Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: Join us!

In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson’s Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with’s software.

Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 ‘Magic Grant’ from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.

Learn about the latest and greatest in machine learning (ML) from Google! We cover what’s available to developers when it comes to creating, understanding, and deploying models for a variety of different applications. From Responsible AI to TensorFlow 2.5, mobile devices, microcontrollers, and beyond. We cover new releases and tools, and you hear about the latest from the Google Cloud Platform, and how to enable an end-to-end machine learning pipeline.

ML Kit: turnkey APIs to use on-device ML in mobile apps →
TensorFlow Hub for real world impact →
Machine learning for next gen web apps with TensorFlow.js →

Speakers: Kemal El Moujahid, Sarah Sirajuddin, Craig Wiley

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** AI & Deep Learning with Tensorflow Training: **
This Edureka video on “Keras vs TensorFlow vs PyTorch” will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this video:
1:06 – Introduction to keras, Tensorflow, Pytorch
2:13 – Parameters of Comparison
2:18 – Level of API
3:06 – Speed
3:28 – Architecture
4:03 – Ease of Code
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Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’

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3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

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PyData LA 2018

I will present some way in which tensor methods can be combined with deep learning, and demonstrate through Jupyter notebooks on how easy it is specify tensorized neural networks.

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

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In this video, watch this special keynote talk from Hilary Mason about “The Present and Future of Artificial Intelligence and Machine Learning” during the Open Data Science Conference in Boston 2019. Hilary is a data scientist at Accel Partners, as well as the founder of technology at her startup, Fast Forward Labs.

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Today, thousands of scientists and engineers are applying machine learning to an extraordinarily broad range of domains, and over the last five decades, researchers have created literally thousands of machine learning algorithms. Traditionally an engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, and their choice is often constrained by their familiar with an algorithm, or by the availability of software implementations. In this talk we talk about ‘model-based machine learning’, a new approach in which a custom solution is formulated for each new application. We show how probabilistic graphical models, coupled with efficient inference algorithms, provide a flexible foundation for model-based machine learning, and we describe several large-scale commercial applications of this framework. We also introduce the concept of ‘probabilistic programming’ as a powerful approach to model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

See more on this video at

Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer – Stanford University

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

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Machine learning is changing the world that we live in. Top companies such as Facebook, Google, Microsoft and Amazon are looking for machine learning engineers and the average salary of a machine learning engineer is around 120k$ dollars.

Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free.

Get the free Great Learning App for a seamless experience, enroll for free courses and watch them offline by downloading them.

This full course on Machine Learning with Python will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India.

He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).

These are the topics covered in this session:

-Agenda – 0:00
-Introduction to Python and Anaconda – 3:58
-Introduction to Pandas and Data Manipulation – 1:07:05
-Introduction to Numpy and Numerical Computing – 4:42:32
-Data Visualization – 5:10:58
-Statistics vs Machine Learning – 6:06:12
-Types of Statistics – 6:12:44
-Understanding Data – 7:54:39
-What is Reinforcement Learning? – 7:58:19
-Reinforcement Learning Framework – 8:53:46
-Q-Learning – 9:24:58
-Case Study on Smart Taxi – 9:51:08
Read more on Machine Learning

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In this video, GovBrain Founder and CEO Brent M. Eastwood discusses the ethics of artificial intelligence and machine learning.

Dr. Eastwood explains how to ensure that the machine can ultimately be controlled by human beings. The importance of having ethical, moral, and virtuous human beings train the machine is paramount in this construct. Dr. Eastwood also talks about how the current status of artificial intelligence, machine learning and data science can be used to conduct a Turing Test to determine that perhaps the state of the art is not as advanced as we think. Dr. Eastwood then takes a longer look at robotics and the ethics of singularity.

This video was part of the “With the Best Artificial Intelligence Conference” in September of 2016.

On Monday, April 15, NYU Stern’s Fubon Center for Technology, Business and Innovation hosted a talk on “AI in Business: Machine Learning, Ethics, and Fairness” by Dr. Solon Barocas.

Machine learning is everywhere in today’s NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations.

Can an algorithm help you improve your penalty kick or tennis serve? In this episode of Making with Machine Learning, Dale Markowitz chats with Machine Learning Engineer Zack Akil to learn about how Google Cloud’s ML services, like Cloud AutoML vision and the Video Intelligence API, can be used to analyze, assess, and improve your game.

0:00 – Introduction
0:40 – Overview
2:05 – What was measured?
3:37 – What powered the demo?
4:12 – Problems/Challenges
4:45 – Training Auto ML Vision model
5:50 – Using it for tennis serve

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Product: Video Intelligence API, Cloud AutoML Vision; fullname: Dale Markowitz;

In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View.

As you’ll hear, I was very excited for this interview, because so many of Jeff’s contributions since he started at Google in ‘99 have touched my life and work. In our conversation, Jeff and I dig into a bunch of the core machine learning innovations we’ve seen from Google. Of course we discuss TensorFlow, and its origins and evolution at Google. We also explore AI acceleration hardware, including TPU v1, v2 and future directions from Google and the broader market in this area. We talk through the machine learning toolchain, including some things that Googlers might take for granted, and where the recently announced Cloud AutoML fits in. We also discuss Google’s process for mapping problems across a variety of domains to deep learning, and much, much more. This was definitely one of my favorite conversations, and I’m pumped to be able to share it with you.

The notes for this show can be found at

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Machine Learning Python Weather Prediction


In this video I give machine learning with python a go. And I build a machine learning model for predicting the weather in the future.
If you want to learn machine learning I do attempt to explain how machine learning works at 7:02 in the video. As kind of an intro to machine learning.

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🔥Edureka and NIT Warangal Post Graduate Program on AI and Machine Learning:
This Edureka Session explores and analyses the spread and impact of the novel coronavirus pandemic which has taken the world by storm with its rapid growth. In this session, we shall develop a machine learning model in Python to analyze what has been its impact so far and analyze the outbreak of COVID 19 across various regions, visualize them using charts and tables, and predict the number of upcoming confirmed cases.
Finally, we’ll conclude with a few safety measures that you can take to save yourself and your loved ones from getting adversely affected in the hour of crisis.
02: 53 Introduction to COVID 19 
05:49 Case Study: the outbreak of COVID 19
57:20 Conclusion

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2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
About the Course

Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger.

Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.

For more information, please write back to us at or call us at IND: 9606058406 / US: 18338555775 (toll-free).

One of the main challenges for AI remains unsupervised learning, at which humans are much better than machines, and which we link to another challenge: bringing deep learning to higher-level cognition. We review earlier work on the notion of learning disentangled representations and deep generative models and propose research directions towards learning of high-level abstractions. This follows the ambitious objective of disentangling the underlying causal factors explaining the observed data. We argue that in order to efficiently capture these, a learning agent can acquire information by acting in the world, moving our research from traditional deep generative models of given datasets to that of autonomous learning or unsupervised reinforcement learning. We propose two priors which could be used by an agent acting in its environment in order to help discover such high-level disentangled representations of abstract concepts. The first one is based on the discovery of independently controllable factors, i.e., in jointly learning policies and representations, such that each of these policies can independently control one aspect of the world (a factor of interest) computed by the representation while keeping the other uncontrolled aspects mostly untouched. This idea naturally brings fore the notions of objects (which are controllable), agents (which control objects) and self. The second prior is called the consciousness prior and is based on the hypothesis that our conscious thoughts are low-dimensional objects with a strong predictive or explanatory power (or are very useful for planning). A conscious thought thus selects a few abstract factors (using the attention mechanism which brings these variables to consciousness) and combines them to make a useful statement or prediction. In addition, the concepts brought to consciousness often correspond to words or short phrases and the thought itself can be transformed (in a lossy way) into a brief linguistic expression, like a sentence. Natural language could thus be used as an additional hint about the abstract representations and disentangled factors which humans have discovered to explain their world. Some conscious thoughts also correspond to the kind of small nugget of knowledge (like a fact or a rule) which have been the main building blocks of classical symbolic AI. This, therefore, raises the interesting possibility of addressing some of the objectives of classical symbolic AI focused on higher-level cognition using the deep learning machinery augmented by the architectural elements necessary to implement conscious thinking about disentangled causal factors.

See more at

The Future of Work: Capital Markets, Digital Assets, and the Disruption of Labor
Date: Friday, April 27, 2018

MODERATOR: Erik Brynjolffson, PhD ’91

DISCUSSANT: Daniel Kahneman, Professor of Psychology and Public Affairs Emeritus, Princeton University; Nobel Laureate; Author of Thinking, Fast and Slow

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AI With The Best hosted 50+ speakers and hundreds of attendees from all over the world on a single platform on October 14-15, 2017. The platform held live talks, Insights/Questions pages, and bookings for 1-on-1s with speakers.

We will discuss multiple ways in which healthcare data is acquired and machine learning methods are currently being introduced into clinical settings. This will include: 1) Modeling disease trends and other predictions, including joint predictions of multiple conditions, from electronic health record (EHR) data using Gaussian processes. 2) Predicting surgical complications and transfer learning methods for combining databases 3) Using mobile apps and integrated sensors for improving the granularity of recorded health data for chronic conditions and 4) The combination of mobile app and social network information in order to predict the spread of contagious disease. Current work in these areas will be presented and the future of machine learning contributions to the field will be discussed.

Katherine Heller, Duke University
Computational Challenges in Machine Learning

There are a lot of things you can do to learn Machine Learning. There are resources like books and courses you can follow, competitions you can enter and tools you can use.
And this course is one of the best awesome to begin with. It tought by an professor at Standford University.
Welcome to AI, to Machine Learning.

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Hello Everyone!!!

Let’s check out what are the 5 must-have skills to become a machine learning engineer.

First, let’s understand what machine learning is.
In simple words.,
Machine learning is all about making the computers to perform intelligent tasks without explicitly coding. This is achieved by training the computer with lots and lots of data.

For example: Detecting whether a mail is a spam or not, recognizing handwritten
digits, Fraud detection in Transactions… and many such applications…

Now let’s see what are the top 5 skills to get a machine learning job.

1). At number 1, we have
Math Skills: Under math skills, we need to know probability and statistics, linear algebra
and calculus.

Probability and Statistics: Machine learning is very much closely related to statistics.
You need to know the fundamentals of statistics and probability theory,
descriptive statistics, Baye’s rule and random variables,
probability distributions,
sampling, hypothesis testing, regression and decision analysis.

Linear Algebra: You need to know how to with matrices and some basic operations on matrices such as matrix addition,
subtraction, scalar and vector multiplication,
inverse, transpose and vector spaces.

Calculus: In calculus, you need to know the basics of differential and integral calculus.

2). At number two we have
Programming skills: A little bit of coding skills is enough. But it’s preferred to have the knowledge of data structures, algorithms and Object Oriented Programming (or OOPs) concepts.

Some of the popular programming languages to learn for machine learning is Python, R, Java, and C++.

It’s your preference to master any one programming language. But its advisable
to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one.

3). At number 3 we have
Data engineer skills: Ability to work with large amounts of data (or big data), Data preprocessing,
the knowledge of SQL and NoSQL, ETL (or Extract Transform and Load) operations,
data analysis and visualization skills.

4). Next, we have
Knowledge of Machine Learning Algorithms: you should be familiar with popular machine learning
algorithms such as linear regression, logistic
regression, decision trees, random forest, clustering (like K means, hierarchical), reinforcement learning and neural networks.

5). And Finally,
The Knowledge of Machine Learning Frameworks:
You Should be Familiar with popular machine learning frameworks such as sci-kit learn, tensorflow, Azure, caffe, theano, spark and torch.


Namaskaar Dosto, is video mein maine aapse Artificial Intelligence aur Machine Learning ke baare mein baat ki hai, Artificial Intelligence aur Machine Learning mein kya difference hai aur kaise yeh hamare kaam aati hai, yahi maine is video mein bataya hai. Mujhe umeed hai ki aapko Machine Learning aur AI ki yeh video pasand aayegi.

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We’re going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I’ll explain why we use recurrent nets for time series data, and why LSTMs boost our network’s memory power.

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Can we predict the outcome of a football game given a dataset of past games? That’s the question that we’ll answer in this episode by using the scikit-learn machine learning library as our predictive tool.

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Artificial Intelligence (AI) and robotics in Canada, Deep Learning, Machine Learning — Bloomberg Businessweek presents an exclusive premiere of the latest episode of Hello World, the tech-travel show hosted by journalist and best-selling author Ashlee Vance and watched by millions of people around the globe.

Hello World – Canada: The Rise of Al
Stars: Brandon Lisy, Ashlee Vance,
Genre: Documentary

Hello World invites the viewer to come on a journey. It’s a journey that stretches across the globe to find the inventors, scientists and technologists shaping our future. Each episode explores a different country and uncovers the ways in which the local culture and surroundings have influenced their approach to technology. Join journalist and best-selling author Ashlee Vance on a quest to find the freshest, weirdest tech creations and the beautiful freaks behind them.

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COPYRIGHT: All of the films published by us are legally licensed. We have acquired the rights (at least for specific territories) from the rightholders by contract. If you have questions please send an email to:, Amogo Networx – The AVOD Channel Network,

Deep learning is a revolutionary technique for discovering patterns from data. We’ll see how this technology works and what it offers us for computer graphics. Attendees learn how to use these tools to power their own creative and practical investigations and applications.

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This one is a bit more symbol-heavy, and that’s actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later.

For more on backpropagation:

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Video timeline
0:00 – Introduction
0:38 – The Chain Rule in networks
3:56 – Computing relevant derivatives
4:45 – What do the derivatives mean?
5:39 – Sensitivity to weights/biases
6:42 – Layers with additional neurons
9:13 – Recap

3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you’re into that):

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The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these “nudges” in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen’s book or Chis Olah’s blog.

Video timeline:
0:00 – Introduction
0:23 – Recap
3:07 – Intuitive walkthrough example
9:33 – Stochastic gradient descent
12:28 – Final words

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