1. Course Overview, Installs, and Setup

  1. Course Overview.srt

  2. Course Setup and Installation.srt

  2.2 requirements.txt.txt

  3.1 FINAL_TF2_FILES.zip.zip

 10. Natural Language Processing

  1. Introduction to NLP Section.srt

  2. NLP - Part One - The Data.srt

  3. NLP - Part Two - Text Processing.srt

  4. NLP - Part Three - Creating Batches.srt

  5. NLP - Part Four - Creating the Model.srt

  6. NLP - Part Five - Training the Model.srt

  7. NLP - Part Six - Generating Text.srt

 11. AutoEncoders

  2. Autoencoder Basics.mp4

  3. Autoencoder for Dimensionality Reduction.mp4

  4. Autoencoder for Images - Part One.mp4

  5. Autoencoder for Images - Part Two - Noise Removal.mp4

  6. Autoencoder Exercise Overview.mp4

  7. Autoencoder Exercise - Solutions.mp4

 12. Generative Adversarial Networks

  1. GANs Overview.srt

  2. Creating a GAN - Part One- The Data.srt

  3. Creating a GAN - Part Two - The Model.srt

  4. Creating a GAN - Part Three - Model Training.srt

  5. DCGAN - Deep Convolutional Generative Adversarial Networks.srt

 13. Deployment

  1. Introduction to Deployment.srt

  2. Creating the Model.srt

  3. Model Prediction Function.srt

  4. Running a Basic Flask Application.srt

  5. Flask Postman API.srt

  6. Flask API - Using Requests Programmatically.srt

  7. Flask Front End.srt

  8. Live Deployment to the Web.srt

 2. COURSE OVERVIEW CONFIRMATION

 3. NumPy Crash Course

  1. Introduction to NumPy.srt

  2. NumPy Arrays.srt

  3. Numpy Index Selection.srt

  4. NumPy Operations.srt

  5. NumPy Exercises.srt

  6. Numpy Exercises - Solutions.srt

 4. Pandas Crash Course

  1. Introduction to Pandas.srt

  10. Pandas Exercises - Solutions.srt

  2. Pandas Series.srt

  3. Pandas DataFrames - Part One.srt

  4. Pandas DataFrames - Part Two.srt

  5. Pandas Missing Data.srt

  6. GroupBy Operations.srt

  7. Pandas Operations.srt

  8. Data Input and Output.srt

  9. Pandas Exercises.srt

 5. Visualization Crash Course

  1. Introduction to Python Visualization.srt

  2. Matplotlib Basics.srt

  3. Seaborn Basics.srt

  4. Data Visualization Exercises.srt

  5. Data Visualization Exercises - Solutions.srt

 6. Machine Learning Concepts Overview

  1. What is Machine Learning.srt

  2. Supervised Learning Overview.srt

  3. Overfitting.srt

  4. Evaluating Performance - Classification Error Metrics.srt

  5. Evaluating Performance - Regression Error Metrics.srt

  6. Unsupervised Learning.srt

 7. Basic Artificial Neural Networks - ANNs

  1. Introduction to ANN Section.srt

  10. Keras Syntax Basics - Part Two - Creating and Training the Model.srt

  11. Keras Syntax Basics - Part Three - Model Evaluation.srt

  12. Keras Regression Code Along - Exploratory Data Analysis.srt

  13. Keras Regression Code Along - Exploratory Data Analysis - Continued.srt

  14. Keras Regression Code Along - Data Preprocessing and Creating a Model.srt

  15. Keras Regression Code Along - Model Evaluation and Predictions.srt

  16. Keras Classification Code Along - EDA and Preprocessing.srt

  17. Keras Classification - Dealing with Overfitting and Evaluation.srt

  18. TensorFlow 2.0 Keras Project Options Overview.srt

  19. TensorFlow 2.0 Keras Project Notebook Overview.srt

  2. Perceptron Model.srt

  20. Keras Project Solutions - Exploratory Data Analysis.srt

  21. Keras Project Solutions - Dealing with Missing Data.srt

  22. Keras Project Solutions - Dealing with Missing Data - Part Two.srt

  23. Keras Project Solutions - Categorical Data.srt

  24. Keras Project Solutions - Data PreProcessing.srt

  25. Keras Project Solutions - Creating and Training a Model.srt

  26. Keras Project Solutions - Model Evaluation.srt

  27. Tensorboard.srt

  3. Neural Networks.srt

  4. Activation Functions.srt

  5. Multi-Class Classification Considerations.srt

  6. Cost Functions and Gradient Descent.srt

  7. Backpropagation.srt

  8. TensorFlow vs. Keras Explained.mp4

  9. Keras Syntax Basics - Part One - Preparing the Data.mp4

 8. Convolutional Neural Networks - CNNs

  1. CNN Section Overview.srt

  10. CNN on CIFAR-10 - Part Two - Evaluating the Model.srt

  11. Downloading Data Set for Real Image Lectures.srt

  12. CNN on Real Image Files - Part One - Reading in the Data.mp4

  13. CNN on Real Image Files - Part Two - Data Processing.mp4

  14. CNN on Real Image Files - Part Three - Creating the Model.mp4

  15. CNN on Real Image Files - Part Four - Evaluating the Model.mp4

  16. CNN Exercise Overview.mp4

  17. CNN Exercise Solutions.mp4

  2. Image Filters and Kernels.mp4

  3. Convolutional Layers.mp4

  4. Pooling Layers.mp4

  5. MNIST Data Set Overview.mp4

  6. CNN on MNIST - Part One - The Data.mp4

  7. CNN on MNIST - Part Two - Creating and Training the Model.mp4

  8. CNN on MNIST - Part Three - Model Evaluation.mp4

  9. CNN on CIFAR-10 - Part One - The Data.mp4

 9. Recurrent Neural Networks - RNNs

  1. RNN Section Overview.srt

  10. RNN on a Time Series - Part One.srt

  11. RNN on a Time Series - Part Two.srt

  12. RNN Exercise.srt

  13. RNN Exercise - Solutions.srt

  14. Bonus - Multivariate Time Series - RNN and LSTMs.srt

  3. Vanishing Gradients.mp4

  4. LSTMS and GRU.mp4

  4.1 Great Blog Post on Exploring LSTM Neurons.html

  4.3 Wikipedia Article Describing LSTM Variants.html

  5. RNN Batches.mp4

  6. RNN on a Sine Wave - The Data.mp4

  7. RNN on a Sine Wave - Batch Generator.mp4

  8. RNN on a Sine Wave - Creating the Model.mp4

  9. RNN on a Sine Wave - LSTMs and Forecasting.mp4

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