Knowledge is acquired by humans through experience, and no boundary is set between the kinds of knowledge or skill levels we can achieve on different tasks at the same time. When it comes to Neural Networks, that is not the case, the major breakthroughs in the field are extremely task and domain specific. Vision and language are dealt with in separate manners, using separate methods and different datasets. In this work, we propose to use knowledge acquired by benchmark Vision Models which are trained on ImageNet to help a much smaller architecture learn to classify text. After transforming the textual data contained in the IMDB dataset to gray scale images. An analysis of different domains and the Transfer Learning method is carried out. Despite the challenge posed by the very different datasets, promising results are achieved. The main contribution of this work is a novel approach which links large pretrained models on both language and vision to achieve state-of-the-art results in different sub-fields from the original task. Without needing high compute capacity resources. Specifically, Sentiment Analysis is achieved after transferring knowledge between vision and language models. BERT embeddings are transformed into grayscale images, these images are then used as training examples for pre-trained vision models such as VGG16 and ResNet Index Terms: BERT, Convolutional Neural Networks, Domain Adaptation, image classification, Natural Language Processing, t-SNE, text classification, Transfer Learning

The abundance of clouds, located both spatially and temporally, often makes remote sensing (RS) applications with optical images difficult or even impossible to perform. Traditional cloud removing techniques have been studied for years, and recently, Machine Learning (ML)-based approaches have also been considered. In this manuscript, a novel method for the restoration of clouds-corrupted optical images is presented, able to generate the whole optical scene of interest, not only the cloudy pixels, and based on a Joint Data Fusion paradigm, where three deep neural networks are hierarchically combined. Spatio-temporal features are separately extracted by a conditional Generative Adversarial Network (cGAN) and by a Convolutional Long Short-Term Memory (ConvLSTM), from Synthetic Aperture Radar (SAR) data and optical time-series of data respectively, and then combined with a U-shaped network. The use of time-series of data has been rarely explored in the state of the art for this peculiar objective, and moreover existing models do not combine both spatio-temporal domains and SAR-optical imagery. Quantitative and qualitative results have shown a good ability of the proposed method in producing cloud-free images, by also preserving the details and outperforming the cGAN and the ConvLSTM when individually used. Both the code and the dataset have been implemented from scratch and made available to interested researchers for further analysis and investigation.

In this article I want to share about the evolution of text analysis algorithms in last decade. Natural Language(NLP)has been around for a long time, In fact, a very simple bag of words model was introduced in the 1950s. But in this article I want to focus on evolution of NLP during recent times. There has been enormous progress in the field since 2013 due to the evolution and the advancement of machine learning algorithms together with reduced cost of computation and memory. In 2013, a research team led by Thomas Michael off at Google introduced the Word2Vec algorithm.

This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists and python developers to learn the AzureML . This course is designed based on latest changes done in DP-100 Certification. This course would also be useful for the experts who needs to know how to create and deploy a machine learning environment in production. Will train machine learning and deep learning algorithm in azure ml in local machine and same code will be executed in azure as well.

Apple's machine learning research team has developed a no-code Artificial Intelligence (AI) platform called Trinity. This AI is designed to enable machine learning researchers and non-technical geospatial domain experts alike to experiment with different signals or datasets in order to solve problems on their own, such as complex issues that arise from the world around us every day. The ability to solve diverse problems is made possible by transforming complex Spatio-temporal datasets so that they can be consumed and solved with a standard deep learning model, like Convolutional Neural Networks. This new way of looking at data has the potential to formulate disparate problems in one standardized form for easy consumption. Trinity is a powerful software platform for domain experts to share the stage with scientists and engineers in solving business-critical problems.

Supervised Deep Learning is similar to concept learning in humans and animals, the difference being that the student in the former case is a computational network. Supervised deep learning frameworks are trained using well-labelled data. It teaches the learning algorithm to generalise from the training data and to implement in unseen situations. After completing the training process, the model is tested on a subset of the testing set to predict the output. Thus, datasets containing inputs and correct outputs become critical as they help the model learn faster. Regression and classification are two subfields of supervised machine learning.

Google researchers found that "data cascades -- compounding events causing negative, downstream effects from data issues -- triggered by conventional AI/ML practices that undervalue data quality… are pervasive (92% prevalence), invisible, delayed, but often avoidable." Lets discuss on the trend being followed widely for most or all of the AI use cases across the organizations. Just to make myself clear the term AI over here in being referred as an umbrella encompassing our DataScience/Machine Learning and Deep Learning Use cases. The Two basic components of all AI systems are Data and Model, both go hand in hand in producing desired results. We do realize that the AI community has been biased towards putting more effort in the model building One plausible reason is that AI industry closely follows academic research in AI.

Petuum's CASL research and engineering team has won this year's OSDI 2021 Best Paper Award. This effort is led by Dr. Aurick Qiao who heads the Composability, Automatic, and Scalable Learning (CASL) research and engineering team at Petuum. Dr. Qiao received the Jay Lepreau Best Paper Award at the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI) 2021 for the paper he co-authored, Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning which captures the revolutionary work implemented using one of CASL's key components, AdaptDL. Current live application of Pollux can be implemented via AdaptDL that integrates with PyTorch, Microsoft NNI, and with Ray coming soon. Pollux as implemented by AdaptDL improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level.

Deep Learning models have made incredible progress in discriminative tasks. This has been fueled by the advancement of deep network architectures, powerful computation, and access to big data. Having a large dataset is crucial for the performance of the deep learning model. Thus with data augmentation we can improve the performance of the model with the data we already have. Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks.

In recent years, artificial intelligence agents have succeeded in a range of complex game environments. For instance, AlphaZero beat world-champion programs in chess, shogi, and Go after starting out with knowing no more than the basic rules of how to play. But AlphaZero still trained separately on each game -- unable to simply learn another game or task without repeating the RL process from scratch. The same is true for other successes of RL, such as Atari, Capture the Flag, StarCraft II, Dota 2, and Hide-and-Seek. DeepMind's mission of solving intelligence to advance science and humanity led us to explore how we could overcome this limitation to create AI agents with more general and adaptive behaviour.