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+--- Thread: Deep Learning using Tensor Flow (/Thread-deep-learning-using-tensor-flow--26184)
Deep Learning using Tensor Flow - aretr - 07-26-2019
![[Image: 00698805_medium.jpg]](https://pixhost.icu/avaxhome/05/88/00698805_medium.jpg)
Deep Learning using Tensor Flow
.MP4 | Video: 1280x720, 30 fps® | Audio: AAC, 44100 Hz, 2ch | 8.73 GB
Duration: 18 hours | Genre: eLearning | Language: English
Learn how to use Deep Learning Framework - TensorFlow,Keras, Create your own Chatbots,Intro to Tensorflow 2.0.
Become a Deep Learning Expert
Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
Learn how to use Deep Learning Framework - TensorFlow,Keras, Create your own Chatbots,Intro to Tensorflow 2.0.
What you'll learn
Become a Deep Learning Expert
Use TensorFlow for Image Classification with Convolutional Neural Networks
Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
Use TensorFlow for Time Series Analysis with Recurrent Neural Networks Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
Create Generative Adversarial Networks with TensorFlow
Understand the intuition behind Recurrent Neural Networks
Get notes and study material from MIT and manymore
Create your own Chatbots
Introduction To TensorFlow 2.0
Requirements
Basic math (calculus derivatives, matrix arithmetic, probability)
Don't worry about installing TensorFlow, we will do that in the lectures.
Install Numpy and Python
Decent Python coding skills, preferably in data science and the Numpy Stack Description
An Strong enthusiasm of learning
Description
Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!
This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!
This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, including
Neural Network Basics
TensorFlow Basics
Artificial Neural Networks
Densely Connected Networks
Convolutional Neural Networks
Recurrent Neural Networks
AutoEncoders
Reinforcement Learning
OpenAI Gym
and much more!
I hope you're excited to learn about these advanced applications of CNNs, I'll see you in class!
AWESOME FACTS:
One of the major themes of this course is that we're moving away from the CNN itself, to systems involving CNNs.
Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.
Another result? No complicated low-level code such as that written in Tensorflow, Theano, orPyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
What material you will get in this course?
you will get study material .like notes from MIT and other reputed universities.
you will get interveiw question Also
Who this course is for:
Anyone interested in Deep Learning
Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms
Python students eager to learn the latest Deep Learning Techniques with TensorFlow
Students and professionals who want to take their knowledge of computer vision and deep learning to the next level
Self-taught programmers who want to improve their computer science theoretical skills
DOWNLOAD
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