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This week...

⬆️ @GracieAbramsis BACK at the top with That’s So True for a sixth (!) week

⬆️ @LolaYoungggviral breakout hit Messy (3) vaults into Top 5 for the first time, earning a new chart peak

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1 Sugar Shack - - The Fireballs
2 Surfin' U.S.A. - The Beach Boys
3 The End of the World - Skeeter Davis
4 Rhythm of the Rain - The Cascades
5 He's So Fine - The Chiffons
6 Blue Velvet - Bobby Vinton
7 Hey Paula - Paul & Paula
8 Fingertips - Little Stevie Wonder
9 Washington Square - The Village Stompers
10 It's All Right - The Impressions
11 Can't Get Used to Losing You - Andy Williams
12 My Boyfriend's Back - The Angels
13 Sukiyaki - Kyu Sakamoto
14 She's a Fool - Lesley Gore
15 So Much in Love - The Tymes
16 Puff, the Magic Dragon - Peter, Paul and Mary
17 Blowin' in the Wind - Peter, Paul and Mary
18 I'm Leaving It Up to You - Dale and Grace
19 Deep Purple - Nino Tempo & April Stevens
20 Wipe Out - The Surfaris
21 I Love You Because - Al Martino
22 Wild Weekend - The Rebels
23 You're the Reason I'm Living - Bobby Darin
24 Walk Like a Man - The Four Seasons
25 Mockingbird - Inez and Charlie Foxx
26 I Will Follow Him - Little Peggy March
27 Pipeline - The Chantays
28 Surf City - Jan and Dean
29 It's My Party - Lesley Gore
30 Blame It on the Bossa Nova - Eydie Gorme

eep learning networks are mechanical systems which “learn” by modeling high-level abstractions in datasets, and cycling through trial-and-error guesses with feedback, to establish an optimally-weighted system that can make accurate predictions about new data. LSTM RNNs (long short term memory Recurrent Neural Nets) is a deep learning method that overcomes some of the limitations in classical time series forecasting (non-linearity, fixed-time windows, time lag specification, and multivariate forecasting). Hence, deep learning is emerging as an important forecasting technique for coping with exponentially growing data, and the ability to expediently process and direct these data. In the future, deep learning smart networks might provide an advanced computational infrastructure for tackling real-time predictive data science problems such global health monitoring, energy storage and transmission, and financial risk assessment. Slides: https://www.slideshare.net/lab....logga/deep-learning-

In this video we will cover how to build a neural network in TensorFlow 2.0 using the Keras Sequential and Functional API. We also take a look at other details like how we obtain the output features from specific layers and obtaining summaries of the model etc.

Resources or prerequisite videos:
1. 3Blue1Brown:
https://www.youtube.com/playli....st?list=PLZHQObOWTQD

2. Deep Learning Specialization Course 1 (Probably don't need to watch all):
https://www.youtube.com/playli....st?list=PLkDaE6sCZn6

2. Deep Learning Specialization Course 2 (Probably don't need to watch all):
https://www.youtube.com/playli....st?list=PLkDaE6sCZn6

I learned a lot and was inspired to make these TensorFlow videos by the TensorFlow Specialization on Coursera. Below you'll find both affiliate and non-affiliate links, the pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
affiliate: https://bit.ly/3t3tgI5
non-affiliate: https://bit.ly/3kZgN5B

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

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Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
GitHub - https://github.com/aladdinpersson

TensorFlow Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

OUTLINE:
0:00 - Introduction
0:40 - Resources for theory
1:06 - Imports
3:13 - Loading Mnist dataset
5:56 - Using Sequential API
14:10 - Using Functional API
17:20 - Extracting specific layer features
19:53 - Suggestions to try out

Pondering the future of user interfaces with advancements in natural language processing and artificial intelligence

Tweet in reference: https://twitter.com/c_valenzue....lab/status/157444845

Neural Networks from Scratch book: https://nnfs.io
Channel membership: https://www.youtube.com/channe....l/UCfzlCWGWYyIQ0aLC5
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Point cloud is an important type of geometric data structure. They are simple and unified structures that avoid the combinatorial irregularities and complexities of meshes. These properties make point clouds widely used for 3D reconstruction or visual understanding applications, such as AR, autonomous driving, and robotics. This course will teach how we apply deep learning methods to point cloud data. We will cover the following topics in this short course and will end with some open problems.

• Basic neural architectures to process point cloud as input or to generate point cloud as output
• Scene-level understanding of static and dynamic point clouds
• Point cloud based inverse graphics
• Learning to convert point cloud to other 3D representations
• Learning to map point cloud with data in other modalities (images, languages)

After my last video I got a lot of comments (mainly on Reddit) asking me to make a video explaining how I did it.
It took me a while to learn how to video edit, voice act, and animate, so it was about time I presented and explained this project.

The Bibites

Made in C# on Unity

I highly inspired my algorithm from the following document :
Stanley K. O. and Miikkulainen R. (2002). Evolving Neural
Networks through Augmenting Topologies. MIT Press journals

Music: "Perspectives" by Kevin MacLeod
http://incompetech.com/music/royalty-...

Suguna Sundari
Singer - Ram Miriyala & Snigdha Sharma
Lyrics - Ramjogayya sastry
Programmed And Arranged By Thaman S
Musical Bands - Band Thamania & Gongura Band
Live Percussions - Anandan Sivamani
Live Percussions - Krishna Kishore
Folk Rhythm - Vaniyambadi illam Puyal
Sound Indian Tapes - Lakshmi Narayana,Raju,Vedha,Jagan Mohan,Jaya Prakash,Lakshmi Kanth,Pyare Lal
Thalam - Veeda
Nadaswaram - Sivakumar
Trumpet - Babu
Guitar - GodFray
Vocal Supervision -Sri Krishna
Male Chours - Sri Krishna,Dhanunjay,Saicharan
Female Chours - Padmaja Vrusha Rachita Padmapriya Reshma Adviteeya,Pratyusha & Ramya
Song Recorded @Prasad Studio ( Hyd ) ,V Studio ( Chennai ) Uno Records Studios by Osho V , Biju
Mixed & Mastered By Shadab Rayeen At
New Edge Mumbai & New Edge (Ny)
Assisted By - Pukhraj & Anup
Musicians Co Ordinator - Manikandan K
Studio Manager - Seenu & Sridhar
Studio Assistance - Kannan,Lingam,Raju & Ranjith

Movie Details:
Cast : NATASIMHAM Nandamuri Balakrishna, Shruti Haasan, Varalaxmi Sarathkumar, Honey Rose and Duniya Vijay
Story-Screenplay-Direction : Gopichandh Malineni
Producers : Naveen Yerneni and Ravi Shankar Yalamanchili
CEO : Cherry
Executive Producer : Chandu Ravipati
Music : Thaman S
Cinematographer : Rishi Punjabi
Editing : Navin Nooli
Production Designer : A.S. Prakash
Fights : Ram-Laxman, VVenkat
Dialogues : Sai Madhav Burra
Lyrics : Rama Jogaiah Sastry
Choreography : Sekhar VJ, Shankar
Chief-Co Director : Kurra Rangarao
Co-Directors : Gulabi Srinu and Nimmagadda Srikanth
Direction Team: Prakash Dussa, Gajjala Bhaskar Reddy, Ramananda Sagar, Surya Guddati, Jyothi Prakash, Rithica Devireddy, Hemanth C. Yoganand
PRO : Vamsi – Sekhar
Publicity Designer : Working Title Shiva
Publicity : Baba Sai Kumar
Marketing : First Show
Line Producer : Bala Subhramanyam KVV
DI : Annapurna Studios
Colorist : Venu Gopal Rao. J
Associate Editors: N. Durga Prakash, Srinivas Nadiminti
Online Editor: Venkat Jalagam
Making: Krishna Reddy

Music Label - Sony Music Entertainment India Pvt. Ltd.

© 2022 Sony Music Entertainment India Pvt. Ltd.

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