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Course curriculum

1
Introduction

2
Installation

3
TensorFlow and Machine Learning

4
Working with Images

5
KNearestNeighbors with TensorFlow

6
Linear Regression with a Single Neuron

7
Linear Regression in TensorFlow

8
Logistic Regression in TensorFlow

9
The Estimator API

10
Neural Networks and Deep Learning

11
Classifiers and Classification

12
Convolutional Neural Networks (CNNs)

13
Recurrent Neural Networks (RNNs)

14
Unsupervised Learning

15
TensorFlow on the Google Cloud

16
TensorFlow Using Cloud ML Engine

17
Feature Engineering and Hyperparameter Tuning

You, This Course and Us Source Code and PDFs Datasets for all Labs Downloads 
Install TensorFlow Install Jupyter Notebook Running on the GCP vs. Running on your local machine Lab: Setting Up A GCP Account Lab: Using The Cloud Shell Datalab ~ Jupyter Lab: Creating And Working On A Datalab Instance Downloads 
Introducing Machine Learning Representation Learning Neural Networks Introduced Introducing TensorFlow Running on the GCP vs. Running on your local machine Lab: Simple Math Operations Computation Graph Tensors Lab: Tensors Linear Regression Intro Placeholders and Variables Lab: Placeholders Lab: Variables Lab: Linear Regression with Madeup Data 
Image Processing Images As Tensors Lab: Reading and Working with Images Lab: Image Transformations 
Introducing MNIST KNearest Neigbors Onehot Notation and L1 Distance Steps in the KNearestNeighbors Implementation Lab: KNearestNeighbors 
Learning Algorithm Individual Neuron Learning Regression Learning XOR XOR Trained 
Lab: Access Data from Yahoo Finance Non TensorFlow Regression Lab: Linear Regression  Setting Up a Baseline Gradient Descent Lab: Linear Regression Lab: Multiple Regression in TensorFlow 
Logistic Regression Introduced Linear Classification Lab: Logistic Regression  Setting Up a Baseline Logit Softmax Argmax Lab: Logistic Regression 
Estimators Lab: Linear Regression using Estimators Lab: Logistic Regression using Estimators 
Traditional Machine Learning Deep Learning Operation of a Single Neuron The Activation Function Training a Neural Network: Back Propagation Lab: Automobile Price Prediction  Exploring the Dataset Lab: Automobile Price Prediction  Using TensorFlow for Prediction Hyperparameters Vanishing and Exploding Gradients The BiasVariance Tradeoff Preventing Overfitting Lab: Iris Flower Classification 
Classification as an ML Problem Confusion Matrix: Accuracy, Precision and Recall Confusion Matrix: Accuracy, Precision and Recall F1 Scores and The ROC Curve 
Mimicking the Visual Cortex Convolution Choice of Kernel Functions Zero Padding and Stride Size CNNs vs DNNs Feature Maps Pooling Lab: Classification of Street View House Numbers  Exploring the Dataset Basic Architecture of a CNN Lab: Classification of Street View House Numbers  Building the Model Lab: Classification of Street View House Numbers  Running the Model Lab: Building a CNN Using the Estimator API 
Learning From the Past Unrolling an RNN Cell Through Time Training an RNN  Back Propagation Through Time Lab: RNNs for Image Classifcation Vanishing and Exploding Gradients in an RNN Long Memory Neurons vs Truncated BPTT The Long/Short Term Memory Cell A Sequence of Words Text in Numeric Form Lab: Sentiment Analysis on Rotten Tomatoes Reviews  Exploring the Dataset Lab: Sentiment Analysis on Rotten Tomatoes Reviews  Building, Running the Model 
Supervised and Unsupervised Learning Expressing Attributes as Numbers KMeans Clustering Lab: KMeans Clustering with 2Dimensional Points in Space Lab: KMeans Clustering with Images Patterns in Data Principal Components Analysis Autoencoders Autoencoder Neural Network Architecture Lab: PCA on Stock Data  Matplotlib vs Autoencoders Stacked Autoencoders Lab: Stacked Autoencoder With Dropout Lab: Stacked Autoencoder With Regularization and He Initialization Denoising Autoencoders Lab: Denoising Autoencoder with Gaussian Noise 
Running TensorFlow on the Cloud Lab: Taxicab Prediction  Setting up the dataset Lab: Taxicab Prediction  Training and Running the model Downloads 
A Taxicab Fare Prediction Problem Datalab Querying BigQuery Explore Data Clean Data Benchmark Using TensorFlow The Estimator API The Experiment Function Introduction to Cloud MLE Using Cloud MLE The Training Service The Prediction Service 
Feature Engineering to the rescue New Approach Dataflow Create Pipeline Dataflow Run Pipeline Feature Engineering Deep And Wide Models Hyperparameter Tuning Hyperparameter Tuning on the GCP
Course Description
What will I learn?
 Build and execute machine learning models on TensorFlow
 Implement Deep Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks
 Understand and implement unsupervised learning models such as Clustering and Autoencoders
About the course
TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction.
This is a comprehensive, fromthebasics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming.
What's covered:
 Deep learning basics: What a neuron is; how neural networks connect neurons to 'learn' complex functions; how TF makes it easy to build neural network models
 Using Deep Learning for the famous ML problems: regression, classification, clustering and autoencoding
 CNNs  Convolutional Neural Networks: Kernel functions, feature maps, CNNs v DNNs
 RNNs  Recurrent Neural Networks: LSTMs, Backpropagation through time and dealing with vanishing/exploding gradients
 Unsupervised learning techniques  Autoencoding, Kmeans clustering, PCA as autoencoding
 Working with images
 Working with documents and word embeddings
 Google Cloud ML Engine: Distributed training and prediction of TF models on the cloud
 Working with TensorFlow estimators
Who should take the course?
 Developers who want to understand and build ML and deep learning models in TensorFlow
 Data scientists who want to learn cutting edge TensorFlow technology
Prerequisites & Requirements
 Basic proficiency at programming in Python
 Basic understanding of machine learning models is useful but not required