Erik Schmidt, a Senior Scientist at Pandora is going to propose an insight of recommendations and deeper challenges involved with Pandora at the Machine Intelligence Summit. This global tech event will take place in San Francisco on 23rd and 24th of March 2017. Continue reading “Pandora: Blending Music with Machine Learning”
Uber is here as a mobile app, which allows you to request for a ride, but this company has never deemed itself to be a mere transportation service provider, rather it prefers to call itself a technology service provider, more like some logistics company.
More than a year ago, Danny Lange was appointed as the head of Machine Learning at Uber and he along with his team associates started operations from San Francisco. Being an ardent believer of the benefits that Machine learning can bring upon the society, Lange considers that AI and Machine Learning, if combined together can absolutely solve any business discrepancies, irrespective of the nature of the problem.
Continue reading “Uber: Pioneering Machine Learning into Everything it Does”
A major misconception about artificial intelligence is the fact that today’s robots possess a very generalized intelligence, however, we are fairly efficient in leveraging large datasets to accomplish otherwise complex tasks. Nevertheless we still fail and fall flat at the prospect of replicating the breadth of human intelligence.
Care to contribute to AI development in today’s world? Then take up a Machine Learning course online with us. But in order to move forward a generalized intelligence, Facebook is ensure that we know how to evaluate the process. In a recently released paper, Facebook’s AI research (FAIR) lab has outlined just that as a part of its CommAI framework.
We will need our systems to be able to communicate and will be able to learn through language effectively even when they lack in context and discussing thing in undefined terms.
Furthermore, such systems should be capable of learning up new skills, fairly simply. As per Facebook this skill set is called “learning to learn”. Present machine learning models may be trained on data and be used for classifying defined objects. We can also make use of transfer learning to quickly adapt a model to achieve the same task on the new data, however our machines cannot completely teach themselves without heavy to moderate intervention from the developers.
It is in general agreed upon, that in order to generalize across several tasks, a program should be capable of compositional training. And that is of storing and recombination solutions to sub-problems across the different tasks, as per the team from Facebook.
As per Facebook they consider these capabilities to be of more of a prerequisite to being a generalized AI than the true Turing test. Alan Turing created the original Turing test in the 1950s. It is usually understood to be a means of assessing machine learning intelligence with respect to human intelligence.
However, with the maturation of the field of Ai the Turing test has lost a lot of its relevance. Facebook hopes to offer a nice alternative way to think about the necessary requirements of a modern generalized AI which should be less of a research distraction than the more rigid Turing Test.
The team at FAIR which include – Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic and Tomas Mikolov have also developed another open source platform for the testing and training of AI systems.
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Before we begin with our Python tutorial on how to parse data with Python, we would like you to download this machine learning data file, and then get set to learn how to parse data.
The data set we have provided in the above link, mimics exactly the way the data was when we visited the web pages at that point of time, but the interesting thing about this is we need not visit the page even. We actually have the full HTML source code, so it is just like parsing the website without the annoying bandwidth use.
Now, the first thing to do when we start is to correspond the date to our data, and then we will pull the actual data.
Here is how we start:
import pandas as pd import os import time from datetime import datetime path = "X:/Backups/intraQuarter"
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As given above, we are importing the Pandas for the Pandas module, OS, that is so we can interact with the directories, date and time for managing the date and time information.
Furthermore, we will finally define the path, which is the path to the intraQuarter folder than one will need to unzip the original zip file, which you just downloaded from the website.
def Key_Stats(gather="Total Debt/Equity (mrq)"): statspath = path+'/_KeyStats' stock_list = [x[0] for x in os.walk(statspath)] #print(stock_list)
We began our functions, with the specification that we are going to try to collect all the Debt/equity values.
The path to the stats directory is Statspath.
To list all the contents in the directory, you can use stock_list which is a fast one-liner for the loop that uses os.walk.
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Then the next step is to do this:
for each_dir in stock_list[1:]: each_file = os.listdir(each_dir) if len(each_file) > 0:
Mentioned above is a cycling through of directory (which is every stock ticker). Then the next step is to list “each_file”, which is each file within that very stock’s directory. If in case the length of each_file which is in fact is a list of all of the files in the stock’s directory, is greater than 0 only then will we want to proceed. However, there are some stocks with no files or data:
for file in each_file: date_stamp = datetime.strptime(file, '%Y%m%d%H%M%S.html') unix_time = time.mktime(date_stamp.timetuple()) print(date_stamp, unix_time) #time.sleep(15) Key_Stats()
Finally, at the end, we must run a loop that pulls the date_stamp, from each file. All our files are actually stored under their ticket, with a file name for the exact date and time from which the information is being taken out.
It is from there that we will explain to date-time what the format for our date stamp is, and then we will convert it to a Unix time stamp.
To know more about data parsing or anything else in python, learn Machine Learning Using Python with the experts at DexLab Analytics.
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It is evident that in the last few months, we have had several people showcase their enthusiasm about venturing into the world of data science using Machine Learning techniques. They are keen on probing the statistical regularities and building impeccable data-driven products. but we have made an observation that some may actually lack the necessary mathematical knowledge and intuition to get the framework for achieving results with data. And this is why we have decided to discuss this lacking through our blog.
In the recent times, there has been a noticeable upsurge in the availability of several easy-to-use machine and deep learning packages such as Weka, Tensorflow, scikit learn etc. But you must understand that machine learning as a field is one that has both statistical concepts, probabilistic concepts, computer science and algorithmic concepts to arise from learning intuitively from available data and also is about determining the patterns and hidden insights, which can be used to build intelligent applications. While still having the immense possibilities of Machine Learning and Deep Learning which is a thorough mathematical understanding of many of these techniques which is necessary for a good grasp of the internal workings of algorithms to achieve a good result.
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To explain why it is necessary to behind the scenes into the mathematical details of Machine Learning, we have put own a few important points:
The primary question when one tries to understand an interdisciplinary field such as Machine Learning, is the amount of math needed and the level of math needed to understand these techniques.
The answer to this question is not as simple as it may seem and is multidimensional which, depends upon the level and interest of the individual. Research conducted in these mathematical formulations and theoretical advancements for Machine Learning is an ongoing process and a few researchers are already working on few more advanced techniques. However, we will state the least amount of math that is a must have skill for being a successful Machine learning Engineer/ Scientist is the importance of each and every mathematical concept.
This is the math skill to have for the 21st century. One must be well-versed with the topics of Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigendecomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces as these norms are absolutely necessary for the understanding and the optimization methods for machine learning. The best thing about linear algebra is that there are a lot of online resources.
Machine learning and statistics are not too different a field. And in reality some people have actually defined Machine Learning as “doing statistics on a Mac”. A few fundamentals that are a must have for machine learning are – Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.
Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution are some of the necessary topics necessary for understanding ML.
In order to realize the computational efficiency and scalability of our Machine Learning Algorithm and for exploiting the sparsity in the dataset, this concept is necessary. One must have knowledge of data structures such as Binary Trees, Hashing, Heap, Stack etc, and Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods.
A few other mathematical skills that are often necessary for understanding ML are the following Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits), Information Theory (Entropy, Information Gain), Function Spaces and Manifolds.
Machine learning training in Gurgaon from experts with in-depth instruction on math skills is offered at DexLab Analytics. Check out our Machine learning certification brochure for the same at the website.
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We have some intriguing news, The Ultimate CMO Challenge from the Delhi School of Economics, University of Delhi is being held and we are sponsoring the event. Participate at the Ultimate CMO Challenge if you a PG student or are pursuing a course in MBA.
The first round will be an online round where participants would have to submit their solutions online. Problem case will be sent to the teams that have registered for the challenge, the solution to be submitted should be of 6 slides except the cover slides. One can mail their solutions to cmo.atharva@mibdu.org
BINA 48 is the world’s most renowned and highly sought after humanoid robot in America. You can visit her there, by driving down a long winding dirt road just west of the Lincoln Gap in Bristol, Vt. Where sits two large yellow houses on a sprawling property that features ten solar panels and a dock over-looking the sunlit pond filled with trout, a homely porch decorated with rocking chairs.
In the smaller of the two houses resides BINA 48, who is one of the most sought after humanoid who is based on a real personality – Bina Rothblatt.
Continue reading “A robot too close to humans! Story of BINA 48”
Last year was the year, which saw artificial intelligence, went mainstream.
By that, we do not mean just getting filtered raunchy photos on Twitter or getting the fake news suggestions on Facebook.
Continue reading “Here Are Four Predictions For AI This 2017!”
Before we get started with the topic, let us first get an idea about its background. Have you ever given a thought as to how many cats does it take to identify one cat?
In this article we will cover the five types of problems that people face with Artificial Intelligence (AI) i.e. we will address the all important question of – in which situation must one make use of AI (artificial intelligence)?
To have a better understanding of such concepts you can take up a Machine Learning course in Delhi.
Just some time ago, we conducted a strategy workshop for a bunch of senior executives who are running a large multinational company. In that workshop, someone asked this question – “How many cats will it need to identify a cat?”
In this post, we will discuss the problems which can be uniquely resolved through Artificial Intelligence. While this may not be the exact taxonomy, but it still is pretty comprehensive. The main reason we have added extra emphasis on Enterprise AI issues, because we believe that this subject will have a deep impact on many mainstream applications, but despite that a lot of media attention focuses at the more esoteric avenues. Further, information about these concepts are available in our Machine Learning training course.
But before we delve into AI application types, we must discuss the main distinguishing characters between AI / Deep Learning / Machine Learning.
The term Artificial Intelligence by definition implies that machines can reason with the help of this feature. However, here is a better more complete list of AI characteristics:
With developments in Deep Learning algorithms, AI is driven forward. The various deep learning algorithms can detect numerous patterns without having any prior definition of these features. And in a broader sense, Machine Learning means the application of any algorithm which can be applied against a set of data to discover a pattern within the same. Such algorithms have features like supervised, unsupervised, classification, segmentation, or regression. Moreover, while they are very popular, there are many reasons why Deep Learning algorithms may not make other Machine Learning algorithms.
Now that we have some background knowledge, we can now discuss the five major types of problems with AI:
This consists of tasks that are based on learning several knowledge bodies like financial, legal, and more, and then formulating a process where the machine will be able to simulate as an expert in the given field.
In this case, the machine learns a complex body of knowledge like information regarding the existing medication and much more, and then suggests new ideas to the domain itself, like for instance new drugs for curing diseases.
There are many logistics and scheduling projects, which can be done by current (non AI) algorithms. But as optimization keeps developing and gets more complex AI would slowly grow.
AI and deep learning can offer benefits to many communication modes such as intelligent agents, automatic and much more.
Deep learning and AI can be capable of producing newer forms of perception which enables new services like autonomous automotives and more.
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