Deep Learning Assignment Help

Codersarts  is a top rated website for  Deep Learning Assignment Help, Project Help, Homework Help and Mentorship. Our dedicated team of Deep learning assignment experts will help and guide you throughout your Deep learning journey

Deep Learning  Assignment Help | Deep Learning  Homework Help

Looking for an expert to provide you help in  Deep Learning  assignment help?  or Deep Learning  Homework Help with neat and clean coding with sufficient comments.

Codersarts is a top-rated website for students which is looking for online Deep Learning Assignment Help, Deep Learning  Homework help, Deep Learning  Coursework Help to students at all levels whether it is school, college and university level Coursework Help or Real-time Deep Learning project. Hire us and Get your projects done by  Deep Learning  expert developer or learn from Deep Learning expert with team training & coaching experiences.

 

Our Deep Learning expert will provide help in any type of programming Help, tutoring, Deep Learning  project development.

What is Deep Learning  and why it is used?

Deep Learning is a subfield of machine learning methods based on artificial neural networks with representation learning.  Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.​

Deep Learning Tools Used For Assignment Help

Theano  - Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones.

 

Pylearn2  - Pylearn2 is a library designed to make machine learning research easy. It is built on top of Theano. Hence there are many functions similar between them. Pylearn2 is also capable of running on the CPU and GPU as well.

 

Tensorflow - 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.

 

Keras - It is an open-source software library that provides a Python interface for artificial neural networks. It acts as an interface for the TensorFlow library.

 

Caffe - Caffe is a deep learning framework made with expression, speed, and modularity in mind.

 

Torch - Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple.

 

OverFeat - OverFeat is an image recognizer and feature extractor built around a convolutional network.

 

Cuda - CUDA is a parallel computing platform and application programming interface model created by Nvidia.

 

CNNs / convnet - Convolutional network is a specific artificial neural network topology that is inspired by biological visual cortex and tailored for computer vision tasks by Yann LeCun in early 1990s.

 

Deeplearning4j - Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine. It is a framework with wide support for deep learning algorithms. 

 

OpenCL - OpenCL is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units, graphics processing units, digital signal processors, field-programmable gate arrays and other processors or hardware accelerators.

 

DeepCL - python wrapper for DeepCL deep convolutional neural network library for OpenCL

 

Pytorch - PyTorch is an open source machine learning library for Python and is completely based on Torch. It is primarily used for applications such as natural language processing.

Deep Learning Application

  • Self Driving Cars

  • News Aggregation and Fraud News Detection

  • Natural Language Processing

  • Virtual Assistants

  • Entertainment

  • Visual Recognition

  • Fraud Detection

  • Healthcare

  • Personalisations

  • Detecting Developmental Delay in Children

  • Colourisation of Black and White images

  • Adding sounds to silent movies

  • Automatic Machine Translation

  • Automatic Handwriting Generation

  • Automatic Game Playing

  • Language Translations

  • Pixel Restoration

  • Photo Descriptions

  • Demographic and Election Predictions

  • Deep Dreaming

Want more coding Help, Assignment Help, or any Deep Learning  relate to the above topics.

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Deep Learning Assignment Help

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Deep Learning project Help

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Deep Learning Sample Assignments

Classification of Breast cancer | Sample Assignment

Requirement of the project:

  • Built and implement a classifier of Breast cancer Wisconsin based on Multi-Layer Perceptron.

  • Study the performance of the classifier in terms of accuracy with respect to the different parameters of the MLP (number of Layers, activation function in hidden layers, learning rate, batch length, iterations number, etc

 

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TensorFlow, PyTorch Assignment Help

n this project, you will design and implement your own deep learning model to perform 10-class image classification on the given dataset. You will be able to access the training data to train and tune your model, and a public testing dataset for the evaluation

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Solve Machine Learning Mathematical Problems | Sample Assignment

a. Solve for each αi showing ALL of your work! (2 pts.)

b. Using your results from part a., define the discriminating 2D hyperplane for this dataset; that is, give an equation for the 2D hyperplane. Show your work! (2 pts.)

c. Using the support vector machine you have defined, predict the value for the decision attribute (z) for an instance that has x = 2 and y = 5. Show your work! (2 pts.)

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Blowfish and ECC algorithms to secure data | Sample Assignment

  • Load the Heart Disease Dataset from UCI Repository

  • Encrypt the Dataset using Blowfish and ECC

  • Save the encrypted dataset as csv file.

  • Decrypt the Encrypted dataset using Blowfish and ECC

  • Save the decrypted dataset as csv file.

 

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