The component of the gradient in direction of w1 is much larger because of the curvature of the loss function, and hence the direction of the gradient is much more towards w1, and not towards w2 (along which the minima lies). Normally, we could use a slow learning rate to deal with ...
The Importance of Optimization in Deep Learning Why Should We Care? Why the Right Kind of Optimization May Be Helpful? Course Goal ML Basics Errors in Machine Learning Models Analyzing Estimation Error in Deep Learning Models 另一个error: representation error 训练深度学习模型的困难 Common loss funct...
This is the third post in the optimization series, where we are trying to give the reader a comprehensive review of optimization in deep learning. So far, we have looked at how: Mini Batch Gradient Descent is used to combat local minima, and saddle points. How adaptive methods like Momentum...
另一种比较流行的做法是每过几个EPOCH 降低学习率。例如每5-10个 eposchs 学习率减半。 Learning rate warmup 在开始的时候使用非常小的learning rate,然后过几次迭代使用正常的learning rate。 这种方法在ResNet , Large-batch training, Transformer 或 BERT 中使用比较常见。 Cyclical learning rate. 在一个epoc...
(),lr=learning_rate,momentum=0.9)loss_list=[]forepochinrange(num_eps):index_samples=np.random.choice(a=n_samples,size=n_samples,replace=False,p=None)Y_shuffle=A[index_samples,:]forstepinrange(steps_per_epoch):Y_batch=Y_shuffle[step*batch_size:(step+1)*batch_size,:]optimizer.zero_...
深度学习(Deep Learning)中最大的特点,就是大量使用深度网络的无监督学习(unsupervised learning)。但是监督学习仍然扮演着非常重要的角色。非监督预学习(pre-training)的作用在于,评估(在监督精细迭代(fine-tuning)之后)网络可以达到的性能。这节回顾了分类模型中监督学习的理论基础,并且包含了多数模型中精细迭代所需要的...
Deep learning has achieved remarkable breakthroughs in the past decade across a wide range of application domains, such as computer games, natural language processing, pattern recognition, and medical diagnosis, to name a few. In this article, we investigate the application of deep learning techniques...
Feature extraction and optimization in deep learning The solution to the challenge of feature extraction has been modeled using deep learning (DL) models. However, knowing which subset of features is suggestive of the existence of abnormality, leading to solving the classification problem, remains a ...
We also discuss the issue of "pathological curvature" as a possible explanation for the difficulty of deep- learning and how 2 nd -order optimization, and our method in particular, effectively deals with it. 展开 会议名称: Proceedings of the 27th International Conference on Machine Learning (...
The best learning rate can depend on your data as well as the network you are training. Stochastic gradient descent momentum. Momentum adds inertia to the parameter updates by having the current update contain a contribution proportional to the update in the previous iteration. This results in ...