This course was designed to bring anyone up to speed on Machine Learning & Deep Learning in the shortest time. This particular field in computer engineering has gained an exponential growth in interest worldwide following major progress in this field. The course starts with building on foundation concepts relating to Neural Networks. Then the course goes over Tensorflow libraries and Python language to get the students ready to build practical projects. You will build a practical Tensorflow project for each of the above Neural Networks.

"It was the worst possible time, Everyone else was doing something different." In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts created computational models based on math algorithms called Threshold Logic Unit (TLU) to describe how neurons might work. Simulations of neural networks were possible until computers became more advanced in the 1950s. Before the 2000s it was considered one of the worst areas of research. LeCun and Hinton variously mentioned how in this period their papers were routinely rejected from being published due to their subject being neural networks.

Transformer based models are creating tremendous impact in the space of NLP as they have proven to be effective in a wide range of tasks such as POS tagging, machine translation, named-entity recognition, and a series of text classification tasks. This year saw the introduction to a whole family of transformer-based language models such as BERT, Transformer-XL, and GPT-2, among others. Langauge models, in general, offer desirable properties that can be leveraged in a transfer learning setting where you train a model with large-scale data to learn the properties of language in an unsupervised setting. The resulting model and weights can then be fine-tuned and be applied in low-resourced regimes to address different NLP tasks. In particular, it's exciting to see the use of BERT in different domains such as text classification, text summarization, text generation, and information retrieval.

Artificial intelligence routinely produces startling achievements, but those advances require staggering amounts of computing power and electricity. Last month, researchers at OpenAI in San Francisco revealed an algorithm capable of learning, through trial and error, how to manipulate the pieces of a Rubik's Cube using a robotic hand. It was a remarkable research feat, but it required more than 1,000 desktop computers plus a dozen machines running specialized graphics chips crunching intensive calculations for several months. The effort may have consumed about 2.8 gigawatt-hours of electricity, estimates Evan Sparks, CEO of Determined AI, a startup that provides software to help companies manage AI projects. A spokesperson for OpenAI questioned the calculation, noting that it makes several assumptions. But OpenAI declined to disclose further details of the project or offer an estimate of the electricity it consumed.

Neural architecture search(NAS) is one of the hottest trends in modern deep learning technologies. Conceptually, NAS methods focus on finding a suitable neural network architecture for a given problem and dataset. Think about it as making machine learning architecture a machine learning problem by itself. In recent years, there have been an explosion in the number of NAS techniques that are making inroads into mainstream deep learning frameworks and platforms. However, the first generation of NAS models have encountered plenty of challenges adapting neural networks that were tested on one domain to another domain.

So, this is the second Computer Vision project that I have implemented. If you haven't checked out the first project that is the Facial Keypoint Detection's blog already, I'll leave a link here. Now, You might think what in the world is image captioning? and How can it be done automatically? Okay! so, in order to explain that to you in simple "gestures", let me introduce, the almighty Pikotaro. I'll take it from here.

Deep neural networks (DNNs) are increasingly powering high-stakes applications such as autonomous cars and healthcare; however, DNNs are often treated as "black boxes" in such applications. Recent research has also revealed that DNNs are highly vulnerable to adversarial attacks, raising serious concerns over deploying DNNs in the real world. To overcome these deficiencies, we are developing Massif, an interactive tool for deciphering adversarial attacks. Massif identifies and interactively visualizes neurons and their connections inside a DNN that are strongly activated or suppressed by an adversarial attack. Massif provides both a high-level, interpretable overview of the effect of an attack on a DNN, and a low-level, detailed description of the affected neurons. These tightly coupled views in Massif help people better understand which input features are most vulnerable or important for correct predictions.

Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. The recent link between the quantile score function and the Asymmetric Laplace density has led to a flexible likelihood-based framework for joint modelling of VaR and ES. It is of high interest in financial applications to be able to capture the underlying joint dynamics of these two quantities. We address this problem by developing a hybrid model that is based on the Asymmetric Laplace quasi-likelihood and employs the Long Short-Term Memory (LSTM) time series modelling technique from Machine Learning to capture efficiently the underlying dynamics of VaR and ES. We refer to this model as LSTM-AL. We adopt the adaptive Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference in the LSTM-AL model. Empirical results show that the proposed LSTM-AL model can improve the VaR and ES forecasting accuracy over a range of well-established competing models.

Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time. In this work, we focus on the task of predicting new interactions in the network of bank clients and treat it as a link prediction problem. We propose a new graph neural network model, which uses not only the topological structure of the network but rich time-series data available for the graph nodes and edges. We evaluate the developed method using the data provided by a large European bank for several years. The proposed model outperforms the existing approaches, including other neural network models, with a significant gap in ROC AUC score on link prediction problem and also allows to improve the quality of credit scoring. 1 Introduction It is important for the financial institutions to know their client well in order to mitigate credit risks [ Siddiqi, 2012 ], deal with fraud [ Phua et al., 2010 ] and recommend relevant services [ Bruss et al., 2019 ] . One of the defining properties of a particular bank client is his or her social and financial interactions with other people. It motivates to look on the bank clients as on the network of interconnected agents [ Tran et al., 2019; Bruss et al., 2019; Weber et al., 2018 ] .

The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings. While deep learning (DL) models has become a promising predictive technology with near-human accuracy, they commonly suffer from a lack of explainability, which is an important aspect for clinical deployment of DL models in the highly regulated healthcare industry. For example, localizing critical findings in an image is useful for explaining the predictions of DL classification algorithms. While there have been a host of joint classification and localization methods for computer vision, the state-of-the-art DL models require locally annotated training data in the form of pixel level labels or bounding box coordinates. In the medical domain, this requires an expensive amount of manual annotation by medical experts for each critical finding. This requirement becomes a major barrier for training models that can rapidly scale to various findings. In this work, we address these shortcomings with an interpretable DL algorithm based on multi-instance learning that jointly classifies and localizes critical findings in CXR without the need for local annotations. We show competitive classification results on three different critical findings (pneumothorax, pneumonia, and pulmonary edema) from three different CXR datasets.