Activation Function

    5. How do Neural Networks work?

    6. How do Neural Networks learn?

    7. Gradient Descent

    8. Stochastic Gradient Descent

    9. Backpropagation

    Section 2

    10. Building an ANN

    11. Prerequisites

    12. How to get the dataset

    13. Business Problem Description

    14. Building an ANN – Step 1

    15. Building an ANN – Step 2

    16. Building an ANN – Step 3

    17. Building an ANN – Step 4

    18. Building an ANN – Step 5

    19. Building an ANN – Step 6

    20. Building an ANN – Step 7

    21. Building an ANN – Step 8

    22. Building an ANN – Step 9

    23. Building an ANN – Step 10

    Section 3

    24. Homework Challenge – Should we say goodbye to that customer?

    25. Homework Instruction

    26. Homework Solution

    Section 4

    27. Evaluating, Improving and Tuning the ANN

    28. Evaluating the ANN

    29. Improving the ANN

    30. Tuning the ANN

    Part 2 – Convolutional Neural Networks

    Section 5

    31. CNN Intuition

    32. What you’ll Need for CNN

    33. Plan of attack

    34. What are convolutional neural networks?

    35. Step 1 – Convolution Operation

    36. Step 1(b) – ReLU Layer

    37. Step 2 – Pooling

    38. Step 3 – Flattening

    39. Step 4 – Full Connection

    40. Summary

    41. Softmax

    42. Cross-Entropy

    Section 6

    43. Building a CNN

    44. How to get the dataset

    45. Introduction to CNNs

    46. Building a CNN – Step 1

    47. Building a CNN – Step 2

    48. Building a CNN – Step 3

    49. Building a CNN – Step 4

    50. Building a CNN – Step 5

    51. Building a CNN – Step 6

    52. Building a CNN – Step 7

    53. Building a CNN – Step 8

    54. Building a CNN – Step 9

    55. Building a CNN – Step 10

    Section 7

    56. Homework – What’s that pet?

    57. Homework Instruction

    58. Homework Solution

    59. Evaluating, Improving and Tuning the CNN

    Part 3 – Recurrent Neural Networks

    Section 8

    60. RNN (Recurrent Neural networks) Intuition

    61. What you’ll need for RNN

    62. Plan of attack

    63. The idea behind Recurrent Neural Networks

    64. The Vanishing Gradient Problem

    65. LSTMs

    66. Practical intuition

    67. LSTM Variations

    Section 9

    68. Building a RNN

    69. How to get the dataset

    70. Building a RNN – Step 1

    71. Building a RNN – Step 2

    72. Building a RNN – Step 3

    73. Building a RNN – Step 4

    74. Building a RNN – Step 5

    75. Building a RNN – Step 6

    76. Building a RNN – Step 7

    77. Building a RNN – Step 8

    78. Building a RNN – Step 9

    79. Building a RNN – Step 10

    80. Building a RNN – Step 11

    81. Building a RNN – Step 12

    82. Building a RNN – Step 13

    83. Building a RNN – Step 14

    84. Building a RNN – Step 15

    Section 10

    85. Evaluating, Improving and Tuning the RNN

    86. Evaluating the RNN

    87. Improving the RNN

    88. Tuning the RNN

    Part 4 – Self Organizing Maps

    Section 11

    89. SOMs [Self-Organizing Maps] Intuition

    90. Plan of attack

    91. How do Self-Organizing Maps Work?

    92. Why revisit K-Means?

    93. K-Means Clustering (Refresher)

    94. How do Self-Organizing Maps Learn? (Part 1)

    95. How do Self-Organizing Maps Learn? (Part 2)

    96. Live SOM example

    97. Reading an Advanced SOM

    98. K-means Clustering (part 2)

    99. K-means Clustering (part 3)

    Section 12

    100. Building a SOM

    101. How to get the dataset

    103. Building a SOM – Step 1

    104. Building a SOM – Step 2

    105. Building a SOM – Step 3

    106. Building a SOM – Step 4

    Section 13

    Case study All above sections will be implemented with tensorflow and keras, Programming knowledge of tensorflow and keras will be given during Model buildings

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