Tag: Deep Learning

Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs

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Course content

  • Introduction
  • Intro to Computer Vision & Deep Learning
  • Installation Guide
  • Handwriting Recognition
  • OpenCV Tutorial – Learn Classic Computer Vision & Face Detection (OPTIONAL)
  • Neural Networks Explained
  • Convolutional Neural Networks (CNNs) Explained
  • Build CNNs in Python using Keras
  • What CNNs ‘see’ – Filter Visualizations, Heatmaps and Salience Maps
  • Data Augmentation: Cats vs Dogs
  • Assessing Model Performance
  • Optimizers, Learning Rates & Callbacks with Fruit Classification
  • Batch Normalization & LeNet, AlexNet: Clothing Classifier
  • Advanced Image Classiers – ImageNet in Keras (VGG16/19, InceptionV3, ResNet50)
  • Transfer Learning: Build a Flower & Monkey Breed Classifier
  • Design Your Own CNN – LittleVGG: A Simpsons Classifier
  • Advanced Activation Functions & Initializations
  • Facial Applications – Emotion, Age & Gender Recognition
  • Medical Imaging – Image Segmentation with U-Net
  • Principles of Object Detection
  • TensorFlow Object Detection API
  • Object Detection with YOLO & Darkflow: Build a London Underground Sign Detector
  • DeepDream & Neural Style Transfers: Make A1 Generated Art
  • Generative Adversarial Networks (GANs): Simulate Aging Faces
  • Face Recognition with VGGFace
  • The Computer Vision World
  • BONUS – Build a Credit Card Number Reader
  • BONUS – Use Cloud GPUs on PaperSpace
  • BONUS – Create a Computer Vision API & Web App Using Flask and AWS

Time Series Analysis, Forecasting, and Machine Learning

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Course content

  • Welcome
  • Getting Set Up
  • Time Series Basics
  • Exponential Smoothing and ETS Methods
  • ARIMA
  • Vector Autoregression (VAR, VMA, VARMA)
  • Machine Learning Methods
  • Deep Learning: Artificial Neural Networks (ANN)
  • Deep Learning: Convolutional Neural Networks (CNN)
  • Deep Learning: Recurrent Neural Networks (RNN)
  • VIP: GARCH
  • VIP: AWS Forecast
  • VIP: Facebook Prophet
  • Setting Up Your Environment FAQ
  • Extra Help With Python Coding for Beginners FAQ
  • Effective Learning Strategies for Machine Learning FAQ
  • Appendix / FAQ Finale

Anomaly Detection: Machine Learning, Deep Learning, AutoML

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Course content

  • Introduction
  • The Three Types of Anomalies
  • Anomaly Detection – Time Series
  • Anomaly Detection –
  • Unsupervised DBSCAN
  • Anomaly Detection – Unsupervised Isolation Forest
  • Anomaly Detection – Supervised
  • Anomaly Detection – Images
  • Anomaly Detection Using Deep Learning
  • PyOD: A comparison of 10 algorithms
  • Predicting High Impact Low Volume Events: Predictive Maintenance
  • No Code (AutoML) approach to anomaly detection using PowerBl
  • Machine Learning
  • Bonus Lecture

Machine Learning, Deep Learning + AWS Sagemaker

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Course content

  • Introduction
  • Basic python + Pandas + Plotting
  • Machine Learning: Numpy + Scikit Learn
  • Machine Learning: Classification + Time Series + Model Diagnostics
  • Unsupervised Learning
  • Natural Language Processing + Regularization
  • Deep Learning
  • Deep Learning (TensorFIow) – Convolutional Neural Nets
  • Deep Learning: Recurrent Neural Nets
  • Deep Learning: PyTorch Introduction
  • Deep Learning: Transfer Learning with PyTorch Lightning
  • Pixel Level Segmentation (Semantic Segmentation) with PyTorch
  • Deep Learning: Transformers and BERT
  • Bayesian Learning and probabilistic programming
  • Model Deployment
  • AWS Sagemaker (for Model Deployment)
  • Final Thoughts

Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

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Course content

  • Introduction
  • Fundamentals of Reinforcement Learning
  • Deep Learning Crash Course
  • Human Level Control Through Deep Reinforcement Learning: From Paper to Code
  • Deep Reinforcement Learning with Double Q Learning
  • Dueling Network Architectures for Deep Reinforcement Learning
  • Improving On Our Solutions
  • Conclusion
  • Bonus Lecture
  • Tensorflow 2 Implementations
  • Appendix

Deep Learning with PyTorch for Medical Image Analysis

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Course content

  • Introduction
  • Crash Course: NumPy
  • Machine Learning Concepts Overview
  • PyTorch Basics
  • CNN – Convolutional Neural Networks
  • Medical Imaging – A short Introduction
  • Data Formats in Medical Imaging
  • Pneumonia-Classification
  • Cardiac-Detection
  • Atrium-Segmentation
  • Capstone-Project: Lung Tumor Segmentation
  • 3D Liver and Liver Tumor Segmentation
  • BONUS SECTION: THANK YOU!

The Complete Self-Driving Car Course – Applied Deep Learning

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Course content

  • Introduction
  • Installation
  • Python Crash Course (Optional)
  • NumPy Crash Course (Optional)
  • Computer Vision: Finding Lane Lines
  • The Perceptron
  • Keras
  • Deep Neural Networks
  • Multiclass Classification
  • MN 1ST Image Recognition
  • Convolutional Neural Networks
  • Classifying Road Symbols
  • Polynomial Regression
  • Behavioural Cloning

Deep Learning: Convolutional Neural Networks in Python

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Course content

  • Welcome
  • Google Colab
  • Machine Learning and Neurons
  • Feedforward Artificial Neural Networks
  • Convolutional Neural Networks
  • Natural Language Processing (NLP)
  • Convolution In-Depth
  • Convolutional Neural Network Description
  • Practical Tips
  • In-Depth: Loss Functions
  • In-Depth: Gradient Descent
  • Setting Up Your Environment (FAQ by Student Request)
  • Extra Help With Python Coding for Beginners (FAQ by Student Request)
  • Effective Learning Strategies for Machine Learning (FAQ by Student Request)
  • Appendix / FAQ Finale

The Data Science Course: Complete Data Science Bootcamp 2024

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Course content

  • Part 1: Introduction
  • The Field of Data Science – The Various Data Science Disciplines
  • The Field of Data Science – Connecting the Data Science Disciplines
  • The Field of Data Science – The Benefits of Each Discipline
  • The Field of Data Science – Popular Data Science Techniques
  • The Field of Data Science – Popular Data Science Tools
  • The Field of Data Science – Careers in Data Science
  • The Field of Data Science – Debunking Common Misconceptions
  • Part 2: Probability
  • Probability – Combinatorics
  • Probability – Bayesian Inference
  • Probability – Distributions
  • Probability – Probability in Other Fields
  • Part 3: Statistics
  • Statistics – Descriptive Statistics
  • Statistics – Practical Example: Descriptive Statistics
  • Statistics – Inferential Statistics Fundamentals
  • Statistics – Inferential Statistics: Confidence Intervals
  • Statistics – Practical Example: Inferential Statistics
  • Statistics – Hypothesis Testing
  • Statistics – Practical Example: Hypothesis Testing
  • Part 4: Introduction to Python
  • Python – Variables and Data Types
  • Python – Basic Python Syntax
  • Python – Other Python Operators
  • Python – Conditional Statements
  • Python – Python Functions
  • Python – Sequences
  • Python – Iterations
  • Python – Advanced Python Tools
  • Part 5: Advanced Statistical Methods in Python
  • Advanced Statistical Methods – Linear Regression with StatsModels
  • Advanced Statistical Methods – Multiple Linear Regression with StatsModels
  • Advanced Statistical Methods – Linear Regression with sklearn
  • Advanced Statistical Methods – Practical Example: Linear Regression
  • Advanced Statistical Methods – Logistic Regression
  • Advanced Statistical Methods – Cluster Analysis
  • Advanced Statistical Methods – K-Means Clustering
  • Advanced Statistical Methods – Other Types of Clustering
  • Part 6: Mathematics
  • Part 7: Deep Learning
  • Deep Learning – Introduction to Neural Networks
  • Deep Learning – How to Build a Neural Network from Scratch with NumPy
  • Deep Learning – TensorFlow 2.0: Introduction
  • Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
  • Deep Learning – Overfitting
  • Deep Learning – Initialization
  • Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
  • Deep Learning – Preprocessing
  • Deep Learning – Classifying on the MNIST Dataset
  • Deep Learning – Business Case Example
  • Deep Learning – Conclusion
  • Appendix: Deep Learning – TensorFlow 1: Introduction
  • Appendix: Deep Learning – TensorFlow 1: Classifying on the MN 1ST Dataset
  • Appendix: Deep Learning – TensorFlow 1: Business Case
  • Software Integration
  • Case Study – What’s Next in the Course?
  • Case Study – Preprocessing the ‘Absenteeism_data’
  • Case Study – Applying Machine Learning to Create the ‘absenteeism module’
  • Case Study – Loading the ‘absenteeism _ module’
  • Case Study – Analyzing the Predicted Outputs in Tableau
  • Appendix – Additional Python Tools
  • Appendix – pandas Fundamentals
  • Appendix – Working with Text Files in Python
  • Bonus Lecture