- If you’re like most people who work with data on a regular basis, you’re probably hearing about data science as a career change option and wondering “Is data science right for me?
- Although I can’t answer those lingering questions for you – I can tell you my experience, as a person who approached data science as a career change.
- In this exclusive premier interview for LinkedIn Learning, I discuss how I transitioned myself from an Environmental Engineer to a Data Scientist.
- There’s a lot covered in this lively 30-minute session; And if you’re considering data science as a career change, watching it should help you get a better idea what to expect, and hopefully a little inspiration to ignite your passion.
- If you liked this video and want to learn more about how to make the transition into data science as a career change, then be sure to check out my LinkedIn Learning / Lyndas training courses here.
If you’re like most people who work with data on a regular basis, you’re probably hearing about data science as a career change option and wondering…
Continue reading “Data Science As A Career Change – My Story as a Video Interview”
- That may be true, but according to a Valve spokesperson writing in the thread, it wouldn’t be the best approach.
- “Instead, you’d want to take a machine-learning approach, training (and continuously retraining) a classifier that can detect the differences between cheaters and normal/highly-skilled players.”
- “The process of parsing, training, and classifying player data places serious demands on hardware, which means you want a machine other than the server doing the work.
- And because you don’t know ahead of time who might be using this kind of cheat, you’d have to monitor matches as they take place, from all ten players’ perspectives.”
- The spokesperson continued: “There are over a million CS:GO matches played every day, so to avoid falling behind you’d need a system capable of parsing and processing every demo of every match from every player’s perspective, which currently means you’d need a datacenter capable of powering thousands of CPU cores.”
Anti-cheat software has a lot of weight to pull in the modern age, with few major games going to market without some form of online competitive mode. Detecting and smiting cheaters is a thankless task too, with most folk ignoring anti-cheat technology unless it stops working effectively. Typically enough, Valve has a new approach in mind.During a discussion on the Counter-Strike: Global Offensive Reddit page, one user asked why Valve doesn’t implement auto-detection for spinbots – bots that literally spin on the spot, auto-killing every player in range. Other users posit quite reasonably that it wouldn’t be hard to detect this supernaturally quick and effective player behavior. That may be true, but according to a Valve spokesperson writing in the thread, it wouldn’t be the best approach.”So some bad news: any hard-coded detection of spin-botting leads to an arms race with cheat developers – if they can find the edges of the heuristic you’re using t
Continue reading “Valve wants to take a ‘machine learning’ approach to Counter-Strike anti-cheat”
- Speed Up Deep Learning Training With GPU-Accelerated Caffe
- Caffe is a deep learning framework made with expression, speed, and modularity in mind.
- GPU Applications Case Studies Why Choose Tesla Servers and Workstations Where to Buy
- The popular computer vision framework is developed by the Berkeley Vision and Learning Center (BVLC), as well as community contributors.
- Software and Hardware Tesla Product Literature NVLink High-speed Interconnect Tesla Software Features Software Development Tools CUDA Training and Consulting GPU Cloud Computing OpenACC GPU Directives Data Center Management Tools
Run deep learning training with Caffe up to 65% faster on the latest NVIDIA Pascal GPUs. Learn more.
Continue reading “Caffe Deep Learning Framework and GPU Acceleration”
- In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and Theano and TensorFlow and was built with OpenAI Gym in mind.
- keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras .
- You can extend keras-rl according to your own needs.
- The repo provides some weights that were obtained by running (at least some) of the examples that are included in keras-rl .
- Keras-rl works with OpenAI Gym out of the box.
Read the full article, click here.
@kdnuggets: “Deep #ReinforcementLearning for #Keras: state-of-the art #DeepLearning in #Python”
keras-rl – Deep Reinforcement Learning for Keras.
- Our new problem is to minimize the cost function given this added constraint.
- We don’t want the model to memorize the training dataset, we want a model that generalizes well to new, unseen data.
- In more specific terms, we can think of regularization as adding (or increasing the) bias if our model suffers from (high) variance (i.e., it overfits the training data).
- A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.
- If we regularize the cost function (e.g., via L2 regularization), we add an additional to our cost function (J) that increases as the value of your parameter weights (w) increase; keep in mind that the regularization we add a new hyperparameter, lambda, to control the regularization strength.
Read the full article, click here.
@kdnuggets: “Regularization in Logistic Regression: Better Fit & Generalization? #MachineLearning @rasbt”
A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.
Regularization in Logistic Regression: Better Fit and Better Generalization?