- The one and only newsletter about the latest research, current trends, and upcoming events in Machine Learning, flavored with graphs.
- The best free & open-source vector graphics software allows you to enjoy creativity & easily create quality images that are ideal for detailed illustrations.
- Written by Dheepan Ramanan (@dheepan_ramanan), Data Scientist and Ivan Kopas (@ivan_kopas), Machine Learning Engineer Last Friday ARK Invest released a new price target for Tesla as well as an updated, open-source model. The scale of autonomous ride hailing networks and ARK’s estimate for Tesla’s dominance emerged as the most contentious elements in the model. These components contribute nearly 50% of ARK’s $3k 2025 price target. On twitter there has been considerable debate on the size of the Robotaxi market and Tesla’s lead in autonomous driving, questioning whether Tesla’s Full Self Driving (FSD) approach can be reverse-engineered and replicated by the competitors.
- For instance, you might learn in an online course how to run a YOLO network, but a real-world use case might asks for 7 YOLO networks in distributed GPUs and a HydraNet architecture. What the heck is…
- The ability to store and manipulate information is a hallmark of computational systems. Whereas computers are carefully engineered to represent and perform mathematical operations on structured data, neurobiological systems adapt to perform analogous functions without needing to be explicitly engineered. Recent efforts have made progress in modelling the representation and recall of information in neural systems. However, precisely how neural systems learn to modify these representations remains far from understood. Here, we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory. Specifically, we drive an RNN with examples of translated, linearly transformed or pre-bifurcated time series from a chaotic Lorenz system, alongside an additional control signal that changes value for each example. By training the network to replicate the Lorenz inputs, it learns to autonomously evolve about a Lorenz-shaped manifold. Additionally, it learns to continuously interpolate and extrapolate the translation, transformation and bifurcation of this representation far beyond the training data by changing the control signal. Furthermore, we demonstrate that RNNs can infer the bifurcation structure of normal forms and period doubling routes to chaos, and extrapolate non-dynamical, kinematic trajectories. Finally, we provide a mechanism for how these computations are learned, and replicate our main results using a Wilson–Cowan reservoir. Together, our results provide a simple but powerful mechanism by which an RNN can learn to manipulate internal representations of complex information, enabling the principled study and precise design of RNNs. Recurrent neural networks (RNNs) can learn to process temporal information, such as speech or movement. New work makes such approaches more powerful and flexible by describing theory and experiments demonstrating that RNNs can learn from a few examples to generalize and predict complex dynamics including chaotic behaviour.
- Databases are the cornerstone of any Software Applications. You will need one or more databases to develop almost all kind of Software Applications: Web, Enterprise, Embedded Systems, Real-Time…
- Why Germany is becoming a career destination for many researchers.
- An introduction to what a Mesh, Shader and Material is in Unity, how to set Shader Properties from C#, a brief look at Forward vs Deferred rendering and some information about Material instances and Batching. HLSL | Unity Shader Tutorials, @Cyanilux
- Let’s imagine a hypothetical situation. There’s an infection going round, and we want to predict the future severity of someone’s illness. There is a test that offers a good prediction. Let’s say the outcome of the test has a correlation of 0.78 with the patient's severity of infection. The problem with the test is that…
- FastAPI framework, high performance, easy to learn, fast to code, ready for production
- This blog post is going to be a little different to the previous few posts, there will be essentially no mathematics nor code. It is not intended as a how to or instructional post, merely a repository for my current opinions.
- A blog about maths, probability, modelling and computing.
- In this blog post we will cover some of the basics of the Barnes Hut algorithm. This is completely new to me, it is not an algorithm I’ve used/studied before (and I am by no means an astrophysicist). Nonetheless it has piqued my interest so I have decided to write about it. In this blog I will be talking about 2 dimensions unless otherwise stated, this just makes the resulting code run a little quicker and output easier to visualise. Modifying the 2d code to be 3d (or even higher dimension) requires only minor revisions.

*Nature**585 (7825): 357--362*(*2020*)*Nature**567 (7748): 305--307*(*2019*)*Notices of the AMS*(*2003*)- (
*2017*)*undefined.* *Commun. ACM**55 (10): 78--87*(*October 2012*)*Palgrave Communications**4 (1): 82--*(*2018*)