Curriculum
6 Sections
42 Lessons
20 Weeks
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Introduction to Python
0
Introduction to Logistic Regression
0
Introduction to Artificial Neural Network
11
3.1
History of Neural networks and Deep Learning.
3.2
How Biological Neurons work?
3.3
Growth of biological neural networks.
3.4
Diagrammatic representation: Logistic Regression and Perceptron.
3.5
Multi-Layered Perceptron (MLP).
3.6
Notation.
3.7
Training a single-neuron model.
3.8
Backpropagation.
3.9
Activation functions.
3.10
Vanishing Gradient problem.
3.11
Bias-Variance tradeoff.
Deep Multi-layer perceptrons
11
4.1
Deep Multi-layer perceptrons:1980s to 2010s
4.2
Dropout layers & Regularization.
4.3
Rectified Linear Units (ReLU).
4.4
Weight initialization.
4.5
Batch Normalization.
4.6
Optimizers:Hill descent in 3D and contours.
4.7
Adam
4.8
Which algorithm to choose when?
4.9
Gradient Checking and clipping
4.10
Softmax and Cross-entropy for multi-class classification.
4.11
How to train a Deep MLP?
Convolutional Neural Network
13
5.1
Biological inspiration: Visual Cortex
5.2
Convolution:Edge Detection on images.
5.3
Convolution:Padding and strides
5.4
Convolution over RGB images.
5.5
Convolutional layer.
5.6
Max-pooling.
5.7
CNN Training: Optimization
5.8
Receptive Fields and Effective Receptive Fields
5.9
ImageNet dataset.
5.10
Data Augmentation.
5.11
Convolution Layers in Keras
5.12
AlexNet
5.13
VGGNet
Recurrent Neural Network
7
6.1
Why RNNs?
6.2
Recurrent Neural Network
6.3
Training RNNs: Backprop
6.4
Types of RNNs
6.5
Need for LSTM/GRU
6.6
LSTM
6.7
GRUs
Deep Learning & AI
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