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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