machine-learning deep-neural-networks deep-learning neural-network mxnet tensorflow scikit-learn keras ml dnn pytorch onnx Updated Feb 16, 2025 Python microsoft / ailab Star 7.8k Code Issues Pull requests Experience, Learn and Code the latest breakthrough innovations with Microsoft AI javascript...
pythontutorialtheanodeep-learninganacondatensorflowkeraskeras-tutorialscudnnkeras-tensorflow UpdatedJul 25, 2023 Jupyter Notebook DefTruth/CUDA-Learn-Notes Star2.8k Code Issues Pull requests 📚200+ Tensor/CUDA Cores Kernels, ⚡️flash-attn-mma, ⚡️hgemm with WMMA, MMA and CuTe (98%~100%...
Autokeras starts with three-layers CNN. Each convolutional layer is actually a convolutional block of a ReLU layer, a batch-normalization layer, the convolutional layer, and a pooling layer. All the convolutional layers are with kernel size equal to three, stride equal to one, and number of ...
http://deeplearning.net/tutorial/lenet.html http://cs231n.github.io/convolutional-networks/ http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/ https://cambridgespark.com/content/tutorials/convolutional-neural-networks-with-keras/index.html http://colah.github.io/posts/2014-03-...
This also leads to substantial speed gains (our CNTK 204 Tutorial now runs more than 2x faster). 对于embedded layer的Aggregation on sparse gradient This change saves costly conversion from sparse to dense before gradient aggregation when embedding vocabulary size is huge. It is currently ...
作者发现(Deep networks with stochastic depth)通过类似Dropout的方法随机扔掉一些层,能够提高ResNet的泛化能力。于是设计了DenseNet。 DenseNet 将ResNet的residual connection 发挥到了极致,它做了两个重要的设计,一是网络的每一层都直接与其前面层相连,实现特征的重复利用,第二是网络的每一层都很窄,达到降低冗余性的...
To learn how to install the NVIDIA CUDA drivers, CUDA Toolkit, and cuDNN, I recommend you read myUbuntu 18.04 and TensorFlow/Keras GPU install guide— once you have the proper NVIDIA drivers and toolkits installed, you can come back to this tutorial. ...
I am experimenting with CuDNNLSTMs and, i dont know why, even though i am following a tutorial on this, i get this weird error, that i can understand, but i can't debug: So i have a 4073 time-series * 175 features array and i am trying to pass those 175...
window_size = 30 dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size) model = tf.keras.models.Sequential([ tf.keras.layers.Dense(10, activation="relu", input_shape=[window_size]), tf.keras.layers.Dense(10, activation="relu"), tf.keras.layers.Dense(1) ]...
As of building from the parts, I followed the tutorial of [this video]. Although the parts are slightly different, the building process is quite similar. I have no previous experience building by my own, but with this tutorial I was able to make it work within 3 hours. (It should take...