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Understanding Generative Adversarial Networks - Part II

This is a two part series on understanding Generative Adversarial Networks (GANs). This part deals with the conceptual understanding of GANs. In the second part we will try to understand the mathematics behind GANs.

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Understanding Generative Adversarial Networks - Part I

This is a two part series on understanding Generative Adversarial Networks (GANs). This part deals with the conceptual understanding of GANs. In the second part we will try to understand the mathematics behind GANs.

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Reinforcement Learning - Part 3

Robotics and artificial intelligence has made a huge advancement in their respective fields. But the blend of Robotics and Artificial Intelligence have created some amazing stuff too. It doesn't seem far in future when robots can learn themselves what humans can using Artificial Intelligence.

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Deep Biology Program

Cere Labs is happy to start the Deep Biology program under the umbrella of CoE with Patkar-Varde College, Goregaon. This unique program brings together multiple departments in Patkar-Varde College, Goregaon to collaborate with CereLabs.

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Why Evaluation Metrics Matters

This is a follow up article on "The Importance of F1 Score" in which we understood the technical aspects of evaluating a Machine Learning model. In this article we will understand how different evaluation metrics can help us in designing solutions based on the problem statement and domain.

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Why Internship Matters in AI?

If you visit Cere Labs office, it is occupied with enormous enthusiasm. Why will it not be? Almost half of it is occupied by young people who want to showcase their capabilities in the field of Artificial Intelligence (AI). Although we call them interns, they are much more prepared for the job they commit to. It boils down to the LEARNING-KNOWING-DOING cycle.

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Will AI evolve to be as bad as humans?

Will AI be a threat to humans? Will it take over our job? These are questions that are popping out of newspapers, blogs and journals everywhere. These are really important questions.Many authors who predict an AI victory over humans opt to skip defining what they mean by AI. And it is understandable.

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Reinforcement Learning - Part 2

This is a follow up article on "Reinforcement Learning - Part 1". Q learning is an algorithm in reinforcement learning. It originates from the model based reinforcement learning. It can be referred to as a different kind of value function. The values are called Q values and are denoted by Q(s,a).

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Understanding Projection Pursuit Regression

Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting.

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Reinforcement Learning - Part 1

This article is the first part of the series of three articles that would give us a peek into the future of robot learning and an introduction to Reinforcement Learning. By the end of part three an overview of creating a crawling robot that learns itself how to move in a particular direction will be given.

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Helping the Blind See

The Sense of Vision is taken for granted by us in our day to day life, but only a visually impaired person can understand the true value and necessity of Vision. To accelerate further research and to boost the possible applications of this technology, Google made the latest version of their Image Captioning System available as an open source model in Tensorflow.

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The Importance of Intents and Context in Chatbots

Humans are good in conversations, because they can understand the intent of any statement, also the context in which the statement is placed.
Using machine learning, statements are mapped to intents, and the chabot can handle infinite variations of the same statement. Why is this technique useful?

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In the World of Document Similarity

How does a human infer whether two documents are similar? This question has dazzled cognitive scientists, and is one area under which a lot of research is taking place. As of now there is no product that is able to match or surpass human capability in finding the similarity in documents. But things are improving in this domain, and companies such as IBM and Microsoft are investing a lot in this area.

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Why is it Important for Executives to know about Artificial Intelligence?

Imagine you are an IT executive in a company. As per your role, you have to offer IT solutions to problems that your company is facing, be it related to Finance, Sales, HR, Administration, etc. Maybe you are already playing this role. You are living in a perfect world, where most of the problems that you are solving can be solved with programming. You are living in a non-Artificial Intelligence world.

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The Importance of F1 Score

At Cere Labs, we are building various image classification systems. While building any kind of classification system one is often challenged to test the trained models. One useful measure to test such models is accuracy, which is the proportion of true results and the total number of images examined. If we are trying to classify the image of an apple, accuracy will be the measure of how accurately the classifier is able to detect the apple in an image.

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Greetings, in the World of Chatbots

Imagine a scenario - You are on a website, and on the bottom right you see a chat window. The look of that window tempts you to experiment. You have interacted with various chatbots previously, a kind of a hobby you have developed, and have seen how miserable the chatbots are.

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The Project Fellowship Program - 2016

Cere Labs’ Project Fellowship Program will develop professionals as well as leaders in emerging world of Artificial Intelligence.

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50 Questions about Convolutional Neural Networks

Well! Convolutional Neural Network (CNN) is such a technology. How it does, what it does is truly indistinguishable from magic. Read our earlier post - “From Cats to Convolutional Neural Networks”, to understand why CNNs come close to human intelligence.

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How is AI Saving the Future

Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer.

It will be a daunting task for humans to understand enormous amount of data that is generated all over the world.

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Why Study the Brain?

“There is no scientific study more vital to man than the study of his own brain. Our entire view of the universe depends on it.” ― Francis Crick.

After unraveling the mysteries of DNA, the secret to life, Francis Crick for the rest of his life turned his attention to solve the mysteries of brain and consciousness. He was certain that the answer to intelligence lies deep in the structure of brain.

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Building Commonsense in AI

It is often debated that what makes humans the ultimate intelligent species is the innate quality of doing commonsense reasoning. Humans use common sense knowledge about the world around to take appropriate decisions, and this turns out to be the necessary ingredient for their survival.

AI researches have long thought about building commonsense knowledge in AI.

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Anomaly Detection based on Prediction - A Step Closer to General Artificial Intelligence

There are two approaches to create intelligence in machines. One is to understand how human brain creates intelligence and replicate the methods used by it to create AI. The other is to take a fresh engineering approach to create intelligence.

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Understanding Neocortex to Create Intelligence

There are two approaches to create intelligence in machines. One is to understand how human brain creates intelligence and replicate the methods used by it to create AI. The other is to take a fresh engineering approach to create intelligence.

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Information Extraction - The key to Question Answering Systems

The day AI reads a document and answers each and every question asked and do reasoning on it, will be the day when we will call it true intelligence. Welcome to the world of Information Extraction, where algorithms try to extract information from unstructured documents into structured information, which the AI can further access to answer questions.

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GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast.

Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data.

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From Cats to Convolutional Neural Networks

Widely used in image recognition, Convolutional Neural Networks (CNNs) consist of multiple layers of neuron collection which look at small window of the input image, called receptive fields.

The history of Convolutional Neural Networks begins with a famous experiment “Receptive Fields of Single Neurons in the Cat’s Striate Cortex” conducted by Hubel and Wiesel. The experiment confirmed the long belief of neurobiologists and psychologists that the neurons in the brain act as feature detectors.

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Dynamics of Selecting your Open Source AI

The landscape of open source AI is big. To identify suitable open source tools to make your AI dream product is a herculean task. Selecting an AI toolkit for your product might turn out costly when you need to scale your software, thus it turns out to be a strategic decision. We at Cere Labs have developed a criteria to choose Open Source AI Toolkit.

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OpenAI and future of AI

With the advent of AI in almost every industry, right from self driving cars to robot nurses, there is a general concern as to how AI might impact humanity. The presence of AI can be felt on every device including mobile phones. The reach of AI in every aspect of our life is inevitable. So how do we make sure that it benefits humanity as a whole?

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Implement XOR in Tensorflow

XOR is considered as the 'Hello World' of Neural Networks. It seems like the best problem to try your first TensorFlow program.
Tensorflow makes it easy to build a neural network with few tweaks. All you have to do is make a graph and you have a neural network that learns the XOR function.

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TensorFlow: A new generation of Google's Machine Learning Open Source Library

Although Machine Learning has dominated the Artificial Intelligence scene for long, easy access to open source machine learning libraries is recently made possible. With the launch of TensorFlow, Google has made it possible for corporates to add intelligence to make sense of data.

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