In statistical modelling the biggest threat is concept drift which makes the model gradually showing deteriorating performance over time. There are state of the art methodologies to detect the impact of concept drift, however general strategy considered to overcome the issue in performance is to rebuild or re-calibrate the model periodically as the variable patterns for the model changes significantly due to market change or consumer behavior change etc. Quantitative research is the most widely spread application of data science in Marketing or financial domain where applicability of state of the art reinforcement learning for auto-learning is less explored paradigm. Reinforcement learning is heavily dependent on having a simulated environment which is majorly available for gaming or online systems, to learn from the live feedback. However, there are some research happened on the area of online advertisement, pricing etc where due to the nature of the online learning environment scope of reinforcement learning is explored. Our proposed solution is a reinforcement learning based, true self-learning algorithm which can adapt to the data change or concept drift and auto learn and self-calibrate for the new patterns of the data solving the problem of concept drift. Index Terms-- Reinforcement learning, Genetic Algorithm, Q-learning, Classification modelling, CMA-ES, NES, Multi objective optimization, Concept drift, Population stability index, Incremental learning, F1-measure, Predictive Modelling, Self-learning, MCTS, AlphaGo, AlphaZero 1. Introduction Concept drift is well known challenge for sustainability of any machine learning predictive model over time. Machine learning offers diverse techniques to understand the underlying pattern of the data and associate the same with prediction objective. Any predictive modelling activity in either Marketing, Finance, Management are heavily dependent on the assumption that the training data represents the pattern of target population under specific study such as Fraud Identification, Customer churn prediction, Marketing mix modelling, Target customer identification for specific type of promotion etc. However due to social & economic development, customer behavior changes combined with other external factors making past learned pattern, irrelevant for current predictions.

Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. The problem is challenging due to the data variability among individual users, and sensor artefacts. In this work, we propose an end-to-end framework for real-time cognitive workload classification with mixture Hyper Long Short Term Memory Networks, a novel variant of HyperNetworks. Evaluating the proposed approach on an eye-gaze pattern dataset collected from simulated driving scenarios of different cognitive demands, we show that the proposed framework outperforms previous baseline methods and achieves 83.9\% precision and 87.8\% recall during test. We also demonstrate the merit of our proposed architecture by showing improved performance over other LSTM-based methods.

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers). ANTs allow increased interpretability via hierarchical clustering, e.g., learning meaningful class associations, such as separating natural vs. man-made objects. We demonstrate this whilst achieving over 99% and 90% accuracy on the MNIST and CIFAR-10 datasets. Neural networks (NNs) and decision trees (DTs) are both powerful classes of machine learning models with proven successes in academic and commercial applications. The two approaches, however, typically come with mutually exclusive benefits and limitations. NNs are characterised by learning hierarchical representations of data through the composition of nonlinear transformations (Zeiler & Fergus, 2014; Bengio, 2013), which has alleviated the need for feature engineering, in contrast with many other machine learning models. In addition, NNs are trained with stochastic optimisers, such as stochastic gradient descent (SGD), allowing training to scale to large datasets. Consequently, with modern hardware, we can train NNs of many layers on large datasets, solving numerous problems ranging from object detection to speech recognition with unprecedented accuracy (LeCun et al., 2015). However, their architectures typically need to be designed by hand and fixed per task or dataset, requiring domain expertise (Zoph & Le, 2017). Inference can also be heavyweight for large models, as each sample engages every part of the network, i.e., increasing capacity causes a proportional increase in computation (Bengio et al., 2013). Alternatively, DTs are characterised by learning hierarchical clusters of data (Criminisi & Shotton, 2013).

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean F1 score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of human-computer communications, they suffer from the problem of strongly favoring short generic responses. In this paper, we argue that a good response should smoothly connect both the preceding dialogue history and the following conversations. We strengthen this connection through mutual information maximization. To sidestep the non-differentiability of discrete natural language tokens, we introduce an auxiliary continuous code space and map such code space to a learnable prior distribution for generation purpose. Experiments on two dialogue datasets validate the effectiveness of our model, where the generated responses are closely related to the dialogue context and lead to more interactive conversations.

Deep learning is a subset of machine learning, and chances are you've already used it whether you know it or not. While you most likely have heard the terms deep learning, machine learning, and AI, you might not be as familiar with real-world deep learning solutions. As you might suspect, the terms are interrelated. If it isn't already, deep learning should be on your radar. It is a subset of machine learning, and both fall under the umbrella of artificial intelligence, which refers to systems built to carry out tasks that normally require human input.

Deep learning process for the NLP market confirms that increasing applicability in customer-centric organizations is one of the key factors that can positively impact market growth. In-depth study covering high data volume, high computing performance, improved data storage and efficient recognition of various aspects, especially in speech recognition and pattern recognition. Organizations are implementing this process to improve their product portfolio. This in-depth learning improves some of the NLP's features, such as emotional analysis, which allows companies to gain insight into their emotions, provide improved services to their customers, and predict customer behavior. Global Deep Learning Courses For NLP Market is expected to grow at a Compound Annual Growth Rate (CAGR) of 5.4%.

Artificial intelligence is advancing at a rocket's pace, and every year the field looks fundamentally different than the year before. It's often difficult to keep up with all the news and exciting results. The best way I've found is to follow the machine learning community on Twitter. Keeping track of advancements in AI is not only fun but will also help in interviews by demonstrating to hiring managers your investment in the field. To get you started following the machine learning community, here's a fairly extensive list of AI researchers and pioneers I'm following.

The considerable number of articles cover machine learning for cybersecurity and the ability to protect us from cyberattacks. Still, it's important to scrutinize how actually Artificial Intelligence (AI),Machine Learning (ML),and Deep Learning (DL) can help in cybersecurity right now, and what this hype is all about. First of all, I have to disappoint you. Unfortunately, machine learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where machine learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What's worse, now hackers are able to use machine learning to carry out all their nefarious endeavors.

What happens when you put a neural network in charge of a rules-based marketing automation solution? Would the AI emerge victorious, or does the human driver still have a thing or two to show the talented mimicker? It's an interesting question, to be sure, but more importantly, and it's an experiment that the folks at SAS – which still uses rules-based approaches in some of its analytics offerings -- actually ran, and the results might surprise you. "It beat our system," SAS Executive Vice President Oliver Schabenberger said during the SAS Analytics Experience conference held last week in San Diego, California. The result forced Schabenberger, who also holds the title of CTO and COO, to inquire about the cause. "Why is it the AI system works better than what our best minds can put together?" he said.