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