The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.

Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.

The prerequisites for this course are:
1) Basic knowledge of Python.
2) Basic linear algebra and probability.

Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand:
1) Linear regression: mean squared error, analytical solution.
2) Logistic regression: model, cross-entropy loss, class probability estimation.
3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions.
4) The problem of overfitting.
5) Regularization for linear models.

Syllabus

Introduction to optimization
-Welcome to the "Introduction to Deep Learning" course! In the first week you'll learn about linear models and stochatic optimization methods. Linear models are basic building blocks for many deep architectures, and stochastic optimization is used to learn every model that we'll discuss in our course.

Introduction to neural networks
-This module is an introduction to the concept of a deep neural network. You'll begin with the linear model and finish with writing your very first deep network.

Deep Learning for images
-In this week you will learn about building blocks of deep learning for image input. You will learn how to build Convolutional Neural Network (CNN) architectures with these blocks and how to quickly solve a new task using so-called pre-trained models.

Unsupervised representation learning
-This week we're gonna dive into unsupervised parts of deep learning. You'll learn how to generate, morph and search images with deep learning.

Deep learning for sequences
-In this week you will learn how to use deep learning for sequences such as texts, video, audio, etc. You will learn about several Recurrent Neural Network (RNN) architectures and how to apply them for different tasks with sequential input/output.

Final Project
-In this week you will apply all your knowledge about neural networks for images and texts for the final project. You will solve the task of generating descriptions for real world images!

General
Complete name : 018. Deep learning as a language.mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom (isom/iso2/avc1/mp41)
File size : 24.6 MiB
Duration : 6 min 58 s
Overall bit rate : 493 kb/s
Writing application : Lavf55.33.100

Video
ID : 1
Format : AVC
Format/Info : Advanced Video Codec
Format profile : Main@L3.1
Format settings : CABAC / 4 Ref Frames
Format settings, CABAC : Yes
Format settings, RefFrames : 4 frames
Codec ID : avc1
Codec ID/Info : Advanced Video Coding
Duration : 6 min 58 s
Bit rate : 358 kb/s
Width : 1 280 pixels
Height : 720 pixels
Display aspect ratio : 16:9
Frame rate mode : Constant
Frame rate : 25.000 FPS
Color space : YUV
Chroma subsampling : 4:2:0
Bit depth : 8 bits
Scan type : Progressive
Bits/(Pixel*Frame) : 0.016
Stream size : 17.9 MiB (73%)
Writing library : x264 core 142
Encoding settings : cabac=1 / ref=3 / deblock=1:0:0 / analyse=0x1:0x111 / me=hex / subme=7 / psy=1 / psy_rd=1.00:0.00 / mixed_ref=1 / me_range=16 / chroma_me=1 / trellis=1 / 8x8dct=0 / cqm=0 / deadzone=21,11 / fast_pskip=1 / chroma_qp_offset=-2 / threads=12 / lookahead_threads=2 / sliced_threads=0 / nr=0 / decimate=1 / interlaced=0 / bluray_compat=0 / constrained_intra=0 / bframes=3 / b_pyramid=2 / b_adapt=1 / b_bias=0 / direct=1 / weightb=1 / open_gop=0 / weightp=2 / keyint=250 / keyint_min=25 / scenecut=40 / intra_refresh=0 / rc_lookahead=40 / rc=crf / mbtree=1 / crf=24.0 / qcomp=0.60 / qpmin=0 / qpmax=69 / qpstep=4 / ip_ratio=1.40 / aq=1:1.00
Language : English

Audio
ID : 2
Format : AAC
Format/Info : Advanced Audio Codec
Format profile : LC
Codec ID : mp4a-40-2
Duration : 6 min 58 s
Duration_LastFrame : -8 ms
Bit rate mode : Constant
Bit rate : 128 kb/s
Channel(s) : 2 channels
Channel positions : Front: L R
Sampling rate : 44.1 kHz
Frame rate : 43.066 FPS (1024 SPF)
Compression mode : Lossy
Stream size : 6.39 MiB (26%)
Language : English
Default : Yes
Alternate group : 1

Citación :

rapidgator_net:
https://rapidgator.net/file/5d2e1b0a5a69d5c2be7fe252b3002e56/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part1.rar.html
https://rapidgator.net/file/fb1718935b33118466e411feeac1eb40/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part2.rar.html
https://rapidgator.net/file/0d2aa3f3af8ffc82f49e18123aa536cc/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part3.rar.html

nitroflare_com:
https://nitroflare.com/view/20ABC01D9EB35AA/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part1.rar
https://nitroflare.com/view/A7614C426BF908B/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part2.rar
https://nitroflare.com/view/2FD3EE30838739B/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part3.rar

uploadgig_com:
http://uploadgig.com/file/download/b4Ce5FD36C069043/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part1.rar
http://uploadgig.com/file/download/a573658f734fcaAF/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part2.rar
http://uploadgig.com/file/download/982657d6ba02ad76/iq889.Coursera..Introduction.to.Deep.Learning.Higher.School.of.Economics.part3.rar