We are looking for deep technical topics related to Data Science. We are committed to diversity and believe in transparency. Hence, all proposals will be public. Registered user will be able to comment on your proposal. You are required to reply to those comments to provide clarifications, explain revisions and respond to questions.

Build predictive deep learning models using Keras & Tensorflow Python, Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts. Instructor: Start Tech Academy Enroll Now - Neural Networks (ANN) using Keras and TensorFlow in Python About this Course You are looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right? You have found the right Neural Networks course! Add To Cart - GET COUPON CODE After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models.

The reality is that for many of applied machine learning's use cases, there is no need to train a new model from scratch. For example, if you are developing a conversational agent, Google's Meena is almost certainly going to outperform your model. If you're developing a text generator, you should use OpenAI's GPT-2 instead of building your own from scratch. For object detection, a model like YOLOv3 is probably your best bet. Thanks to transfer learning--a process in which the "knowledge" of a neural network is fine tuned to a new domain--you can take a relatively small amount of data and fine tune these open source, state-of-the-art models to your task.

The graph represents a network of 2,189 Twitter users whose tweets in the requested range contained "deeplearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 25 March 2020 at 01:00 UTC. The requested start date was Wednesday, 25 March 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 4-hour, 34-minute period from Monday, 23 March 2020 at 19:26 UTC to Wednesday, 25 March 2020 at 00:01 UTC.

Deep learning, a subset of machine learning and artificial intelligence (AI), is predicted to provide formidable momentum for the adoption and growth of artificial intelligence in the Asia-Pacific (APAC) region. The next few years will see deep learning become part of main-stream deployments, bringing commendable changes to businesses in the region, says GlobalData, a leading data and analytics company. GlobalData estimates the APAC region to account for approximately 30% of the global AI platforms' revenue (around US$97.5bn) by 2024. However, the share is expected to significantly go up, given the incumbent technology companies and the increasing number of start-ups that specialize in this field. Furthermore, the technological enhancements supporting higher computation capabilities (CPU and GPU), and the huge amount of data, which is predicted to grow multiple folds due to the growth of connected devices ecosystem, are expected to contribute to this growth. Some of the other key usage areas of deep learning include multi-lingual chatbots, voice and image recognition, data processing, surveillance, fraud detection and diagnostics.

Deep learning, a subset of machine learning and artificial intelligence (AI), is predicted to provide formidable momentum for the adoption and growth of artificial intelligence in the Asia-Pacific (APAC) region. The next few years will see deep learning become part of main-stream deployments, bringing commendable changes to businesses in the region, says GlobalData, a leading data and analytics company. GlobalData estimates the APAC region to account for approximately 30% of the global AI platforms' revenue (around US$97.5bn) by 2024. However, the share is expected to significantly go up, given the incumbent technology companies and the increasing number of start-ups that specialize in this field. Furthermore, the technological enhancements supporting higher computation capabilities (CPU and GPU), and the huge amount of data, which is predicted to grow multiple folds due to the growth of connected devices ecosystem, are expected to contribute to this growth. Some of the other key usage areas of deep learning include multi-lingual chatbots, voice and image recognition, data processing, surveillance, fraud detection and diagnostics.

Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time. In this project, we investigate the performance of the Transformer architectures-BERT and Transformer-XL for the language modeling task. We use a sub-word model setting with the Finnish language and compare it to the previous State of the art (SOTA) LSTM model. BERT achieves a pseudo-perplexity score of 14.5, which is the first such measure achieved as far as we know. Transformer-XL improves upon the perplexity score to 73.58 which is 27\% better than the LSTM model.

Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowing how much confidence there is in a computer-based medical diagnosis is essential for gaining clinicians trust in the technology and therefore improve treatment. Today, the 2019 Coronavirus (SARS-CoV-2) infections are a major healthcare challenge around the world. Detecting COVID-19 in X-ray images is crucial for diagnosis, assessment and treatment. However, diagnostic uncertainty in the report is a challenging and yet inevitable task for radiologist. In this paper, we investigate how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is highly correlates with accuracy of prediction. We believe that the availability of uncertainty-aware deep learning solution will enable a wider adoption of Artificial Intelligence (AI) in a clinical setting.

Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces. In practice, however, this assumption usually does not hold. To achieve nonlinear subspace clustering, we propose a novel method, called kernel truncated regression representation. Our method consists of the following four steps: 1) projecting the input data into a hidden space, where each data point can be linearly represented by other data points; 2) calculating the linear representation coefficients of the data representations in the hidden space; 3) truncating the trivial coefficients to achieve robustness and block-diagonality; and 4) executing the graph cutting operation on the coefficient matrix by solving a graph Laplacian problem. Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces. The first advantage makes our method efficient in handling large-scale datasets, and the second one enables the proposed method to conquer the nonlinear subspace clustering challenge. Extensive experiments on six benchmarks demonstrate the effectiveness and the efficiency of the proposed method in comparison with current state-of-the-art approaches.

In this paper, we propose and end-to-end deep Chinese font generation system. This system can generate new style fonts by interpolation of latent style-related embeding variables that could achieve smooth transition between different style. Our method is simpler and more effective than other methods, which will help to improve the font design efficiency.