I have defined few parameters in my config.yaml like as below. params: epochs: 10 batch_size: 128 num_classes: 10 loss_function: sparse_categorical_cros
I watched the following video on YouTube https://www.youtube.com/watch?v=jx9iyQZhSwI where it was shown that it is possible to use Gradio and the learned model
I'm working on a lane detection project and I need to load the lane video into my colab for processing from youtube. Can i directly upload using the video's lin
I want to build a new computer for Data Science purposes. What do you think about this hardware: https://www.ldlc.com/configurateur-pc/23fe088422141bb69274a13ca
Is the GlobalAveragePooling1D Layer the same like calculating the mean with a custom Lambda Layer? The data is temporal, so x has shape (batch, time, features)
Does PyTorch's nn.Embedding support manually setting the embedding weights for only specific values? I know I could set the weights of the entire embedding laye
The app can be viewed in huggingface https://huggingface.co/spaces/rowel/asr import gradio as gr from transformers import pipeline model = pipeline(task="auto
I have Keras model: pre-trained CV model + a few added layers on top I would want to be able to do model.predict before model.fit Q: how do I instantiate model
I am trying to figure out if I can use fastai for my problem. I am trying to classify sequences of floats. Each sequence is a vector of 24 floats. In principle,
I am new to tensorflow. i've tried to fit X and y both shape=8 float64 tensors X as feature set and y as target set. X = np.array([-7.0, -4.0, -1.0, 2.0, 5.0, 8
Hello guys i am a biggner at computer vision and classification, i am trying to train a model using cnn method with tensorflow and keras, but i keep getting the
I'm trying to split DNN Models in order to execute part of the network on the edge and the rest on the cloud. Because it has to be cross-platform and work with
I am building an address matching algorithm. The main problem is that previous models like Conditional Random fields (CRF)from Paserator and Averaged Perceptron
I am trying to run some example python3 code https://docs.databricks.com/applications/deep-learning/distributed-training/horovod-runner.html on databricks GPU c
input_shape=(100,100,6) input_tensor=keras.Input(input_shape) model.add(Conv2D(32, 3, padding='same', activation='relu', input_shape=input_shape))
This is the code from https://keras.io/examples/vision/image_classification_from_scratch/ import tensorflow as tf from tensorflow import keras from tensorflo
I am building a multi-class Vision Transformer Network. When passing my values through my loss function, it always returns zero. My output layer consisits of 37
I am using easyocr methods to recognize the text on the license plate but the results are not good. I have developed deep learning model which detects license p
I'm trying to train a UNet, but for some reason I get the following error: Traceback (most recent call last): File "<ipython-input-54-b56497e81356>", l
How to apply the initializer to the tf.Variable function? Am I on the right track? def initialize_parameters(): initializer
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