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Artificial Neural Networks_Part-1.pptx
- 1. • Amrita School of Engineering, Bangalore 19ECE354 - DeepLearning Ms. HARIKA PUDUGOSULA Lecturer Department of Electronics & Communication Engineering
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- 4. Type of EvaluationMaximum Marks Weightage Periodical 1 50 15 Periodical 2 50 15 End Semester End Semester (Written) 100 30 2 Lab Tests (10+10) 20 20 Continuous Assessment 2 MCQ-Type Quizzes (05) 30 05 3 Programming Assignments (15) 30 15 Total 280 100 Evaluation Pattern HARIKA 4
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- 6. • The Neuron •Expressing Linear Perceptrons as Neurons • Feed-Forward Neural Networks • Linear Neurons and Their Limitations • Sigmoid, Tanh, and ReLU Neurons • Softmax Output Layers 6 HARIKA
- 7. HARIKA 7 fig: Imagefrom MNIST handwritten digit dataset A zero that’s algorithmically difficult to distinguish from a six
- 8. HARIKA 8 Text bookdefination of Deep Learning - • Deep learning is a subset of a more general field of AI called machine learning, which is predicated on this idea of learning from example. • In machine learning, instead of teaching a computer a massive list of rules to solve the problem, we give it a model with which it can evaluate examples, and a small set of instructions to modify the model when it makes a mistake.
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