Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Course Curriculum

Chapter 1: Course Overview, Installs, and Setup

Lecture 1: Auto-Welcome Message

Lecture 2: Course Overview

Lecture 3: Course Setup and Installation

Lecture 4: FAQ – Frequently Asked Questions

Chapter 2: COURSE OVERVIEW CONFIRMATION

Chapter 3: NumPy Crash Course

Lecture 1: Introduction to NumPy

Lecture 2: NumPy Arrays

Lecture 3: Numpy Index Selection

Lecture 4: NumPy Operations

Lecture 5: NumPy Exercises

Lecture 6: Numpy Exercises – Solutions

Chapter 4: Pandas Crash Course

Lecture 1: Introduction to Pandas

Lecture 2: Pandas Series

Lecture 3: Pandas DataFrames – Part One

Lecture 4: Pandas DataFrames – Part Two

Lecture 5: Pandas Missing Data

Lecture 6: GroupBy Operations

Lecture 7: Pandas Operations

Lecture 8: Data Input and Output

Lecture 9: Pandas Exercises

Lecture 10: Pandas Exercises – Solutions

Chapter 5: Visualization Crash Course

Lecture 1: Introduction to Python Visualization

Lecture 2: Matplotlib Basics

Lecture 3: Seaborn Basics

Lecture 4: Data Visualization Exercises

Lecture 5: Data Visualization Exercises – Solutions

Chapter 6: Machine Learning Concepts Overview

Lecture 1: What is Machine Learning?

Lecture 2: Supervised Learning Overview

Lecture 3: Overfitting

Lecture 4: Evaluating Performance – Classification Error Metrics

Lecture 5: Evaluating Performance – Regression Error Metrics

Lecture 6: Unsupervised Learning

Chapter 7: Basic Artificial Neural Networks – ANNs

Lecture 1: Introduction to ANN Section

Lecture 2: Perceptron Model

Lecture 3: Neural Networks

Lecture 4: Activation Functions

Lecture 5: Multi-Class Classification Considerations

Lecture 6: Cost Functions and Gradient Descent

Lecture 7: Backpropagation

Lecture 8: TensorFlow vs. Keras Explained

Lecture 9: Keras Syntax Basics – Part One – Preparing the Data

Lecture 10: Keras Syntax Basics – Part Two – Creating and Training the Model

Lecture 11: Keras Syntax Basics – Part Three – Model Evaluation

Lecture 12: Keras Regression Code Along – Exploratory Data Analysis

Lecture 13: Keras Regression Code Along – Exploratory Data Analysis – Continued

Lecture 14: Keras Regression Code Along – Data Preprocessing and Creating a Model

Lecture 15: Keras Regression Code Along – Model Evaluation and Predictions

Lecture 16: Keras Classification Code Along – EDA and Preprocessing

Lecture 17: Keras Classification – Dealing with Overfitting and Evaluation

Lecture 18: TensorFlow 2.0 Keras Project Options Overview

Lecture 19: TensorFlow 2.0 Keras Project Notebook Overview

Lecture 20: Keras Project Solutions – Exploratory Data Analysis

Lecture 21: Keras Project Solutions – Dealing with Missing Data

Lecture 22: Keras Project Solutions – Dealing with Missing Data – Part Two

Lecture 23: Keras Project Solutions – Categorical Data

Lecture 24: Keras Project Solutions – Data PreProcessing

Lecture 25: Keras Project Solutions – Creating and Training a Model

Lecture 26: Keras Project Solutions – Model Evaluation

Lecture 27: Tensorboard

Chapter 8: Convolutional Neural Networks – CNNs

Lecture 1: CNN Section Overview

Lecture 2: Image Filters and Kernels

Lecture 3: Convolutional Layers

Lecture 4: Pooling Layers

Lecture 5: MNIST Data Set Overview

Lecture 6: CNN on MNIST – Part One – The Data

Lecture 7: CNN on MNIST – Part Two – Creating and Training the Model

Lecture 8: CNN on MNIST – Part Three – Model Evaluation

Lecture 9: CNN on CIFAR-10 – Part One – The Data

Lecture 10: CNN on CIFAR-10 – Part Two – Evaluating the Model

Lecture 11: Downloading Data Set for Real Image Lectures

Lecture 12: CNN on Real Image Files – Part One – Reading in the Data

Lecture 13: CNN on Real Image Files – Part Two – Data Processing

Lecture 14: CNN on Real Image Files – Part Three – Creating the Model

Lecture 15: CNN on Real Image Files – Part Four – Evaluating the Model

Lecture 16: CNN Exercise Overview

Lecture 17: CNN Exercise Solutions

Chapter 9: Recurrent Neural Networks – RNNs

Lecture 1: RNN Section Overview

Lecture 2: RNN Basic Theory

Lecture 3: Vanishing Gradients

Lecture 4: LSTMS and GRU

Lecture 5: RNN Batches

Lecture 6: RNN on a Sine Wave – The Data

Lecture 7: RNN on a Sine Wave – Batch Generator

Lecture 8: RNN on a Sine Wave – Creating the Model

Lecture 9: RNN on a Sine Wave – LSTMs and Forecasting

Lecture 10: RNN on a Time Series – Part One

Lecture 11: RNN on a Time Series – Part Two

Lecture 12: RNN Exercise

Lecture 13: RNN Exercise – Solutions

Lecture 14: Bonus – Multivariate Time Series – RNN and LSTMs

Chapter 10: Natural Language Processing