Neural Networks with TensorFlow and PyTorch

Neural Networks with TensorFlow and PyTorch

Unleash the power of TensorFlow and PyTorch to build and train Neural Networks effectively

上次更新 3/2019

英语 [自动]

https://www.udemy.com/course/neural-networks-with-tensorflow-and-pytorch/

你将会学到的

Get hands-on and understand Neural Networks with TensorFlow and PyTorch

Understand how and when to apply autoencoders

Develop an autonomous agent in an Atari environment with OpenAI Gym

Apply NLP and sentiment analysis to your data

Develop a multilayer perceptron neural network to predict fraud and hospital patient readmission

Build convolutional neural network classifier to automatically identify a photograph

Learn how to build a recurrent neural network to forecast time series and stock market data

Know how to build Long Short Term Memory Model (LSTM) model to classify movie reviews as positive or negative using Natural Language Processing (NLP)

Get familiar with PyTorch fundamentals and code a deep neural network

Perform image captioning and grammar parsing using Natural Language Processing

要求

Basic knowledge of Python is required. Familiarity with TensorFlow and PyTorch will be beneficial.

说明

TensorFlow is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like TensorFlow, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze. If you’re someone who wants to get hands-on with Deep Learning by building and training Neural Networks, then go for this course.



This course takes a step-by-step approach where every topic is explicated with the help of a real-world examples. You will begin with learning some of the Deep Learning algorithms with TensorFlow such as Convolutional Neural Networks and Deep Reinforcement Learning algorithms such as Deep Q Networks and Asynchronous Advantage Actor-Critic. You will then explore Deep Reinforcement Learning algorithms in-depth with real-world datasets to get a hands-on understanding of neural network programming and Autoencoder applications. You will also predict business decisions with NLP wherein you will learn how to program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). Next, you will explore the imperative side of PyTorch for dynamic neural network programming. Finally, you will build two mini-projects, first focusing on applying dynamic neural networks to image recognition and second NLP-oriented problems (grammar parsing).



By the end of this course, you will have a complete understanding of the essential ML libraries TensorFlow and PyTorch for developing and training neural networks of varying complexities, without any hassle.



Meet Your Expert(s):



We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:



Roland Meertens is currently developing computer vision algorithms for self-driving cars. Previously he has worked as a research engineer at a translation department. Examples of things he has made are a Neural Machine Translation implementation, a post-editor, and a tool that estimates the quality of a translated sentence. Last year, he worked at the Micro Aerial Vehicle Laboratory at the university of Delft, on indoor localization (SLAM) and obstacle avoidance behaviors for a drone that delivers food inside a restaurant. Another thing he worked on was detecting and following people using onboard computer vision algorithms on a stereo camera. For his Master's thesis, he did an internship at a company called SpirOps, where he worked on the development of a dialogue manager for project Romeo. In his Artificial Intelligence study, he specialized in cognitive artificial intelligence and brain-computer interfacing.



Harveen Singh Chadha is an experienced researcher in Deep Learning and is currently working as a Self Driving Car Engineer. He is currently focused on creating an ADAS (Advanced Driver Assistance Systems) platform. His passion is to help people who currently want to enter into the Data Science Universe.



Anastasia Yanina is a Senior Data Scientist with around 5 years of experience. She is an expert in Deep Learning and Natural Language processing and constantly develops her skills as far as possible. She is passionate about human-to-machine interactions. She believes that bridging the gap may become possible with deep neural network architectures.



此课程面向哪些人：

This course is for machine learning developers, engineers, and data science professionals who want to work with neural networks and deep learning using powerful Python libraries, TensorFlow and PyTorch.