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- History of Neural networks and Deep Learning
- How do Biological Neurons work?
- Growth of biological neural networks
- Diagrammatic representation: Logistic Regression and Perceptron
- Multi-Layered Perceptron (MLP)
- Notation
- Training a single-neuron model
- Training an MLP: Chain Rule
- Training an MLP: Memoization
- Backpropagation
- Activation functions
- Vanishing Gradient problem
- Bias-Variance tradeoff
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- 3 Days Live Virtual Training on AI and Deep Learning with Python
3 Days Live Virtual Training on AI and Deep Learning with Python
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekdays
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Course and certificate fees
certificate availability
Yes
certificate providing authority
Simpliv Learning
The syllabus
Introduction to Python
Introduction to Logistic Regression
Introduction to Artificial Neural Network
Deep Multi-layer perceptrons
- Deep Multi-layer perceptrons:1980s to 2010s
- Dropout layers & Regularization
- Rectified Linear Units (ReLU)
- Weight initialization
- Batch Normalization
- Optimizers: Hill-descent analogy in 2D
- Optimizers: Hill descent in 3D and contours
- SGD Recap
- Batch SGD with momentum
- Nesterov Accelerated Gradient (NAG)
- Optimizers: AdaGrad
- Optimizers : Adadelta andRMSProp
- Adam
- Which algorithm to choose when?
- Gradient Checking and clipping
- Softmax and Cross-entropy for multi-class classification
- How to train a Deep MLP?
Convolutional Neural Network
- Biological inspiration: Visual Cortex
- Convolution: Edge Detection on images
- Convolution: Padding and strides
- Convolution over RGB images
- Convolutional layer
- Max-pooling
- CNN Training: Optimization
- Receptive Fields and Effective Receptive Fields
- ImageNet dataset
- Data Augmentation
- Convolution Layers in Keras
- AlexNet
- VGGNet
- Residual Network
- Inception Network
- What is Transfer learning?
Recurrent Neural Network
- Why RNNs?
- Recurrent Neural Network
- Training RNNs: Backprop
- Types of RNNs
- Need for LSTM/GRU
- LSTM
- GRUs
- Deep RNN
- Bidirectional RNN
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