Feature engineering is the process of using domain knowledge of the data to transform existing features or to create new variables from existing ones, for use in machine learning. Data in its raw format is almost never suitable for use to train machine learning algorithms. Instead, data scientists devote a substantial amount of time to pre-process the variables to use them in machine learning. As you can see, feature engineering is an umbrella term that includes multiple techniques to perform everything from filling missing values, to encoding categorical variables, to variable transformation, to creating new variables from existing ones. In this post, I highlight the main feature engineering techniques to process the data and leave it ready to use for machine learning. I describe what each technique entails, and say a few words about when we should use each technique.

Online Courses Udemy - Python Data Science with Pandas: Master 12 Advanced Projects, Work with Pandas, SQL Databases, JSON, Web APIs & more to master your real-world Machine Learning & Finance Projects Bestseller Created by Alexander Hagmann English [Auto] Students also bought Machine Learning and AI: Support Vector Machines in Python Unsupervised Machine Learning Hidden Markov Models in Python Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Cutting-Edge AI: Deep Reinforcement Learning in Python Preview this course GET COUPON CODE Description Welcome to the first advanced and project-based Pandas Data Science Course! This Course starts where many other courses end: You can write some Pandas code but you are still struggling with real-world Projects because Real-World Data is typically not provided in a single or a few text/excel files - more advanced Data Importing Techniques are required Real-World Data is large, unstructured, nested and unclean - more advanced Data Manipulation and Data Analysis/Visualization Techniques are required many easy-to-use Pandas methods work best with relatively small and clean Datasets - real-world Datasets require more General Code (incorporating other Libraries/Modules) No matter if you need excellent Pandas skills for Data Analysis, Machine Learning or Finance purposes, this is the right Course for you to get your skills to Expert Level! This Course covers the full Data Workflow A-Z: Import (complex and nested) Data from JSON files. Efficiently import and merge Data from many text/CSV files. Clean, handle and flatten nested and stringified Data in DataFrames.

Increasing use of motorized vehicles, such as these in Cameroon, are the dominate cause of injury in low- and middle-income countries.

Natural language processing (NLP), or NLP for short, is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. It is broadly defined as the automatic manipulation of natural language, like speech and text, by software or technology. Natural language processing is a form of AI that is easy to understand and start using. It can also do a lot to help you in making better business decisions. In order to make your website worth your user's time, NLP can do help you a lot.

The low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardles...

How can we efficiently compress a model while maintaining its performance? Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible. However, the existing KD methods suffer from the following limitations. First, since the student model is small in absolute size, it inherently lacks model complexity. Second, the absence of an initial guide for the student model makes it difficult for the student to imitate the teacher model to its fullest. Conventional KD methods yield low performance due to these limitations. In this paper, we propose Parameter-efficient and accurate Knowledge Distillation (Pea-KD), a novel approach to KD. Pea-KD consists of two main parts: Shuffled Parameter Sharing (SPS) and Pretraining with Teacher's Predictions (PTP). Using this combination, we are capable of alleviating the KD's limitations. SPS is a new parameter sharing method that allows greater model complexity for the student model. PTP is a KD-specialized initialization method, which can act as a good initial guide for the student. When combined, this method yields significant increase in student model's performance. Experiments conducted on different datasets and tasks show that the proposed approach improves the student model's performance by 4.4% on average in four GLUE tasks, outperforming existing KD baselines by significant margins.

General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn from the mixed-attribute data. While categorical features encoding methods can be used with the GFMMNN learning algorithms, they exhibit a lot of shortcomings. Other approaches proposed in the literature are not suitable for on-line learning as they require entire training data available in the learning phase. With the rapid change in the volume and velocity of streaming data in many application areas, it is increasingly required that the constructed models can learn and adapt to the continuous data changes in real-time without the need for their full retraining or access to the historical data. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms for the GFMM model.

The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge distillation training scheme; however, the teacher is formed by sharing the model parameters of the student network. We obtain the soft labels of the teacher by changing the bit precision of the activation stochastically at each layer of the forward-pass computation. The student model is trained with these soft labels to reduce the activation quantization noise. The cosine similarity loss is employed, instead of the KL-divergence, for KD training. As the teacher model changes continuously by random bit-precision assignment, it exploits the effect of stochastic ensemble KD. SPEQ outperforms the existing quantization training methods in various tasks, such as image classification, question-answering, and transfer learning without the need for cumbersome teacher networks.

In the era of the big data, we create and collect lots of data from all different kinds of sources: the Internet, the sensors, the consumer market, and so on. Many of the data are coming sequentially, and would like to be processed and understood quickly. One classic way of analyzing data is based on batch processing, in which the data is stored and analyzed in an offline fashion. However, when the volume of the data is too large, it is much more difficult and time-consuming to do batch processing than sequential processing. What's more, sequential data is usually changing dynamically, and needs to be understood on-the-fly in order to capture the changes. Online Convex Optimization (OCO) is a popular framework that matches the above sequential data processing requirement. Applications using OCO include online routing, online auctions, online classification and regression, as well as online resource allocation. Due to the general applicability of OCO to the sequential data and the rigorous theoretical guarantee, it has attracted lots of researchers to develop useful algorithms to fulfill different needs. In this thesis, we show our contributions to OCO's development by designing algorithms to adapt to changing environments.

Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model (i.e., student model) by transferring the information from the teacher. An essential challenge in knowledge distillation is to identify the appropriate information to transfer. In early works, only the final output of the teacher model is used as the soft label to help the training of student models. Recently, the information from intermediate layers is also adopted for better distillation. In this work, we aim to optimize the process of knowledge distillation from the perspective of kernel matrix. The output of each layer in a neural network can be considered as a new feature space generated by applying a kernel function on original images. Hence, we propose to transfer the corresponding kernel matrix (i.e., Gram matrix) from teacher models to student models for distillation. However, the size of the whole kernel matrix is quadratic to the number of examples. To improve the efficiency, we decompose the original kernel matrix with Nystr{\"{o}}m method and then transfer the partial matrix obtained with landmark points, whose size is linear in the number of examples. More importantly, our theoretical analysis shows that the difference between the original kernel matrices of teacher and student can be well bounded by that of their corresponding partial matrices. Finally, a new strategy of generating appropriate landmark points is proposed for better distillation. The empirical study on benchmark data sets demonstrates the effectiveness of the proposed algorithm. Code will be released.