Building Classification Models with TensorFlow
This course covers the finer points of building such models as well the logistic regression, nearest-neighbor methods, and metrics for evaluating classifiers such as accuracy, precision, and recall.
What you'll learn
TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this course, Building Classification Models with TensorFlow, you'll learn a variety of different machine learning techniques to build classification models. First, you'll begin by covering metrics, such as accuracy, precision, and recall that can be used to evaluate classification models and determine which metric is the right one for your use case. Next, you'll delve into more traditional machine learning techniques such as logistic regression and the k-nearest neighbor methods for classification. Finally, you'll discover how to implement more powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. By the end of this course, you'll have a better understanding of how to build classification models with TensorFlow.
Table of contents
- Version Check 0m
- Prerequisites and Software Needed for This Course 4m
- Classification and Classifiers 6m
- Using Accuracy to Evaluate Models 4m
- Using Precision and Recall to Evaluate Models 2m
- The Precision/Recall Tradeoff 5m
- The Precision-Recall Tradeoff 4m
- Binary, Multilabel, Multiclass, and Multioutput Classifiers 5m
- Representing Images as Tensors 4m
- The K-nearest Neighbors Algorithm 3m
- Distance Measures 2m
- Demo: Environment and Package Setup 2m
- Demo: Image Classification Using K-nearest Neighbors 11m
- The Intuition Behind Logistic Regression 6m
- Logistic Regression for Prediction 2m
- Cross-entropy as a Cost Function 2m
- Demo: Exploring the Census Dataset 4m
- Feature Engineering with Bucketized and Crossed Columns 3m
- Working with Estimators in TensorFlow 4m
- Demo: Income Prediction Using Logistic Regression 7m
- Neurons and Neural Networks 6m
- Understanding How Convolution Works 6m
- Zero Padding and Stride Size 4m
- Introducing Convolutional Neural Networks 3m
- Convolutional Layers and Feature Maps 6m
- Pooling Layers 4m
- Architecture of CNNs 6m
- Demo: Image Classification Using CNNs (MNIST Dataset) 11m
- Demo: Exploring the CIFAR-10 Dataset 5m
- Demo: Image Classification Using CNNs (CIFAR-10 Dataset) 8m
- Why Is the Past Important? 4m
- Understaning the Recurrent Neuron 5m
- Training Using Back Propogation 5m
- Demo: Classifying Images Using RNNs (MNIST Dataset) 4m
- Dealing with Vanishing and Exploding Gradients 6m
- The LSTM Memory Cell 6m
- Word Vector Encodings 8m
- Demo: Exploring the DBPedia Dataset for Text Classification 7m
- Demo: Text Classification Using RNNs 8m
- Summary and Next Steps for Learning 1m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providin... more
