An important problem in fiber-optic communications is to invert the nonlinear Schr\"odinger equation in real time to reverse the deterministic effects of the channel. Interestingly, the popular split-step Fourier method (SSFM) leads to a computation graph that is reminiscent of a deep neural network. This observation allows one to leverage tools from machine learning to reduce complexity. In particular, the main disadvantage of the SSFM is that its complexity using M steps is at least M times larger than a linear equalizer. This is because the linear SSFM operator is a dense matrix. In previous work, truncation methods such as frequency sampling, wavelets, or least-squares have been used to obtain "cheaper" operators that can be implemented using filters. However, a large number of filter taps are typically required to limit truncation errors. For example, Ip and Kahn showed that for a 10 Gbaud signal and 2000 km optical link, a truncated SSFM with 25 steps would require 70-tap filters in each step and 100 times more operations than linear equalization. We find that, by jointly optimizing all filters with deep learning, the complexity can be reduced significantly for similar accuracy. Using optimized 5-tap and 3-tap filters in an alternating fashion, one requires only around 2-6 times the complexity of linear equalization, depending on the implementation.

Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties. The ConditionaL Neural Network (CLNN) is designed to consider the relational properties across frames in a temporal signal, and its extension the Masked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within the network, which enforces the network to learn in frequency bands rather than bins. Additionally, it automates the exploration of different feature combinations analogous to handcrafting the optimum combination of features for a recognition task. We applied the MCLNN to the environmental sounds of the ESC-10 dataset. The MCLNN achieved competitive accuracies compared to state-of-the-art convolutional neural networks and hand-crafted attempts.

Activation functions play a key role in neural networks so it becomes fundamental to understand their advantages and disadvantages in order to achieve better performances. This paper will first introduce common types of non linear activation functions that are alternative to the well known sigmoid function and then evaluate their characteristics. Moreover deeper neural networks will be analysed because they positively influence the final performances compared to shallower networks. They also strictly depend on the weight initialisation hence the effect of drawing weights from Gaussian and uniform distribution will be analysed making particular attention on how the number of incoming and outgoing connection to a node influence the whole network.

Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required changes to the prototype. In this work, we develop an algorithmic unsupervised-approach that automatically generates potential drug molecules given a prototype drug. We show that the molecules generated by the system are valid molecules and significantly different from the prototype drug. Out of the compounds generated by the system, we identified 35 FDA-approved drugs. As an example, our system generated Isoniazid - one of the main drugs for Tuberculosis. The system is currently being deployed for use in collaboration with pharmaceutical companies to further analyze the additional generated molecules.

"The long-term goal and true potential of AI is to replicate the complexity of human thinking at the macro level, and then surpass it to solve complex problems--problems both well-documented and currently unimaginable in nature."1 Skin cancer has reached epidemic proportions in much of the world. A simple test is needed to perform initial screening on a wide scale to encourage individuals to seek treatment when necessary. Doctor Hazel, a skin cancer screening service powered by artificial intelligence (AI) that operates in real time, relies on an extensive library of images to distinguish between skin cancer and benign lesions, making it easier for people to seek professional medical advice. Hackathons have proven to be a successful way to channel energy and technical expertise into solving very specific problems and generating bright, new ideas for applied technology.

That gets me wondering when talking about the evolution of instincts. Is it possible that that neurons respond to feedback mechanisms -- not in a conscious way, but as a response to recieving energy. If that were the case, don't you think that it's possible that neurons were fighting over who gets the animals attention, like when you listen to reason you activate certain parts of the brain. The neurons that get more attention are activated and given more energy, and if this continues for many generations, then those parts of the brain that don't get energy, kind of die off. Could it be possible that some neurons got greedy and started feeding an animal false information, just to get more energy?

The Scoop: Artificial Intelligence is surely going to be in our futures. If you want to learn more about the future with Artificial Intelligence, here are some open source libraries to feed your machine learning knowledge. Artificial Intelligence or AI is the evolution of our technology. It has the potential to help with many tasks in the future along with machine learning and deep learning. Machine learning is one of the ways you can train AIs to be able to learn on the way without bothering to write a series of complex codes.

The last couple of years have seen a dramatic increase in the popularity of deep learning, an approach to artificial intelligence inspired by a human brain's activity. To this day, deep learning models have introduced advanced solutions in the areas of computer vision, pattern recognition, natural language processing and other. To create a better understanding of the potential this technology has, let's review some successful use cases of deep learning applied in research and business.

About this course: This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We'll learn about the fundamentals of Linear Algebra and Neural Networks. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.

Today's blog post was inspired by PyImageSearch reader, Emmanuel. Emmanuel emailed me after last week's tutorial on object detection with deep learning OpenCV and asked: I really enjoyed last week's blog post on object detection with deep learning and OpenCV, thanks for putting it together and for making deep learning with OpenCV so accessible. I want to apply the same technique to real-time video. What is the best way to do this? How can I achieve the most efficiency?