Course curriculum
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1
Introduction to the Course
- Introduction to the Deep Neural Networks
- Why Deep learning Networks (DNN)
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2
Introduction to Machine Learning
- Introduction to Machine Learning, Learning Process and Supervised Learning
- UnSupervised Learning and Reinforcement Learning
- History and Future of Machine Learning
- Dataset, Label and Features
- Training Data,Testing Data and Outliers
- Machine Learning Model
- Difference between Classification and Regression
- Function, Parameters and Hyperparameters
- Model Training, Cost, Error, Loss, Risk and Accuracy
- Optimization
- Overfitting, Underfitting and Just Right Optimum (Part 1)
- Overfitting, Underfitting and Just Right Optimum (Part 2)
- Validation and Cross Validation,Generalization,Data Snooping,Validation Set
- Probability Distributions and Curse of Dimensionlity
- Small Sample Size problems,One Shot Learning
- Importance of Data in Machine Learning,Data Encoding and Preprocessing
- General Flow of a typical Machine Learning Project
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3
Introduction to Python
- Introduction to Python
- Introduction to IDE,Hello World
- Introduction to Data Type, Numbers
- Variable and Operators (Numbers)
- Variables and Operators (Rational Operators and Functions)
- Variables and Operators (String)
- Variables and Operators (String and print Statement)
- Lists(Indexing,Slicing-Built in Lists Functions)
- Lists(Copying a List)
- Tuples(Indexing,Slicing,Built in Tuple Functions)
- Set(initialize,Built in Set Functions)
- Dictionary
- Logical Operator,Decision Making,For Loops,While Loops,Functions
- Logical Operator,Decision Making,For Loops,While Loops,List Comprehension
- Functions
- Calculator Project
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4
Deep Neural Networks and Deep Learning Basics
- Introduction to Artificial Neural Networks
- Neuron and Perceptron
- Deep Neural Network Architecture
- FeedForward fully Connected MLP
- Calculating Number of weights of DNN
- Number Of Neurons Vs Number Of Layers
- Discriminative Vs Generative Learning
- Universal Approximation Theorem
- Why Depth
- Decision Boundary in DNN
- Bias Term
- The Activation Function
- DNN Training Parameters
- Gradient Descent
- Backpropagation
- Training DNN Animantion
- Weigth Initialization
- Batch MiniBatch Stocastic
- Batch Normalization
- Rprop Momentum
- convergence Animation
- Drop Out Early Stopping Hyperparameters
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5
Python for Data Science
- Python Packages for Data Science
- NumPy Pandas and Matplotlib (Part 1)
- NumPy Pandas and Matplotlib (Part 2)
- NumPy Pandas and Matplotlib (Part 3)
- NumPy Pandas and Matplotlib (Part 4)
- NumPy Pandas and Matplotlib (Part 5)
- NumPy Pandas and Matplotlib (Part 6)
- DataSet Preprocessing
- TensorFlow for classification
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6
Implementation of DNN for COVID 19 Analysis
- COVID19 Data Analysis
- COVID19 Regression with TensorFlow
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7
Optional Course for Maths behind DNN
- Understanding Gradient Decsent
- Understanding Gradient Decsent