I've been working with Logistic Regression to fit my data and make good predictions as a beginner. But as soon as I'm done with that I feel empty and the reason for that was simply because I was performing the same task iteratively (i.e. Just fitting and predicting which could get boring because of not understanding what's going on behind the scene). I've always thought of how is the model able to perform the predictions. But then I sat down one day and studied how the Logistic regression can perform its prediction.

In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. This course would enable you to gain insight into the realm of Quantum Computing. The students would be able to learn and develop expertise in Quantum algorithms, gates and implementation of these codes. The undergraduate students would particularly find it very imperative and for realizing their final year projects and reports. Furthermore, this course is an introduction to the fundamental concepts of quantum circuits and algorithms.

Transformers are taking the world of NLP by storm. After being introduced in Vaswani et al.'s Attention is all you need work back in 2017, they – and particularly their self-attention mechanism requiring no recurrent elements to be used anymore – have proven to show state-of-the-art performance on a wide variety of language tasks. Nevertheless, what's good can still be improved, and this process has been applied to Transformers as well. After the introduction of the'vanilla' Transformer by Vaswani and colleagues, a group of people at OpenAI have used just the decoder segment and built a model that works great. However, according to Devlin et al., the authors of a 2018 paper about pretrained Transformers in NLP, they do one thing wrong: the attention that they apply is unidirectional. This hampers learning unnecessarily, they argue, and they proposed a bidirectional variant instead: BERT, or Bidirectional Encoder Representations from Transformers.

A Gaussian Process is a non-parametric model that can be used to represent a distribution over functions. Lets break this definition down. Parametric models assume that the data distribution (set of input points, images etc.) can be entirely defined in terms of a finite set of parameters theta. For example in simple linear regression the parameters are the m and c in the equation y mx c. Such that if the model is given the parameters, a future prediction is independent of the data.

In the perfect equilibrium, the generator would capture the general training data distribution. As a result, the discriminator would be always unsure of whether its inputs are real or not. In the DCGAN paper, the authors describe the combination of some deep learning techniques as key for training GANs. These techniques include: (i) the all convolutional net and (ii) Batch Normalization (BN). The first emphasizes strided convolutions (instead of pooling layers) for both: increasing and decreasing feature's spatial dimensions.

GANs are one of the very few machine learning techniques which has given good performance for generative tasks, or more broadly unsupervised learning. In particular, they have given splendid performance for a variety of image generation related tasks. Yann LeCun, one of the forefathers of deep learning, has called them "the best idea in machine learning in the last 10 years". Most importantly, the core conceptual ideas associated with a GAN are quite simple to understand (and in-fact, you should have a good idea about them by the time you finish reading this article). In this article, we'll explain GANs by applying them to the task of generating images.

Let's say there's a very cool party going on in your neighborhood that you really want to go to. But, there is a problem. To get into the party you need a special ticket -- that was long sold out. Wait up! Isn't this a Generative Adversarial Networks article? But bear with me for now, it is going to be worth it.