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TensorFlow and the Google Cloud ML Engine for Deep Learning
You, This Course and Us
You, This Course and Us (2:38)
Source Code and PDFs
Datasets for all Labs
Installation
Install TensorFlow (6:24)
Install Jupyter Notebook (4:38)
Running on the GCP vs. Running on your local machine
Lab: Setting Up A GCP Account (6:59)
Lab: Using The Cloud Shell (6:01)
Datalab ~ Jupyter (3:00)
Lab: Creating And Working On A Datalab Instance (4:01)
TensorFlow and Machine Learning
Introducing Machine Learning (8:04)
Representation Learning (10:27)
Neural Networks Introduced (7:35)
Introducing TensorFlow (7:16)
Running on the GCP vs. Running on your local machine
Lab: Simple Math Operations (8:46)
Computation Graph (10:17)
Tensors (9:02)
Lab: Tensors (5:03)
Linear Regression Intro (9:57)
Placeholders and Variables (8:44)
Lab: Placeholders (6:36)
Lab: Variables (7:49)
Lab: Linear Regression with Made-up Data (4:52)
Quiz 1: TensorFlow Basics
Working with Images
Image Processing (8:05)
Images As Tensors (8:16)
Lab: Reading and Working with Images (8:05)
Lab: Image Transformations (6:37)
Quiz 2: Images
K-Nearest-Neighbors with TensorFlow
Introducing MNIST (4:13)
K-Nearest Neigbors as Unsupervised Learning (7:42)
One-hot Notation and L1 Distance (7:31)
Steps in the K-Nearest-Neighbors Implementation (9:32)
Lab: K-Nearest-Neighbors (14:14)
Quiz 3: MNIST with K-Nearest Neighbors
Linear Regression with a Single Neuron
Learning Algorithm (10:58)
Individual Neuron (9:52)
Learning Regression (7:51)
Learning XOR (10:26)
XOR Trained (11:11)
Linear Regression in TensorFlow
Lab: Access Data from Yahoo Finance (2:49)
Non TensorFlow Regression (8:05)
Lab: Linear Regression - Setting Up a Baseline (11:18)
Gradient Descent (9:56)
Lab: Linear Regression (14:42)
Lab: Multiple Regression in TensorFlow (9:15)
Quiz 4: Linear Regression
Logistic Regression in TensorFlow
Logistic Regression Introduced (10:16)
Linear Classification (5:25)
Lab: Logistic Regression - Setting Up a Baseline (7:33)
Logit (8:33)
Softmax (11:55)
Argmax (12:13)
Lab: Logistic Regression (16:56)
Quiz 5: Logistic Regression
The Estimator API
Estimators (4:10)
Lab: Linear Regression using Estimators (7:49)
Lab: Logistic Regression using Estimators (4:54)
Quiz 6: Estimators
Neural Networks and Deep Learning
Traditional Machine Learning (6:24)
Deep Learning (9:23)
Operation of a Single Neuron (8:17)
The Activation Function (10:41)
Training a Neural Network: Back Propagation (6:40)
Lab: Automobile Price Prediction - Exploring the Dataset (11:13)
Lab: Automobile Price Prediction - Using TensorFlow for Prediction (14:35)
Hyperparameters (6:27)
Vanishing and Exploding Gradients (12:10)
The Bias-Variance Trade-off (8:26)
Preventing Overfitting (7:36)
Lab: Iris Flower Classification (12:08)
Quiz 7: Neural Networks and Deep Learning
Classifiers and Classification
Classification as an ML Problem (7:49)
Confusion Matrix: Accuracy, Precision and Recall (12:38)
Decision Thresholds and The Precision-Recall Trade-off (10:44)
F1 Scores and The ROC Curve (7:45)
Quiz 8: Classification
Convolutional Neural Networks (CNNs)
Mimicking the Visual Cortex (5:07)
Convolution (6:43)
Choice of Kernel Functions (4:47)
Zero Padding and Stride Size (5:47)
CNNs vs DNNs (7:15)
Feature Maps (9:29)
Pooling (6:14)
Lab: Classification of Street View House Numbers - Exploring the Dataset (10:37)
Basic Architecture of a CNN (7:07)
Lab: Classification of Street View House Numbers - Building the Model (12:52)
Lab: Classification of Street View House Numbers - Running the Model (7:35)
Lab: Building a CNN Using the Estimator API (12:19)
Quiz 9: Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Learning From the Past (8:31)
Unrolling an RNN Cell Through Time (6:54)
Training an RNN - Back Propagation Through Time (8:23)
Lab: RNNs for Image Classifcation (14:21)
Vanishing and Exploding Gradients in an RNN (7:05)
Long Memory Neurons vs Truncated BPTT (6:03)
The Long/Short Term Memory Cell (6:28)
A Sequence of Words (6:35)
Text in Numeric Form (15:08)
Lab: Sentiment Analysis on Rotten Tomatoes Reviews - Exploring the Dataset (10:35)
Lab: Sentiment Analysis on Rotten Tomatoes Reviews - Building, Running the Model (11:20)
Quiz 10: Recurrent Neural Networks (RNNs)
Unsupervised Learning
Supervised and Unsupervised Learning (11:30)
Expressing Attributes as Numbers (5:33)
K-Means Clustering (15:14)
Lab: K-Means Clustering with 2-Dimensional Points in Space (8:51)
Lab: K-Means Clustering with Images (10:19)
Patterns in Data (3:19)
Principal Components Analysis (13:19)
Autoencoders (5:03)
Autoencoder Neural Network Architecture (9:04)
Lab: PCA on Stock Data - Matplotlib vs Autoencoders (14:15)
Stacked Autoencoders (4:27)
Lab: Stacked Autoencoder With Dropout (7:51)
Lab: Stacked Autoencoder With Regularization and He Initialization (6:14)
Denoising Autoencoders (1:26)
Lab: Denoising Autoencoder with Gaussian Noise (1:58)
Quiz 11: Unsupervised Learning
TensorFlow on the Google Cloud
Running TensorFlow on the Cloud
Lab: Taxicab Prediction - Setting up the dataset (14:38)
Lab: Taxicab Prediction - Training and Running the model (11:22)
Quiz 12: GCP Basics
TensorFlow Using Cloud ML Engine
A Taxicab Fare Prediction Problem (3:25)
Datalab (7:03)
Querying BigQuery (5:23)
Explore Data (6:03)
Clean Data (4:47)
Benchmark (5:44)
Using TensorFlow (8:22)
The Estimator API (8:47)
The Experiment Function (5:48)
Introduction to Cloud MLE (7:53)
Using Cloud MLE (8:05)
The Training Service (6:24)
The Prediction Service (7:53)
Quiz 13: Cloud ML Engine
Feature Engineering and Hyperparameter Tuning
Feature Engineering to the rescue (1:04)
New Approach (6:43)
Dataflow Create Pipeline (7:10)
Dataflow Run Pipeline (5:04)
Feature Engineering (8:34)
Deep And Wide Models (9:16)
Hyperparameter Tuning (7:34)
Hyperparameter Tuning on the GCP (6:35)
Quiz 14: Feature Engineering and Hyperparameter Tuning
Lab: Stacked Autoencoder With Dropout
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