计算机代写|CS1699 Deep Learning

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CS1699课程简介

Location: Public Health G23
Time: Tuesday and Thursday, 3pm-4:15pm
Instructor: Adriana Kovashka (email: kovashka AT cs DOT pitt DOT edu; use “CS1699” at the beginning of the subject line)
Office: Sennott Square 5325
Office hours: Tuesday 12:20pm-1:30pm and 4:20pm-5:30pm; Thursday 10am-10:50am and 4:20pm-5:30pm
TA: Mingda Zhang (email: mzhang AT cs DOT pitt DOT edu; use “CS1699” at the beginning of the subject line)
TA’s office hours: Tuesday 9am-10am, Wednesday 10am-11am, Thursday 11:30am-12:30pm
TA’s office: Sennott Square 5503

Prerequisites: Math 220 (Calculus I), Math 280 or 1180 (Linear Algebra), CS 1501 (Algorithm Implementation)

Piazza: Sign up for it here. Please use Piazza rather than email so everyone can benefit from the discussion– you can post in such a way that only the instructor sees your name. Please try to answer each others’ questions whenever possible. The best time to ask the instructor or TA questions is during office hours.

Programming language/framework: We will use Python, NumPy/SciPy, and PyTorch.

Prerequisites 

Overview
Course description: This course will cover the basics of modern deep neural networks. The first part of the course will introduce neural network architectures, activation functions, and operations. It will present different loss functions and describe how training is performed via backpropagation. In the second part, the course will describe specific types of neural networks, e.g. convolutional, recurrent, and graph networks, as well as their applications in computer vision and natural language processing. The course will also briefly discuss reinforcement learning and unsupervised learning, in the context of neural networks. In addition to attending lectures and completing bi-weekly homework assignments, students will also carry out and present a project.

CS1699 Deep Learning HELP（EXAM HELP， ONLINE TUTOR）

Deep Learning is a subset of Machine Learning that involves the use of algorithms called Neural Networks to model and solve complex problems. Neural Networks are a set of algorithms modeled after the structure and function of the human brain. They consist of layers of interconnected nodes or neurons that process and transmit information to each other.

Deep Learning involves training these neural networks on large datasets to learn patterns and features in the data, and then using this learning to make predictions or classify new data. The term “deep” in Deep Learning refers to the fact that these networks have multiple layers of neurons, allowing them to learn and represent increasingly complex and abstract features in the data.

Deep Learning and Neural Networks have been successfully applied in a wide range of applications, such as image recognition, natural language processing, speech recognition, and even games like chess and Go. They have proven to be powerful tools for solving complex problems that are difficult or impossible to solve using traditional algorithms.

Deep Learning and Neural Networks work by processing large amounts of data through a series of mathematical operations and transformations, and using these operations to learn patterns and features in the data.

The basic building block of a Neural Network is the neuron, which receives input from other neurons, performs a calculation on that input, and then produces an output that is transmitted to other neurons in the network. A typical neural network consists of multiple layers of neurons, each of which performs a different type of computation on the input data.

During the training process, the network is presented with a set of input data and a corresponding set of output values, and the weights and biases of the neurons are adjusted to minimize the difference between the network’s predicted output and the actual output. This process is repeated many times using different subsets of the data, until the network has learned to accurately predict the output values for new input data.

Deep Learning involves using very deep neural networks, with many layers of neurons, to learn increasingly complex and abstract features in the data. This requires a large amount of computational resources, such as GPUs, to train the network effectively.

Once the network has been trained, it can be used to make predictions or classify new data by processing the input through the network and producing an output value. This output value can be interpreted as a probability of the input belonging to a certain category, or as a continuous value that represents some property of the input.

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