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Mayurji/N2D-Pytorch

#artificialintelligence

Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss. In such cases, an autoencoder is typically connected with a clustering network, and the final clustering is jointly learned by both the autoencoder and clustering network. Instead, we propose to learn an autoencoded embedding and then search this further for the underlying manifold. We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is able to find the best clusterable manifold of the embedding.


Which machine learning / deep learning algorithm to use by problem type

#artificialintelligence

I like to approach algorithms from the perspective of problem solving. I created this list from a Mc Kinsey document (link below). Predict a sales lead's likelihood of closing Simple, low-cost way to classify images (eg, recognize land usage from satellite images for climate-change models).


Predicting best quality of wine using Linear Regression and PyTorch

#artificialintelligence

In this notebook we will predict the best quality of the wine using PyTorch and linear regression. If you haven't checked out my previous blog on Linear Regression check this out . First of all lets import required libraries.. Now lets analyse our dataset.. its important to analyse to see what we are dealing with.. Training Dataset: The sample of data used to fit the model. The actual dataset that we use to train the model (weights and biases in the case of a Neural Network). The model sees and learns from this data.


The Various Types of Artificial Intelligence Technologies

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Artificial Intelligence is a broad term that encompasses many techniques, all of which enable computers to display some level of intelligence similar to us humans. The most popular use of Artificial Intelligence is robots that are similar to super-humans at many different tasks. They can fight, fly, and have deeply insightful conversations about virtually any topic. There are many examples of robots in movies, both good and bad, like the Vision, Wall-E, Terminator, Ultron, etc. Though this is the holy grail of AI research, our current technology is very far from achieving that AI level, which we call General AI.


Deep Learning Prerequisites: Logistic Regression in Python

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Online Courses Udemy - Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python BESTSELLER Created by Lazy Programmer Inc English [Auto-generated], Portuguese [Auto-generated], 1 more Students also bought Data Science: Deep Learning in Python Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Advanced NLP and RNNs Deep Learning A-Z: Hands-On Artificial Neural Networks Preview this course GET COUPON CODE Description This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.


Adding Common Sense to Machine Learning with TensorFlow Lattice

#artificialintelligence

Training-serving skew: The offline numbers may look great, but what if your model will be evaluated on a different or broader set of examples than those found in the training set? This phenomenon, more generally referred to as "dataset shift" or "distribution shift", happens all the time in real-world situations. Models are trained on a curated set of examples, or clicks on top-ranked recommendations, or a specific geographical region, and then applied to every user or use case. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. Bad individual errors: Models are often judged by their worst behavior --- a single egregious outcome can damage the faith that important stakeholders have in the model and even cause serious reputational harm to your business or institution.


The Roadmap of Mathematics for Deep Learning

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Knowing the mathematics behind machine learning algorithms is a superpower. If you have ever built a model for a real-life problem, you probably experienced that being familiar with the details can go a long way if you want to move beyond baseline performance. This is especially true when you want to push the boundaries of state of the art. However, most of this knowledge is hidden behind layers of advanced mathematics. Understanding methods like stochastic gradient descent might seem difficult since it is built on top of multivariable calculus and probability theory.


Top 25 Best Machine Learning Books You Should Read

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"Machine Learning foners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling."


Data Science & Deep Learning for Business 20 Case Studies

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All MBA's will preach that Data-Driven Methods udemy discount Understand the value of data for businesses Learn to use Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and more! Apply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time Value Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests Unsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNE Build a Product Recommendation Tool using collaborative & item/content based Hypothesis Testing and A/B Testing - Understand t-tests and p values Natural Langauge Processing - Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam Detection To use Google Colab's iPython notebooks for fast, relaible cloud based data science work Deploy your Machine Learning Models on the cloud using AWS This course takes on Machine Learning and Statistical theory and teaches you to use it in solving 20 real-world Business problems. Data Scientist is the buzz of the 21st century for good reason! The tech revolution is just starting and Data Science is at the forefront. As a result, "Data Scientist has become the top job in the US for the last 4 years running!" according to Harvard Business Review & Glassdoor.


Machine Learning in a Nutshell

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Machine Learning, Artificial intelligence (AI) and Deep Learning are taking the world by storm, dominating conversations about how machines can replace humans by providing a competitive advantage to businesses. The World is currently preparing to enter the fourth industrial revolution -- the rise of the "intelligent machine." At the heart of this revolution is Artificial Intelligence (AI), Mimicking human cognitive functions like problem-solving, learning and decision making using algorithms. From speed to efficiency, AI offers an abundance of benefits. Numerous sectors, including healthcare, automotive, defence and retail have already witnessed the game-changing impact of AI.