Some cursory googling didn’t yield anything, so I was wondering if anyone here had come across anything?

Hi folks,

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A lot of people ask how to combine NLP based features (or in general sequence embeddings) with standart features. In keras it pretty easy with a multiple input modell:

nlp_input = Input(shape=(seq_length,), name='nlp_input') meta_input = Input(shape=(10,), name='meta_input') emb = Embedding(output_dim=embedding_size, input_dim=100, input_length=seq_length)(nlp_input) nlp_out = Bidirectional(LSTM(128, dropout=0.3, recurrent_dropout=0.3, kernel_regularizer=regularizers.l2(0.01)))(emb) x = concatenate([nlp_out, meta_input]) x = Dense(classifier_neurons, activation='relu')(x) x = Dense(1, activation='sigmoid')(x) model = Model(inputs=[nlp_input , meta_input], outputs=[x]) 

Here is a link, where it more detailed.

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Cheers

Hi all!

I’m Nezir, representative of the Institute of Contemporary Sciences, an NGO in Serbia whose aim is to popularize and advance Data Science and all related fields of science. Our main activity that we work on is Data Science Conference, which aims to bring data scientists from all over the world to exchange ideas and discuss the hottest topics in data science.

Data Science Conference, one of the biggest conferences dedicated to data science in Europe, will be held for 5th annual time on 19-20 November in Belgrade. We are looking for inspirational individuals who would like to become a part of our Speakers and Tutors team. For this year we have planned to organize 5 Workshops, 5 parallel tracks, 8 Data discussion, 60 Talks & 6 Keynote talks and over 70 Tech Tutorials for over 1500 attendees.

If you are interested to become part of the Conference apply and join us! Application is open until 17 June.

Call For Speakers If you have an interesting story you would like to share with our attendees – apply to become a part of Speakers team. You would be able to choose between 10 topics. Topics we are going to cover this year are: Applicative ML, Data Monetization, Deep Learning & AI, Business Intelligence, Big Data & Data Engineering, Framework for AI & ML modeling, How to start Career in Data Science, Data Science in 4th Industrial revolution, Open Data, Data Privacy & Data for good, Data Science Startups & Digital transformation. More information you can find on our website.

Call For Tutors We are looking for experts who would like to share their knowledge with attendees of Data Science Conference as a part of our Tutors team. You would be able to choose between more than 15 topics, 6 languages, 18 platforms and tools you would like to present to attendees of the Conference. More information you can find at our website.

Tutors and Speakers who reside outside of Republic of Serbia will be accommodated in Belgrade city center, while the cost of the stay will be covered by the organizers. All selected speakers & tutors will get a free pass for the Conference and invitation to participate in Data Science Summit (3rd networking day of the Conference).

For any additional questions, you may contact us in comments or through private messages.

Paper: https://arxiv.org/abs/1905.12171

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Abstract: A human does not have to see all elephants to recognize an animal as an elephant. On contrast, current state-of-the-art deep learning approaches heavily depend on the variety of training samples and the capacity of the network. In practice, the size of network is always limited and it is impossible to access all the data samples. Under this circumstance, deep learning models are extremely fragile to human-imperceivable adversarial examples, which impose threats to all safety critical systems. Inspired by the association and attention mechanisms of the human brain, we propose reverse adversarial examples method that can greatly improve models’ robustness on unseen data. Experiments show that our reverse adversarial method can improve accuracy on average 19.02% on ResNet18, MobileNet, and VGG16 on unseen data transformation. Besides, the proposed method is also applicable to compressed models and shows potential to compensate the robustness drop brought by model quantization – an absolute 30.78% accuracy improvement.

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Hi, I am one of the authors on this paper.

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We like to collect opinions on this paper and answer questions.

I got my Ph.D in Machine Learning in 2008. Worked on the intersection of physics and supervised learning methods.Wasn’t happy as a postdoc and decided to join the industry instead.

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Joined a mid-level software company that developed analytics software for various businesses, hoping to utilize my ML and stats knowledge, but quickly found that at the time (2009~2010) domain knowledge and implementation expertise were much more valued than mathematical knowledge and modeling skills (again at that time – things seems to have changed radically since then – I remember hearing more than once while interviewing in 2009 hiring managers say that they’d rather hire a domain expert and train them on the science than hire a scientist and train them on the domain).

Within a year, I switched to a TPM/Architect type of role, going to client sites and doing implementations or assisting with project management and tech support, and worked for various consulting firms in that capacity, as there seemed to be more money and opportunity there.

In a way, I was never that far removed from data science, since the products I was peddling always involved an analytics/predictive function. But none of the roles I had involved any explicit science work, other than having to explain to clients and business stake holders the math that was behind some of the algorithms used in the software. My long term plan had always been to return to the science side of things at one point or another.

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In 2014, I realized that DS and ML were becoming fashionable, and that experience with neural networks (my old passion) was considered a hot skill on the market. It seemed like a perfect time to get back into the science side of things. I started learning R and Python (in my grad school days I was mostly a Matlab person) and catching up on the latest developments in the field, frequenting Kaggle discussions and other forums, practicing Leetcode, etc…

I thought the combination of business knowledge / consulting experience + Ph.D level understanding of the models and math involved would make me a golden candidate for various DS/ML roles.

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Instead, over the last 5 years, I have found it very difficult to break into the field, with recruiters and hiring managers rarely paying attention to me, and when they do, I can’t seem to get passed the technical screens or initial interviews because of my lack of real world experience with things like Spark, Flask, etc… and my mostly theoretical (very deep, but still purely theoretical) knowledge of how models other than time series and linear regression work.

Even more frustrating is that more and more people with less qualifications than me seem to transition at will into data science roles (business analyst or SDE completes a Coursera certificate in machine learning, gets promoted to data science role the next month).

To top it all off: I’m nearing forty now (which I gather is ancient in machine learning years) and I feel like my chances of breaking into the filed based on age alone are decreasing exponentially every year.

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Am I hopeless? Any advice for an aging data science has been/wanna be?