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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

DataHack Radio: All you Need to Know about TensorFlow with Google’s Paige Bailey

Pranav Dar
Pranav Dar Last Updated : 17 Mar, 2020
5 min read

Introduction

TensorFlow is easily the most widely used deep learning framework right now. The idea behind TensorFlow (TF) has even spawned multiple products, such as TensorFlow.js, Swift for TensorFlow, TensorFlow Lite, among other things.

Among all the deep learning frameworks we’ve used in the last few years, none have seen the incredible adoption rate and vast improvement as much as TensorFlow. It has all culminated in the latest release, TensorFlow 2.0, a flexible and super-charged version of the original framework.

And who better to talk about that, plus all things TensorFlow, than TF’s very own Product Manager? We are delighted to welcome Paige Bailey on the latest episode of the DataHack Radio podcast!

Paige and Kunal cover an expansive list of TensorFlow-related topics in this podcast, including:

  • Paige’s role at Google on the TensorFlow team
  • The evolution of TensorFlow
  • Swift for TensorFlow
  • A few really cool examples of TensorFlow in action, and much more!

tensorflow_paige_bailey

You can subscribe to Analytics Vidhya’s DataHack Radio podcast on any of the below platforms:

 

Paige Bailey’s Background – Data Science Journey So Far

“My background is a little different than most machine learning engineers.”

I’m quite intrigued by Paige’s journey before she landed a data science role. Her path clearly emphasizes the diverse background people can take to get into this field. You don’t need an exclusive engineering degree, folks!

Paige did her graduation in Geophysics and applied mathematics from Rice University. All of her research was planetary-science focused (including a couple of NASA projects).

The third internship she did is quite interesting. It was in the oil and gas industry at Chevron where she was introduced to the world of data science. Chevron offered her a full-time role as a data scientist and it all took off from there.

“I went from being a planetary scientist to being a data scientist in the oil and gas industry. My graduate work was focused on computer science and carbonate geology. I then had an opportunity to market Microsoft doing machine learning (in the Azure team).”

Around October of last year, another opportunity came up – this one even more fascinating. Paige was offered a role in Google’s TensorFlow team to help build out open source tooling. And she has recently transitioned into her new role – Product Manager of TensorFlow

Quite a journey!

 

Paige’s Role at Google on the TensorFlow Team – From a Developer Advocate to a Product Manager

Paige started off as a Developer Advocate at Google (for TensorFlow) last year. Curious what this role entails? We were too! Her day-to-day responsibilities included:

  • Going to data science and developer conferences
  • Talking about TensorFlow and it’s various aspects
  • To be ever-present on GitHub
  • Talking one-on-one to developers about their experience using TensorFlow (primarily non-Google developers)
  • Take this feedback back to the product teams

“There are a lot of similarities between being a developer advocate versus being a product manager.”

Being a product manager is quite a hands-on experience. Paige has smoothly transitioned to this role and is directly responsible for the success of the product she is working on (perks of being a product manager!).

Tensorflow

She is currently looking after Swift for TensorFlow – a language with massive potential and a burgeoning developer community. Jeremy Howard is a huge advocate of Swift for TensorFlow so if you needed a reason to check it out – there you go.

“The most effective product managers have a lot of years of engineering under their belt. What you are responsible for is deciding what the strategic direction of a product should be.”

 

The Evolution of TensorFlow

It’s no secret that TensorFlow was painfully slow when it was open-sourced back in 2015. It did open up a lot of possibilities of working with deep learning techniques but the biggest complaint from data scientists and developers pinpointed the biggest issue.

This is where Keras came in – and made TensorFlow a much more viable option to work with.

“Having Keras as the recommended high-level API and building it directly into TensorFlow 2.0 was a great path forward.”

 

Features like Eager Execution and reducing duplication across the codebase has happened directly from user feedback. Another great example of Google listening to its user community and implementing what is required.

We can now scale a complete deep learning model using Keras and just two lines of code. TensorFlow 2.0 is a huge step forward and I highly recommend checking it out today.

 

Non-Typical Examples of TensorFlow in Action

We’re all familiar with the typical use cases of TensorFlow, right? But how about a few examples which took even the TensorFlow team by surprise? Here are some use cases that stood out for Paige:

  • A group of developers came up with a reinforcement learning library called huskarl using TensorFlow at its core!
  • A cool project where you ask a question listing out the symptoms you have and the model (using TensorFlow) scrapes through a bunch of different doctor recommendations
  • A graduate student built a Keras model to classify asteroids and exoplanets
  • Automating the popular Flappy Bird game
  • Using Generative Adversarial Networks (GANs) to create textured experiences for games

There are many more examples of surprising TensorFlow applications that don’t necessarily come to our minds initially. If you have worked on any such projects, make sure you tell us in the comments section below.

 

Swift for TensorFlow

A very basic but curious question I’ve come across (and wondered myself) – why Swift for TensorFlow?

Swift was created by Chris Lattner, who is also part of the TensorFlow team. He has also led the MLIR project for TensorFlow. That gives us a nice hint as to why these two behemoths were combined. Chris is well-known as the main author of LLVM as well.

If you’ve ever used Swift before, you’ll instantly connect with how Paige thinks about it:

“Swift looks a great deal like Python but it’s also supremely fast.”

You can watch Chris presenting Swift for TensorFlow in detail at the TensorFlow Dev Summit earlier this year:

 

Paige has gives us an in-depth look at Swift for TensorFlow in this podcast so make sure you tune in to this section. This could potentially be the next big machine learning tool so you really don’t want to miss out on it.

I’m a data visualization fan so I was especially taken by the ‘swiftplot‘ library – Swift for TF’s answer to matplotlib. You should also check out the official Swift for TensorFlow page to get started with the tool.

 

Resources Mentioned in the Podcast

 

End Notes

TensorFlow 2.0 is a flexible and useful framework that I’ve so far enjoyed working with. If you’ve been hesitant to try out TensorFlow – this is the right time to at least give it a whirl.

If you have any questions related to TensorFlow and its related products, feel free to let us know in the comments section below. And do share your experience with TensorFlow 2.0 or Swift for TF!

data science podcastDataHack RadioGoogle Tensorflowpaige baileytensorflowtensorflow podcast
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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