What is the difference between Perceptron and Adaline?
The main difference between the two, is that a Perceptron takes that binary response (like a classification result) and computes an error used to update the weights, whereas an Adaline uses a continous response value to update the weights (so before the binarized output is produced).
Which has same probability of error?
Which has same probability of error? Explanation: BPSK is similar to bipolar PAM and both have same probability of error.
What is true regarding backpropagation?
Explanation: In backpropagation rule, actual output is determined by computing the outputs of units for each hidden layer. Explanation: The term generalized is used because delta rule could be extended to hidden layer units.
What are the general limitations of backpropagation neural network?
The gradient descent algorithm is generally very slow because it requires small learning rates for stable learning. The momentum variation is usually faster than simple gradient descent, since it allows higher learning rates while maintaining stability, but it is still too slow for many practical applications.
What is the difference between CNN and Ann Mcq?
CNN uses a more simpler alghorithm than ANN. CNN is a easiest way to use Neural Networks. They complete eachother, so in order to use ANN, you need to start with CNN. The only difference is the Convolutional component, which is what makes CNN good in analysing and predict data like images.
What is the biggest advantage using CNN?
The main advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. For example, given many pictures of cats and dogs it learns distinctive features for each class by itself. CNN is also computationally efficient.
Which tool is not suited for building ANN models?
Knowledge test and Interview questions
| Sr No | Question | Answer |
|---|---|---|
| 11 | Which tool is NOT Suited for building ANN models | C |
| 12 | Can we have multidimentional tensors | C |
| 13 | Why Tensorflow uses computational graphs? | D |
| 14 | How do we perform caculations in TensorFlow? | A |
Is PyTorch a deep learning framework?
As you might be aware, PyTorch is an open source machine learning library used primarily for applications such as computer vision and natural language processing. PyTorch is a strong player in the field of deep learning and artificial intelligence, and it can be considered primarily as a research-first library.
Is TensorFlow a deep learning framework?
Widely used deep learning frameworks such as MXNet, PyTorch, TensorFlow and others rely on GPU-accelerated libraries such as cuDNN, NCCL and DALI to deliver high-performance multi-GPU accelerated training.
Is PyTorch better than TensorFlow?
Hence, PyTorch is more of a pythonic framework and TensorFlow feels like a completely new language. These differ a lot in the software fields based on the framework you use. TensorFlow provides a way of implementing dynamic graph using a library called TensorFlow Fold, but PyTorch has it inbuilt.
Is TensorFlow owned by Google?
TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks….TensorFlow.
| Developer(s) | Google Brain Team |
|---|---|
| Website | www.tensorflow.org |
Is TensorFlow an API?
TensorFlow has APIs available in several languages both for constructing and executing a TensorFlow graph. The Python API is at present the most complete and the easiest to use, but other language APIs may be easier to integrate into projects and may offer some performance advantages in graph execution.
Is TensorFlow free to use?
TensorFlow is open source, you can download it for free and get started immediately.
Can I use TensorFlow for commercial?
Yes, TensorFlow is an open-source framework is free for commercial use also.
What is API in TensorFlow?
It is the diagram of Tensor Flow’s distributed Execution engine or the runtime engine. The other way to visualize the above picture is to think of it as a virtual machine whose language like C, C++, R, Java, etc. The use of these API’s in TensorFlow is explained below.