Deep Learning: Advanced Natural Language Processing and RNNs
Год выпуска: 4/2025
Производитель: Udemy
Сайт производителя:
https://www.udemy.com/course/deep-learning-advanced-nlp/
Автор: Lazy Programmer Inc.
Продолжительность: 8h 21m 17s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Английский
Описание:
What you'll learn
- Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems)
- Build a neural machine translation system (can also be used for chatbots and question answering)
- Build a sequence-to-sequence (seq2seq) model
- Build an attention model
- Build a memory network (for question answering based on stories)
- Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
Requirements
- Understand what deep learning is for and how it is used
- Decent Python coding skills, especially tools for data science (Numpy, Matplotlib)
- Preferable to have experience with RNNs, LSTMs, and GRUs
- Preferable to have experience with Keras
- Preferable to understand word embeddings
Description
Ever wondered how AI technologies like
OpenAI ChatGPT,
GPT-4,
DALL-E,
Midjourney, and
Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
It’s hard to believe it's been been over a year since I released my first course on
Deep Learning with
NLP (natural language processing).
A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (
recurrent neural networks), CNNs (
convolutional neural networks), and
word embedding algorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, it’s time to build systems using these components.
At the end of this course, you'll be able to build applications for problems like:
- text classification (examples are sentiment analysis and spam detection)
- neural machine translation
- question answering
We'll take a brief look
chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as:
- bidirectional RNNs
- seq2seq (sequence-to-sequence)
- attention
- memory networks
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as
Keras,
Numpy,
Tensorflow, and
Matpotlib to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class!
"If you can't implement it, you don't understand it"
- Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
- My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
- Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
- After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
- Decent Python coding skills
- Understand RNNs, CNNs, and word embeddings
- Know how to build, train, and evaluate a neural network in Keras
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
UNIQUE FEATURES
- Every line of code explained in detail - email me any time if you disagree
- No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
- Not afraid of university-level math - get important details about algorithms that other courses leave out
Who this course is for:
- Students in machine learning, deep learning, artificial intelligence, and data science
- Professionals in machine learning, deep learning, artificial intelligence, and data science
- Anyone interested in state-of-the-art natural language processing
Формат видео: MP4
Видео: avc, 1920x1080, 16:10, 30.000 к/с, 254 кб/с
Аудио: aac lc sbr, 44.1 кгц, 62.8 кб/с, 2 аудио
Изменения/Changes
The 2025/4 version has increased the number of lessons by 5 compared to 2018/11.
MediaInfo
General
Complete name : D:\1\Udemy - Deep Learning Advanced Natural Language Processing and RNNs (4.2025)\06. Memory Networks\6. Memory Networks Section Summary.mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom (isom/iso2/avc1/mp41)
File size : 8.89 MiB
Duration : 3 min 50 s
Overall bit rate : 323 kb/s
Frame rate : 30.000 FPS
Recorded date : 2025-04-30 23:04:25.2097752+03:30
Writing application : Lavf61.9.100
Video
ID : 1
Format : AVC
Format/Info : Advanced Video Codec
Format profile : Main@L4
Format settings : CABAC / 4 Ref Frames
Format settings, CABAC : Yes
Format settings, Reference frames : 4 frames
Format settings, GOP : M=4, N=60
Codec ID : avc1
Codec ID/Info : Advanced Video Coding
Duration : 3 min 50 s
Bit rate : 254 kb/s
Nominal bit rate : 800 kb/s
Width : 1 920 pixels
Height : 1 080 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) : 0.004
Stream size : 6.98 MiB (79%)
Writing library : x264 core 148
Encoding settings : cabac=1 / ref=3 / deblock=1:0:0 / analyse=0x1:0x111 / me=umh / subme=6 / 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=24 / lookahead_threads=4 / 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=60 / keyint_min=6 / scenecut=0 / intra_refresh=0 / rc_lookahead=60 / rc=cbr / mbtree=1 / bitrate=800 / ratetol=1.0 / qcomp=0.60 / qpmin=0 / qpmax=69 / qpstep=4 / vbv_maxrate=800 / vbv_bufsize=1600 / nal_hrd=none / filler=0 / ip_ratio=1.40 / aq=1:1.00
Codec configuration box : avcC
Audio
ID : 2
Format : AAC LC SBR
Format/Info : Advanced Audio Codec Low Complexity with Spectral Band Replication
Commercial name : HE-AAC
Format settings : Explicit
Codec ID : mp4a-40-2
Duration : 3 min 50 s
Bit rate mode : Constant
Bit rate : 62.8 kb/s
Channel(s) : 2 channels
Channel layout : L R
Sampling rate : 44.1 kHz
Frame rate : 21.533 FPS (2048 SPF)
Compression mode : Lossy
Stream size : 1.73 MiB (19%)
Title : default
Default : Yes
Alternate group : 1
