PyTorch for Deep Learning with Python Bootcamp

Course Curriculum

Chapter 1: Course Overview, Installs, and Setup

Lecture 1: COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!

Lecture 2: Installation and Environment Setup

Chapter 2: COURSE OVERVIEW CONFIRMATION CHECK

Chapter 3: Crash Course: NumPy

Lecture 1: Introduction to NumPy

Lecture 2: NumPy Arrays

Lecture 3: NumPy Arrays Part Two

Lecture 4: Numpy Index Selection

Lecture 5: NumPy Operations

Lecture 6: Numpy Exercises

Lecture 7: Numpy Exercises – Solutions

Chapter 4: Crash Course: Pandas

Lecture 1: Pandas Overview

Lecture 2: Pandas Series

Lecture 3: Pandas DataFrames – Part One

Lecture 4: Pandas DataFrames – Part Two

Lecture 5: GroupBy Operations

Lecture 6: Pandas Operations

Lecture 7: Data Input and Output

Lecture 8: Pandas Exercises

Lecture 9: Pandas Exercises – Solutions

Chapter 5: PyTorch Basics

Lecture 1: PyTorch Basics Introduction

Lecture 2: Tensor Basics

Lecture 3: Tensor Basics – Part Two

Lecture 4: Tensor Operations

Lecture 5: Tensor Operations – Part Two

Lecture 6: PyTorch Basics – Exercise

Lecture 7: PyTorch Basics – Exercise Solutions

Chapter 6: Machine Learning Concepts Overview

Lecture 1: What is Machine Learning?

Lecture 2: Supervised Learning

Lecture 3: Overfitting

Lecture 4: Evaluating Performance – Classification Error Metrics

Lecture 5: Evaluating Performance – Regression Error Metrics

Lecture 6: Unsupervised Learning

Chapter 7: ANN – Artificial Neural Networks

Lecture 1: Introduction to ANN Section

Lecture 2: Theory – Perceptron Model

Lecture 3: Theory – Neural Network

Lecture 4: Theory – Activation Functions

Lecture 5: Multi-Class Classification

Lecture 6: Theory – Cost Functions and Gradient Descent

Lecture 7: Theory – BackPropagation

Lecture 8: PyTorch Gradients

Lecture 9: Linear Regression with PyTorch

Lecture 10: Linear Regression with PyTorch – Part Two

Lecture 11: DataSets with PyTorch

Lecture 12: Basic Pytorch ANN – Part One

Lecture 13: Basic PyTorch ANN – Part Two

Lecture 14: Basic PyTorch ANN – Part Three

Lecture 15: Introduction to Full ANN with PyTorch

Lecture 16: Full ANN Code Along – Regression – Part One – Feature Engineering

Lecture 17: Full ANN Code Along – Regression – Part 2 – Categorical and Continuous Features

Lecture 18: Full ANN Code Along – Regression – Part Three – Tabular Model

Lecture 19: Full ANN Code Along – Regression – Part Four – Training and Evaluation

Lecture 20: Full ANN Code Along – Classification Example

Lecture 21: ANN – Exercise Overview

Lecture 22: ANN – Exercise Solutions

Chapter 8: CNN – Convolutional Neural Networks

Lecture 1: Introduction to CNNs

Lecture 2: Understanding the MNIST data set

Lecture 3: ANN with MNIST – Part One – Data

Lecture 4: ANN with MNIST – Part Two – Creating the Network

Lecture 5: ANN with MNIST – Part Three – Training

Lecture 6: ANN with MNIST – Part Four – Evaluation

Lecture 7: Image Filters and Kernels

Lecture 8: Convolutional Layers

Lecture 9: Pooling Layers

Lecture 10: MNIST Data Revisited

Lecture 11: MNIST with CNN – Code Along – Part One

Lecture 12: MNIST with CNN – Code Along – Part Two

Lecture 13: MNIST with CNN – Code Along – Part Three

Lecture 14: CIFAR-10 DataSet with CNN – Code Along – Part One

Lecture 15: CIFAR-10 DataSet with CNN – Code Along – Part Two

Lecture 16: Loading Real Image Data – Part One

Lecture 17: Loading Real Image Data – Part Two

Lecture 18: CNN on Custom Images – Part One – Loading Data

Lecture 19: CNN on Custom Images – Part Two – Training and Evaluating Model

Lecture 20: CNN on Custom Images – Part Three – PreTrained Networks

Lecture 21: CNN Exercise

Lecture 22: CNN Exercise Solutions

Chapter 9: Recurrent Neural Networks

Lecture 1: Introduction to Recurrent Neural Networks

Lecture 2: RNN Basic Theory

Lecture 3: Vanishing Gradients

Lecture 4: LSTMS and GRU

Lecture 5: RNN Batches Theory

Lecture 6: RNN – Creating Batches with Data

Lecture 7: Basic RNN – Creating the LSTM Model

Lecture 8: Basic RNN – Training and Forecasting

Lecture 9: RNN on a Time Series – Part One

Lecture 10: RNN on a Time Series – Part Two

Lecture 11: RNN Exercise

Lecture 12: RNN Exercise – Solutions

Chapter 10: Using a GPU with PyTorch and CUDA

Lecture 1: Why do we need GPUs?

Lecture 2: Using GPU for PyTorch