Queensland Playoff stats & predictions

Training Focuses: Building Competitive Edges

In preparation for tomorrow’s showdowns between Queensland’s finest football clubs—each team’s training regimen reflects its strategic priorities designed specifically around upcoming opponents’ profiles based on detailed analysis conducted by coaches alongside sports scientists who work hand-in-hand with players every step along this journey towards achieving ultimate victory within these playoff stages!

• Tactical Drills: Teams engage in various drills aimed at enhancing positional awareness—key components when facing off against well-coordinated adversaries who excel at exploiting any lapse in structure or communication among defenders or midfielders alike!

• Mental Conditioning Sessions: Cognitive exercises tailored towards building resilience under pressure help players maintain composure during critical phases such as penalty shootouts or final minutes where every decision can tilt momentum decisively!

  andrewrogers/andrewrogers.github.io<|file_sep|>/_posts/2017-03-01-past-and-present-of-pytorch.md --- layout: post title: "Past & Present Of PyTorch" date: "2017-03-01" --- # Past A few months ago I had just started working as part of Facebook’s AI research group (FAIR) where I was involved in building [PyTorch](https://pytorch.org/). This is not my first time working with FAIR however; I was fortunate enough that my Ph.D research overlapped with theirs so I spent quite some time at Facebook collaborating with them while I was still at Oxford University. I had been developing PyTorch since early last year when FAIR announced it was open-sourcing [Caffe2](http://caffe2.ai/) – its internal framework – as part of its [research program](https://research.fb.com/category/open-source/) which includes [FAIR's other open-source projects](https://research.fb.com/category/open-source/) such as [fastText](https://github.com/facebookresearch/fastText), [DenseCap](https://github.com/facebookresearch/DenseCap), [FBNet](https://github.com/facebookresearch/FBNet), [detectron](https://github.com/facebookresearch/detectron) (now [Detectron2](https://github.com/facebookresearch/detectron2)), [SENet](https://github.com/hujie-frank/SENet), [OpenNMT](https://github.com/OpenNMT/OpenNMT-py), [fblualib](https://github.com/facebook/fblualib), etc. As you might know from my previous post ([here](http://andrew-g-r.co.uk/blog/pytorch-a-new-open-source-deep-learning-framework/)) PyTorch is based off PyTorch’s predecessor – Torch – which is an open-source machine learning library written in LuaJIT developed by Ronan Collobert et al at Facebook Research. ## What I Learned I think what I learned most during my time working on PyTorch was how important it is when developing software libraries – especially ones as complex as machine learning frameworks – that you start small rather than try do everything at once. It took me quite some time before I realized this fact but once I did it changed my whole approach towards building PyTorch (and other software libraries). The problem with starting big is that you need all your pieces connected from day one which means you spend all your time making sure everything works together rather than focusing purely on developing one small component at a time. In order to solve this problem I decided that we should start building PyTorch from scratch but only implement one feature per day; then test it thoroughly before moving onto implementing another feature. This way we were able keep our code base clean because if there were any bugs they were easy enough fix since they were isolated within just one function or class instead of being spread out throughout multiple files like what would happen if we tried doing everything at once! Another thing I learned from working with FAIR was how important communication between developers is when building software libraries like PyTorch because everyone needs clarity about what each other is doing so there aren't any conflicts between different parts being developed simultaneously without knowing about it first! # Present Nowadays PyTorch is being used by many researchers worldwide including myself who recently published our latest paper titled ["How To Train Your Transformer In One Day"](http://arxiv.org/abs/1706.03762) which uses it as its main deep learning framework! We also have several tutorials available online such as ["An Introduction To Deep Learning With PyTorch"](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) which teaches you how easy it can be when using PyTorch compared against other popular frameworks like TensorFlow or Keras. The best part about all these tutorials though? They're written by actual researchers who use PyTorch every day! So not only do they give great advice but also show off some cool stuff you can do with your own code too :) If you're interested in learning more about PyTorch then check out our blog posts here: * ["How To Use PyTorch For Deep Learning"](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) * ["An Introduction To Deep Learning With PyTorch"](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) * ["A Gentle Introduction To Machine Learning With PyTorch"](http://pytorch.org/tutorials/beginner/ml_pt_tutorial.html) * ["Deep Learning In Python Using PyTorch"](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) And if you want even more information then check out our website here: [PyTorch Website](http://pytorch.org/) <|repo_name|>andrewrogers/andrewrogers.github.io<|file_sep|>/_posts/2017-03-07-introducing-pytorch.md --- layout: post title: "Introducing PyTorch" date: "2017-03-07" --- # Introduction I’m very excited today because after months of hard work we’re finally releasing our new deep learning framework called **PyTorch**! It’s open source under Apache License v2 so anyone can use it free-of-charge without worrying about licensing fees or anything like that. # What Is It? PyTorch is an open-source machine learning library built upon Python which provides an easy-to-use interface for defining neural networks while also offering powerful tools such as automatic differentiation (autograd) which allows us define arbitrary functions over tensors without having write custom gradients ourselves! It’s worth noting here though that unlike other frameworks like TensorFlow where everything must happen inside one giant computation graph before being executed later down line when training happens this doesn’t apply here since each operation gets executed immediately after being defined meaning there’s no need worry about keeping track what order things happen either! # Why Use It? There are many reasons why someone would want use pytorch instead other popular frameworks such TensorFlow or Keras but here’s just few: 1) **Dynamic Computation Graphs**: Unlike TensorFlow which requires you define your entire model upfront before training begins (or else risk having errors thrown back at runtime), pytorch allows us define arbitrary functions over tensors without having write custom gradients ourselves! This makes it easier work with dynamic models where inputs change frequently during training since there isn’t need worry about keeping track what order things happen either! 2) **Easy Debugging**: Since each operation gets executed immediately after being defined there’s no need worry about keeping track what order things happen either! This makes debugging much easier since we can see exactly where something went wrong without having sift through hundreds lines code looking for errors manually ourselves! 3) **Pythonic Interface**: Unlike other frameworks such TensorFlow which require us write custom gradients ourselves! This makes defining models much easier since there isn’t need worry about keeping track what order things happen either! # How Do I Get Started? You can get started by installing pytorch using pip: $ pip install torch torchvision Once installed simply import torch & torchvision modules into your project like so: import torch from torchvision import datasets Then define your model using standard python syntax: class MyModel(torch.nn.Module): def __init__(self): super(MyModel,self).__init__() self.fc1 = torch.nn.Linear(784,128) self.fc2 = torch.nn.Linear(128,10) def forward(self,x): x = x.view(-1,784) x = torch.nn.functional.relu(self.fc1(x)) x = self.fc2(x) return x And finally train it using standard python syntax: model = MyModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.01) criterion = torch.nn.CrossEntropyLoss() for epoch in range(10): running_loss = .0 for i,(data,target) in enumerate(train_loader): optimizer.zero_grad() output = model(data) loss = criterion(output,target) loss.backward() optimizer.step() running_loss += loss.item() print("Epoch %d Loss %.3f"%(epoch+1, running_loss/i)) That’s all there is too it! If you want learn more then check out our documentation here: [PyTorch Documentation](http://pytorch.org/docs/stable/index.html) Or if you want see examples then check out our tutorials here: [PyTorch Tutorials](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) We hope you enjoy using pytorch as much as we enjoyed building it! <|repo_name|>andrewrogers/andrewrogers.github.io<|file_sep|>/_posts/2018-05-02-ne