Design Principle 1: Always Start with Design of Datasets and Data Entities
Design Principle 2: Separate Business Rules from Processing Logic
Design Principle 3: Build Exceptions from the Beginning
Design Principle 4: Easy to Integrate using Standard Input and Output
• Learn the idea of importance sampling
• Get deeper understanding by implementing the process
• Compare results from different sampling distribution
Learn how to call state of the art models with some clicks using Azure, Colab Notebooks and a lot of snippets. There is a fleet of clouds with their own minds floating over the internet trying to take control of the winds. They’ve been pushing very aggressively all kinds of services into the world and absorbed data from every possible source. Among this big bubble of services, an increasing number of companies and applications are relying on pre-made AI resources to extract insights, predict outcomes and gain value out of unexplored information. If you are wondering how to try them out, I’d like to give you an informal overview of what you can expect from sending different types of data to these services. In short, we’ll be sending images, text and audio files high into the clouds and explore what we get back. While this way of using AI doesn’t give you direct, full control over what’s happening (as you would using machine learning frameworks) its a quick way for you to play around with several kinds of models and use them in your applications. It’s also a nice way to get to know what’s already out there.
• Natural Language Tool KIT [NLTK]
• We will build our own video classification model in Python
• This is a very hands-on tutorial for video classification – so get your Jupyter notebooks ready
1: Establish a Budget for Training Data
2: Source Appropriate Data
3: Ensure Data Quality
4: Be Aware of and Mitigate Data Biases
5: When Necessary, Implement Data Security Safeguards
6: Select Appropriate Technology
• The fastest way to get to work
• Organizing our budget to get the most out of it
• Planning workouts for maximum impact in the least amount of time
• Doing meal-prep every Sunday
• Packing a suitcase for a long vacation
These are just a few examples of everyday optimization. Optimization is the way of life. It can be as simple as the examples I just mentioned, or as complex as The Traveling Salesman Problem.
• Icecaps’ design is based on a component-chaining architecture, where models are represented as chains of components (e.g. encoders and decoders) that data flows through. This enables complex multi-task learning environments with shared components between tasks.
• Personalization embeddings, SpaceFusion, and MRC-based knowledge grounding models are recent advances in conversational modeling included in our toolkit.
• We provide customized decoding tools that allow users to employ maximum mutual information, token filtering, and repetition penalties to improve response quality and diversity.
• Data processing tools are provided for users to easily convert their text data sets into binarized TFRecords. Our data processor features various text preprocessing tools, including byte pair encoding and fixed-length multi-turn context extraction.
• HungaBunga – A Different Way of Building Machine Learning Models using sklearn
• Behavior Suite for Reinforcement Learning (bsuite) by DeepMind
• DistilBERT – A Lighter and Cheaper Version of Google’s BERT
• ShuffleNet Series – An Extremely Efficient Convolutional Neural Network for Mobile Devices
• RAdam – Improving the Variance of Learning Rates
• ggtext – Improved Text Rendering for ggplot2
• Counting functions: n() and n_distinct()
• If-else functions: if_else() and case_when()
• Comparison functions: between() and near()
• Selecting specific elements based on position: nth(), first() and last()
• Selecting specific rows based on position/value: slice() and top_n()
• Utilities: coalesce() and pull()
• Convergence in Probability
• Convergence in Quadratic Mean
• Convergence in Distribution
Let’s examine all of them.
• What makes memory such a complex subject in deep learning systems?
• Where can we draw inspiration about memory architectures?
• What are the main techniques used to represent memories in deep learning models?
1. Sample Bias
2. Prejudice Bias
3. Confirmation Bias
4. Group attribution Bias
Time series. Datasets that have a time element with them. Such data allow us to think about the combination of 2 properties of time series:
• Seasonality – Patterns in data that tends to repeat over and over at a specific length of time.
• Trend – This is similar to regression, where we are capturing the global pattern of the series.
• The relevance of data tends to center at present time, meaning past and data close to present time is of greater influence and accuracy of future predictions is better when closer to present data (principle of entropy).
2. Google Cloud ML Engine
4. Apache Mahout
6. Oryx 2
7. Apache Singa
8. Apache Spark MLlib
9. Google ML Kit for Mobile
10. Apple’s Core ML
5. Apache MXNet
• its orientation (vertical and horizontal bar charts)
• and its arrangement (e.g. grouped and stacked bar charts)
The reason behind the success of bar charts is a simple and intuitive design that makes it easy to interpret the presented data. It is also a widely used graph type which is already deeply integrated into our daily life (through news, articles, dashboards, etc.). Whenever we think about a representation of discrete data we think of bar charts. Even me, a data viz enthusiast, uses bar charts extensively, mostly to provide me with a quick first insight into the data.
The anomaly/outlier detection algorithms covered in this article include:
• Low-pass filters: taking the centered rolling average of a time series, and removing anomalies based on Z-score
• Isolation forests
• Seasonal-extreme studentized deviate (S-ESD) algorithm
• One class support vector machines (SVM’s)
This article will dwell on the architecture of Kafka, which is pivotal to understand how to properly set your streaming analysis environment. Later on, I will provide an example of real-time data analysis by creating an instant messaging environment with Kafka. By the end of this article, you will be able to understand the principal features which make Kafka so useful, that are:
• commit log
• horizontal scalability
• fault tolerance
For now, let’s keep them in mind without explaining their meaning, which will be far clearer after introducing some further notions.