Here is the schedule for when I plan to release each of the course videos this summer. I am planning on finishing video recording before the fall semester starts on August 26. If need be, I can delay some of the final models to inside of the semester! However, for now, this is the goal. Each video will be relased at 12 noon (USA central time, GMT-5).
Subscribe to my YouTube channel to be updated when each is released.
- Module 1: Python Preliminaries
Part 1.1: Course Overview (will record at the end)
May 21, 2019: Part 1.2: Introduction to Python
May 22, 2019: Part 1.3: Python Lists, Dictionaries, Sets & JSON
May 23, 2019: Part 1.4: File Handling
May 24, 2019: Part 1.5: Functions, Lambdas, and Map/Reduce - Module 2: Python for Machine Learning
May 27, 2019: Part 2.1: Introduction to Pandas for Deep Learning
May 28, 2019: Part 2.2: Encoding Categorical Values in Pandas
May 29, 2019: Part 2.3: Grouping, Sorting, and Shuffling
May 30, 2019: Part 2.4: Using Apply and Map in Pandas
May 31, 2019: Part 2.5: Feature Engineering in Padas - Module 3: TensorFlow and Keras for Neural Networks
June 3, 2019: Part 3.1: Deep Learning and Neural Network Introduction
June 4, 2019: Part 3.2: Introduction to Tensorflow & Keras
June 5, 2019: Part 3.3: Saving and Loading a Keras Neural Network
June 6, 2019: Part 3.4: Early Stopping in Keras to Prevent Overfitting
June 7, 2019: Part 3.5: Extracting Keras Weights and Manual Neural Network Calculation - Module 4: Training for Tabular Data
June 10, 2019: Part 4.1: Encoding a Feature Vector for Keras Deep Learning
June 11, 2019: Part 4.2: Keras Multiclass Classification for Deep Neural Networks with ROC and AUC
June 12, 2019: Part 4.3: Keras Regression for Deep Neural Networks with RMSE
June 13, 2019: Part 4.4: Backpropagation, Nesterov Momentum, and ADAM Training
June 14, 2019: Part 4.5: Neural Network RMSE and Log Loss Error Calculation from Scratch - Module 5: Regularization and Dropout
June 17, 2019: Part 5.1: Introduction to Regularization: Ridge and Lasso
June 18, 2019: Part 5.2: Using K-Fold Cross Validation with Keras
June 19, 2019: Part 5.3: Using L1 and L2 Regularization with Keras to Decrease Overfitting
June 20, 2019: Part 5.4: Drop Out for Keras to Decrease Overfitting
June 21, 2019: Part 5.5: Bootstrapping and Benchmarking Hyperparameters - Module 6: CNN for Vision
June 24, 2019: Part 6.1: Image Processing in Python
June 25, 2019: Part 6.2: Keras Neural Networks for MINST and Fashion MINST
June 26, 2019: Part 6.3: Implementing a ResNet in Keras
June 27, 2019: Part 6.4: Using your own Images with Keras
June 28, 2019: Part 6.5: Recognizing Multiple Images with Darknet - Module 7: GAN
July 1, 2019: Part 7.1: Introduction to GANS for Image and Data Generation
July 2, 2019: Part 7.2: Implementing a GAN in Keras
July 3, 2019: Part 7.3: Face Generation with StyleGAN and Python
July 4, 2019: Part 7.4: GANS for Semi-Supervised Learning in Keras
July 5, 2019: Part 7.5: An Overview of GAN Research - Module 8: Kaggle
July 8, 2019: Part 8.1: Introduction to Kaggle
July 9, 2019: Part 8.2: Building Ensembles with Scikit-Learn and Keras
July 10, 2019: Part 8.3: How Should you Architect Your Keras Neural Network: Hyperparameters
July 11, 2019: Part 8.4: Bayesian Hyperparameter Optimization for Keras
July 12, 2019: Part 8.5: Current Semester’s Kaggle - Module 9: Transfer Learning
July 15, 2019: Part 9.1: Introduction to Keras Transfer Learning
July 16, 2019: Part 9.2: Popular Pretrained Neural Networks for Keras.
July 17, 2019: Part 9.3: Transfer Learning for Computer Vision and Keras
July 18, 2019: Part 9.4: Transfer Learning for Languages and Keras
July 19, 2019: Part 9.5: Transfer Learning for Keras Feature Engineering - Module 10: Time Series in Keras
July 22, 2019: Part 10.1: Time Series Data Encoding for Deep Learning, TensorFlow and Keras
July 23, 2019: Part 10.2: Programming LSTM with Keras and TensorFlow
July 24, 2019: Part 10.3: Image Captioning with Keras and TensorFlow
July 25, 2019: Part 10.4: Temporal CNN in Keras and TensorFlow
July 26, 2019: Part 10.5: Predicting the Stock Market with Keras and TensorFlow - Module 11: Natural Language Processing
July 29, 2019: Part 11.1: Getting Started with Spacy in Python
July 30, 2019: Part 11.2: Word2Vec and Text Classification
July 31, 2019: Part 11.3: Natural Language Processing with Spacy and Keras
August 1, 2019: Part 11.4: What are Embedding Layers in Keras
August 2, 2019: Part 11.5: Learning English from Scratch with Keras and TensorFlow - Module 12: Reinforcement Learning
August 5, 2019: Part 12.1: Introduction to the OpenAI Gym
August 6, 2019: Part 12.2: Introduction to Q-Learning for Keras
August 7, 2019: Part 12.3: Keras Q-Learning in the OpenAI Gym
August 8, 2019: Part 12.4: Atari Games with Keras Neural Networks
August 9, 2019: Part 12.5: How Alpha Zero used Reinforcement Learning to Master Chess - Module 13: Deployment and Monitoring
August 12, 2019: Part 13.1: Deploying a Model to AWS
August 13, 2019: Part 13.2: Flask and Deep Learning Web Services
August 14, 2019: Part 13.3: AI at the Edge: Using Keras on a Mobile Device
August 15, 2019: Part 13.4: When to Retrain Your Neural Network
August 16, 2019: Part 13.5: Using a Keras Deep Neural Network with a Web Application - Module 14: Other Neural Network Techniques
August 19, 2019: Part 14.1: What is AutoML
August 20, 2019: Part 14.2: Using Denoising AutoEncoders in Keras
August 21, 2019: Part 14.3: Anomaly Detection in Keras
August 22, 2019: Part 14.4: Training an Intrusion Detection System with KDD99
August 23, 2019: Part 14.5: The Deep Learning Technologies I am Excited About
August 26, 2019 - First day of fall semester class!