Su	Mo	Tu	We	Th	Fr	Sa
1	2	3	4	5	6	7
8	9	10	11	12	13	14
15	16	17	18	19	20	21
22	23	24	25	26	27	28
29	30	1	2	3	4	5

Coursera - Neural Networks and Deep Learning (Stanford University)

Posted By: ParRus

Date: 26 Apr 2020 21:06:16

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.

In this course, you will learn the foundations of deep learning. When you finish this class, you will:
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network's architecture

This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.

This is the first course of the Deep Learning Specialization.

Syllabus

Introduction to deep learning
-Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.

Neural Networks Basics
-Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

Shallow neural networks
-Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.

Deep Neural Networks
-Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

also You can find my other helpful Coursera-posts
(if old file-links don't show activity, try copy-paste them to the address bar)

MediaInfo:

General
Complete name : 020. Vectorizing Logistic Regression.mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom (isom/iso2/avc1/mp41)
File size : 11.5 MiB
Duration : 7 min 32 s
Overall bit rate : 213 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 : 7 min 32 s
Bit rate : 75.9 kb/s
Width : 1 152 pixels
Height : 720 pixels
Display aspect ratio : 16:10
Frame rate mode : Constant
Frame rate : 30.000 FPS
Color space : YUV
Chroma subsampling : 4:2:0
Bit depth : 8 bits
Scan type : Progressive
Bits/(Pixel*Frame) : 0.003
Stream size : 4.09 MiB (36%)
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

Audio
ID : 2
Format : AAC
Format/Info : Advanced Audio Codec
Format profile : LC
Codec ID : mp4a-40-2
Duration : 7 min 32 s
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 : 6.90 MiB (60%)
Default : Yes
Alternate group : 1

Screenshots