Understanding Transformers – the CORE behind the Mammoth (Bert). In Language Modelling domain, BERT is something that has created quite a chaos since it is introduced. A lot of similar models have come from that time which always have a competition in claiming which one is better. Some of the alternatives include:
• GPT,
• GPT-2,
• RoBERTa,
• DistilBERT,
• XLNet, etc.
BERT, and other alternatives, have found their applications in numerous NLP problem statements like Machine Translation, Text Summarisation, Question Answering, etc. One rule for any famous algorithm is that as it gets introduced, it is followed by a lot of explanations and a lot of packages enabling you to apply the algorithm. This article can also be counted among one of them, but may be different in the approach and examples.

In this post, I’ll explain how to forecast using Facebook’s Prophet and demonstrate a few advanced techniques for handling trend inconsistencies by using domain knowledge. There are a lot of Prophet tutorials floating around the web, but none of them went into any depth about tuning a Prophet model, or about integrating analyst knowledge to help a model navigate the data. I intend to do both of those with this post.

You test your code. We know you do. How else are you sure that your changes don’t break the program? But after you commit, you discard those pesky scripts and throw away code. Don’t you think it’s a bit of a waste to dump all that effort that took you quite a decent chunk of your day to conjure? Well, here you are, so let’s see another way. A better way.

The most time-consuming parts of any machine learning project are the initial phases of data cleaning, pre-processing and analysis. Prior to training a model, you will first need to go through a lengthy process. Carrying out tasks such as dealing with missing values, converting categorical variables to numerical and analysing the data to inform feature engineering and selection.

Collaborative filtering, KNN, Deep learning, transfer learning, Tfidf…ect explore all of these

Everywhere we look, data science – perhaps simply a combination of statistical analysis, machine learning and data analytics as Cassie Kozyrkov put it in one of her articles – is on a hot streak. So much data generated, so many questions to ask.

Google has started offering a new service for ‘explainable AI’ or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction.

Imagine you are in a meeting with 5-8 people. It’s a development meeting, early in the product cycle. Maybe it is a sprint planning session, with ideas flowing from the group about how to approach the problem. You can feel the energy and excitement of the fully engaged team members, with each suggestion bringing about a better solution. The team has brought its best ideas to the table and collectively shaped them, and the meeting is about to wrap up. But, is your solution ethical? Is it ‘ok’ for your customers, or employees? How can you know? Aren’t these questions for the lawyers and people that don’t do the real development work? Most of us in this function/space aren’t used to asking these questions, and our tools don’t account for ethical debates. How can we do it?

1. Natural Language Generation
2. Speech Recognition
3. Online Agents
4. Machine Learning Platforms
5. AI-Optimized Hardware
6. Choice Management
7. Deep Learning Platforms
8. Biometrics
9. Robotic Processes Automation
10. Text Analytics and Natural Language Processing
11. Digital Twin/AI Modeling
12. Cyber Defense
13. Conformity
14. Knowledge Worker Aid
15. Material Creation
16. Peer-to-Peer Networks
17. Feeling Recognition
18. Picture Recognition
19. Advertising and Marketing Automation

Juliabox is similar to Colab, but rather than running Python, it runs Julia. Just like Colab, JuliaBox is free.

Advantages:
1) Reduction in Human Error
2) Takes risks instead of Humans
3) Available 24×7
4) Helping in Repetitive Jobs
5) Digital Assistance
6) Faster Decisions
7) Daily Applications
8) New Inventions
Disadvantages:
1) High Costs of Creation
2) Making Humans Lazy
3) Unemployment
4) No Emotions
5) Lacking Out of Box Thinking

We are excited to announce the Deep Java Library (DJL), an open source library to develop, train and run Deep learning models in Java using intuitive, high-level APIs. If you are a Java user interested in learning Deep learning, DJL is a great way to start learning. If you’re a Java developer working with Deep learning models, DJL will simplify the way you train and run predictions. In this post, we will show how to run a prediction with a pre-trained Deep learning model in minutes.