is it possible to create my own object detection script with YOLO or create a Neuron Network to implement it in the NAO robot( iknow that there is a box of det
I am trying to create a copy of a nn.Sequential network. For example, the following is the easiest way to do the same- net = nn.Sequential( nn.Conv2d(16
Suppose, the following is a dataset for solving a regression problem: H -9.118 5.488 5.166 4.852 5.164 4.943 8.103 -9.152 7.470 6.452 6.069 6
I just read about the Keras weight initializers in here. In the documentation, only different initializers has been introduced. Such as: mode
Below is my code: model = Sequential([ Dense(32, input_shape=(32,), activation = 'relu'), Dense(100, activation='relu'), Dense(65, input_shape=(65
I am trying to implement a VAE for MNIST using convolutional layers using TensorFlow-2.6 and Python-3.9. The code I have is: # Specify latent space dimensions-
I am using the Physics Informed Neural Networks (PINNs) methodology to solve non-linear PDEs in high dimension. Specifically, I am using this class https://git
I'm trying to use the neuralnet package to train a model on this data set. However, I'm getting the following error which I can't understand: Error: the err
What does it mean to "unroll a RNN dynamically". I've seen this specifically mentioned in the Tensorflow source code, but I'm looking for a conceptual explanati
If you have both a classification and regression problem that are related and rely on the same input data, is it possible to successfully architect a neural net
My question is about coding a neural network which does regression (and NOT classification) using tflearn. Dataset: fixed acidity volatile acidity citric acid
I am implementing a CNN for an highly unbalanced classification problem and I would like to implement custum metrics in tensorflow to use the Select Best Model
I am trying to solve the 3-bit parity problem using the functional link neural network (Pao,1988). I am performing backpropagation to update the weights and ext
I'm developing a device for Freshwater Quality Management which can be used for freshwater bodies such as lakes and rivers. The project is spr
Let's say I have a matrix X with n, m == X.shape in PyTorch. What is the time complexity of calculating the pseudo-inverse with torch.pinverse? In other words,
Why does zero_grad() need to be called during training? | zero_grad(self) | Sets gradients of all model parameters to zero.
Why does zero_grad() need to be called during training? | zero_grad(self) | Sets gradients of all model parameters to zero.
My goal is to tune over possible network architectures that meet the following criteria: Layer 1 can have any number of hidden units from this list: [32, 64, 12
I was completing the first course of the deeplearning specialization, where the first programming assignment was to build a logistic regression model from scrat
I have being trying to fit the error during my Tensorflow course (Section 3: Neural network Regression with Tensorflow) with Udemy. import tensorflow as tf impo