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Course
curriculam
Introduction to AI and deep learning
Lecture 1.1 Introduction to Deep Learning
Lecture 1.2 Necessity of Deep Learning over Machine Learning.
Lecture 1.3 History and evolution of various deep learning algorithms
Lecture 1.4 AI and how is Deep Learning one of the paths to AI in the recent era
Lecture 1.5 Types of Machine Learning and Deep Learning
Lecture 1.6 Why Deep Learning
Master Deep Networks
Lecture 2.1 Working of a Deep Network
Lecture 2.2 What is Perceptron
Lecture 2.2 What is Perceptron
Lecture 2.3 What is Neuron
Lecture 2.4 Sigmoid neuron
Lecture 2.5 Activation functions
Lecture 2.6 Cost function
Lecture 2.7 Optimization
Lecture 2.8 Dense networks
Lecture 2.9 Regularization
Lecture 2.10 Layered structures
Lecture 2.11 Types of layers
Lecture 2.12 Forward pass
Lecture 2.13 Back propagation - chain rule and evaluation metrics
Lecture 2.14 Gradient Descent
Lecture 2.15 SGD (for a SoftMax classifier example)
Lecture 2.16 Nestorov's momentum
Lecture 2.17 RMSProp
Lecture 2.18 Adam
Objective on neural networks using tensorflow
Lecture 3.1 Introduction to TensorFlowPreview
Lecture 3.2 Advantages of TensorFlow
Lecture 3.3 VectorizationPreview
Lecture 3.4 Variable declaration
Lecture 3.5 Sessions
Lecture 3.6 Graphs
Lecture 3.7 Tensorboard
Lecture 3.8 Implementation of a simple Perceptron in TensorFlow
Lecture 3.9 Implementing a simple feed forward Neural Network in TensorFlow
Lecture 3.10 Various activation functions and their ranges
Lecture 3.11 Pros and cons of Activation functions
Lecture 3.12 Why to use specific activation function
Lecture 3.13 When is the usage of activation function
Lecture 3.14 What are the ones used in industry for specific tasks
Lecture 3.15 Visualization of competition based craft and model results
Knowledge on CNN
Lecture 4.1 Introduction to CNN (Convolutional Neural Networks)
Lecture 4.2 Applications of CNN
Lecture 4.3 CNN Architecture
Lecture 4.4 Convolution
Lecture 4.5 Pooling layers
Lecture 4.6 CNN illustrations
Knowledge on RNN
Lecture 5.1 Fundamentals of RNN (Recurrent Neural Network)
Lecture 5.2 Applications of RNN
Lecture 5.3 Modelling sequencing
Lecture 5.4 Types of RNNs - LSTM, GRU
Lectures 5.5 Recursive Neural Tensor Network Theory
Keras
Lecture 6.1 Introduction of Keras
Lecture 6.2 Understanding of Keras Model Building Blocks
Lecture 6.3 Illustration of different Compositional Layers
Lectures 6.4 Process based use cases’ implementations
TF Learn
Lecture 7.1 Introduction of TFlearn
Lecture 7.2 Understanding of TFlearn Model Building Blocks
Lecture 7.3 Illustration of different Compositional Layers
Lectures 7.4 Step-wise use-cases implementations
Different architectures and performance improvements
Lecture 8.1 ConvNets architecture
Lecture 8.2 Performance evaluations
Lecture 8.3 Hyperparameter search
Lecture 8.4 Auto-monitoring of loss monitoring
Lecture 8.5 Input pre-processing
Lecture 8.6 Productionization of a deep learning pipeline
Lecture 8.7 Cloud workspace set-up for designing a prototype
Building an AI application with computer vision
Lecture 9.1 Application Building
Building an AI application - natural language processing
Lecture 10.1 Application Building
Simple
Neural Network
Deep Learning
Neural Network
