How to get your dream job in Data Science if you are a career changer?
First you have to sneak around HR and their antiquated methods. This is only possible through contacts or unusual ways.
But what are good ways?
The middleman
Someone who can hand over your application who has a connection to the company or someone who works there.
The direct way, but be careful this must be done well
You look for a contact person via Linkedin, but the pitch has to be right and you really have to have an interesting application. Otherwise it looks like spam and you are out of the game forever.
How?
Prepare a small slidedeck why you are suitable for the job
Know exactly which frameworks they are using and be skilled in these.
Why not show a micro degree for one of their frameworks, just cost you some time. This shows commitment and is a real door opener.
Have a portfolio with your previous projects and also associated Github repositories.
Collect letters of recommendation from your previous projects
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Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? • Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks.
The cost of training such models and the necessity of data access to do so is coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT.
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While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, non-deidentified EHR with which they might train a BERT model (or similar).
Would it be safe to release the weights of such a model if they did?
How do you create a beautiful interface for your machine learning or data science project?
Handmade from scratch?
Any good tools?
Sure there are incredible tools:
Beautiful ML & DS interfaces
Gradio
Quickly create customizable UI components around your ML models. By dragging-and-dropping in your own images, pasting your own text, recording your own voice & seeing what the model outputs.
Dash apps bring Python analytics to everyone with a point-&-click interface to models written in Python, R & Julia - vastly expanding the notion of what's possible in a traditional dashboard.
FELIX, a fast and flexible text-editing system that models large structural changes and achieves a 90x speed-up compared to seq2seq approaches whilst achieving impressive results on four monolingual generation tasks.
Compared to traditional seq2seq methods, FELIX has the following three key advantages:
Sample efficiency: Training a high precision text generation model typically requires large amounts of high-quality supervised data.
FELIX uses three techniques to minimize the amount of required data:
(1) fine-tuning pre-trained checkpoints, (2) a tagging model that learns a small number of edit operations, and (3) a text insertion task that is very similar to the pre-training task.
Learn more about spaCy v3.0 and its new features like: transformer-based pipelines, the new training config and workflow system to help you take projects from prototype to production.
STEP BY STEP
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01:54 – State-of-the-art transformer-based pipelines
05:03 – Declarative configuration system
11:06 – Workflows for end-to-end projects
17:03 – Trainable and rule-based components
21:43 – Custom models in any framework
26:20 – Features and summary
They first introduce an optimization scheme that utilizes a CLIP-based loss to modify an input latent vector in response to a user-provided text prompt.
Next, they describe a latent mapper that infers a text-guided latent manipulation step for a given input image, allowing faster and more stable textbased manipulation.