Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. There are innumerable possibilities to explore using Image Classification. If you have completed the basic courses on Computer Vision, you are familiar with the tasks and routines involved in Image Classification tasks. Image Classification tasks follow a standard flow – where you pass an image to a deep learning model and it outcomes the class or the label of the object present. While learning Computer Vision, most often a project that would be equivalent to your first hello world project, will most likely be an image classifier. You attempt to solve something like the digit recognition on MNIST Digits dataset or maybe the Cats and Dog Classification problem.

The world of artificial intelligence (AI) is revolutionizing the way we live, though it has become something of an acronym soup. From DL to ML, SSD to CNN (not this one), there are many interesting facets of AI and plenty of opportunities for advancements that affect our everyday lives. It's a lucrative career field well worth exploring, and we've got just the place to start.

Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google's most powerful, recently released open source platform to build and deploy AI models in practice. AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab.

This video shows our Driving Intelligence completing an unprotected right turn through an intersection near our London King's Cross HQ. This is one of the hardest manoeuvres for autonomy and behaviour Wayve has been able to learn with end-to-end deep learning. Unlike other approaches, we learn to drive from data using camera-first sensing without needing an HD-map. We train our system to understand the world around it with computer vision and learn to drive with imitation and reinforcement learning. In this example, our Driving Intelligence is able to navigate the complex lane layout, avoiding the car which runs the red light and passing the pedestrians with human-like confidence.

A deep-learning technique that can learn a so-called "fitness function" from a set of sample solutions to a problem has been devised. This technique was initially trained to solve the Rubik's cube, the popular 3-D combination puzzle invented by Hungarian sculptor Ernő Rubik. The aim was to use machine learning to learn to solve the Rubik's cube. Rubik's cube is a very complex puzzle, but any of the vast numbers of combinations is at most 20 steps from a solution. So the approach here is to try and solve the problem by learning to do each of those steps individually. The technique is based on two main approaches: stepwise learning and the use of a deep neural network.

The ability to automate human sight is opening up massive opportunities for value creation across ... [ ] every sector of the economy. Computer vision is the most technologically mature field in modern artificial intelligence. This is about to translate into enormous commercial value creation. The deep learning revolution has its roots in computer vision. At the now-historic 2012 ImageNet competition, Geoff Hinton and team debuted a neural network--a novel architecture at the time--whose performance eclipsed all previous efforts at computer-based image recognition. The era of deep learning was born, with computer vision as its original use case.

Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through the features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.

Activation functions like ReLu, Sigmoid, and Tanh are used to add a non-linear property to the neural network. This way, the network can model more complex structures and patterns in the data.

The training of Deep Neural Networks (DNN) is costly, thus DNN can be considered as the intellectual properties (IP) of model owners. To date, most of the existing protection works focus on verifying the ownership after the DNN model is stolen, which cannot resist piracy in advance. To this end, we propose an active DNN IP protection method based on adversarial examples against DNN piracy, named ActiveGuard. ActiveGuard aims to achieve authorization control and users' fingerprints management through adversarial examples, and can provide ownership verification. Specifically, ActiveGuard exploits the elaborate adversarial examples as users' fingerprints to distinguish authorized users from unauthorized users. Legitimate users can enter fingerprints into DNN for identity authentication and authorized usage, while unauthorized users will obtain poor model performance due to an additional control layer. In addition, ActiveGuard enables the model owner to embed a watermark into the weights of DNN. When the DNN is illegally pirated, the model owner can extract the embedded watermark and perform ownership verification. Experimental results show that, for authorized users, the test accuracy of LeNet-5 and Wide Residual Network (WRN) models are 99.15% and 91.46%, respectively, while for unauthorized users, the test accuracy of the two DNNs are only 8.92% (LeNet-5) and 10% (WRN), respectively. Besides, each authorized user can pass the fingerprint authentication with a high success rate (up to 100%). For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of the DNN model will not be affected. Further, ActiveGuard is demonstrated to be robust against fingerprint forgery attack, model fine-tuning attack and pruning attack.

Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop a first principle graph-time convolutional neural network (GTCNN). The GTCNN is a compositional architecture with each layer comprising a graph-time convolutional module, a graph-time pooling module, and a nonlinearity. We develop a graph-time convolutional filter by following the shift-and-sum principles of the convolutional operator to learn higher-level features over the product graph. The product graph itself is parametric so that we can learn also the spatiotemporal coupling from data. We develop a zero-pad pooling that preserves the spatial graph (the prior about the data) while reducing the number of active nodes and the parameters. Experimental results with synthetic and real data corroborate the different components and compare with baseline and state-of-the-art solutions.