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COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
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Installation and Environment Setup
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Introduction to NumPy
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NumPy Arrays
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NumPy Arrays Part Two
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Numpy Index Selection
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NumPy Operations
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Numpy Exercises
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Numpy Exercises - Solutions
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Pandas Overview
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Pandas Series
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Pandas DataFrames - Part One
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Pandas DataFrames - Part Two
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GroupBy Operations
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Pandas Operations
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Data Input and Output
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Pandas Exercises
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Pandas Exercises - Solutions
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PyTorch Basics Introduction
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Tensor Basics
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Tensor Basics - Part Two
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Tensor Operations
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Tensor Operations - Part Two
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PyTorch Basics - Exercise
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PyTorch Basics - Exercise Solutions
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What is Machine Learning?
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Supervised Learning
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Overfitting
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Evaluating Performance - Classification Error Metrics
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Evaluating Performance - Regression Error Metrics
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Unsupervised Learning
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Introduction to ANN Section
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Theory - Perceptron Model
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Theory - Neural Network
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Theory - Activation Functions
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Multi-Class Classification
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Theory - Cost Functions and Gradient Descent
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Theory - BackPropagation
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PyTorch Gradients
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Linear Regression with PyTorch
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Linear Regression with PyTorch - Part Two
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DataSets with PyTorch
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Basic Pytorch ANN - Part One
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Basic PyTorch ANN - Part Two
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Basic PyTorch ANN - Part Three
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Introduction to Full ANN with PyTorch
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Full ANN Code Along - Regression - Part One - Feature Engineering
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Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features
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Full ANN Code Along - Regression - Part Three - Tabular Model
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Full ANN Code Along - Regression - Part Four - Training and Evaluation
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Full ANN Code Along - Classification Example
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ANN - Exercise Overview
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ANN - Exercise Solutions
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Introduction to CNNs
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Understanding the MNIST data set
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ANN with MNIST - Part One - Data
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ANN with MNIST - Part Two - Creating the Network
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ANN with MNIST - Part Three - Training
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ANN with MNIST - Part Four - Evaluation
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Image Filters and Kernels
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Convolutional Layers
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Pooling Layers
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MNIST Data Revisited
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MNIST with CNN - Code Along - Part One
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MNIST with CNN - Code Along - Part Two
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MNIST with CNN - Code Along - Part Three
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CIFAR-10 DataSet with CNN - Code Along - Part One
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CIFAR-10 DataSet with CNN - Code Along - Part Two
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Loading Real Image Data - Part One
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Loading Real Image Data - Part Two
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CNN on Custom Images - Part One - Loading Data
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CNN on Custom Images - Part Two - Training and Evaluating Model
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CNN on Custom Images - Part Three - PreTrained Networks
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CNN Exercise
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CNN Exercise Solutions
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Introduction to Recurrent Neural Networks
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RNN Basic Theory
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Vanishing Gradients
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LSTMS and GRU
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RNN Batches Theory
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RNN - Creating Batches with Data
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Basic RNN - Creating the LSTM Model
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Basic RNN - Training and Forecasting
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RNN on a Time Series - Part One
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RNN on a Time Series - Part Two
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RNN Exercise
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RNN Exercise - Solutions
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Why do we need GPUs?
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Using GPU for PyTorch
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Introduction to NLP with PyTorch
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Encoding Text Data
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Generating Training Batches
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Creating the LSTM Model
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Training the LSTM Model
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Generating Predictions