Raju (Govinda) lives with his sister Laxmi. One day Raju meets Parul (Sonali Bendre) and both fall in love with each other. Parul uncle, Jagpal (Avtar Gill), has already arranged her marriage with Police Inspector Suryadev Singh (Shakti Kapoor). When Suryadev finds out that Parul is refusing to marry him, he arrests Raju on a false charge of murder and tortures him in prison. Parul commits suicide to prevent her marriage with Suryadev. Raju then decides to seek vengeance against Inspector Suryadev Singh.


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You're Watching The Official 4k Video Of Weapon Of Choice by Fatboy Slim

Arguably Fatboy Slim’s most famous music video of all time, Weapon Of Choice features Christopher Walken dancing around a deserted hotel lobby. The Spike Jonze-directed video won multiple awards at the 2001 MTV music video awards and won the 2002 Grammy award for best music video. The track features vocals by the legendary Bootsy Collins.

To mark the track's 20th anniversary, the video has been lovingly - and painstakingly - restored from the original 35mm film to produce this truly stunning 4k version that befits one of the greatest music videos of all-time.


Subscribe now - http://bit.ly/Subscribe2FatboySlim

Click Here To Listen To The Best Of Fatboy Slim : https://fatboy-slim.lnk.to/BestOfyv

Follow Fatboy Slim

https://www.facebook.com/fatboyslim
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https://open.spotify.com/artis....t/4Y7tXHSEejGu1vQ9bw

Lyrics:

Break, eject, eject, eject
Break, eject, eject, eject
Break, eject, eject, eject
Break, eject, eject, eject
'For they catch ya chainsmokin', word
(Break, eject, eject, eject)
Come before they catch ya chainsmokin', word
(Break, eject, eject, eject)
Come before they catch ya chainsmokin', word
(Break, eject, eject, eject)
Come before they catch ya chainsmokin', word
(Break, eject, eject, eject)
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya, 'for they catch ya
(Break, eject)
'For they catch ya, 'for they catch ya
(Break, break, eject)
'For they catch ya, 'for they catch ya
(Break, break, eject)
'For they catch ya, 'for they catch ya
(Break, break, eject, break, eject)
Don't be shocked by the tone of my voice
Check out my new weapon, weapon of choice
Don't be shocked by the tone of my voice
Check out my new weapon, weapon of choice, yeah
Listen to the sound of my voice
You can check it on out, it's the weapon of choice, yeah
Don't be shocked by the tone of my voice
It's the new weapon, the weapon of choice
You can blow with this or you can blow with that
You can blow with this or you can blow with that
Or you can blow with this or you can blow with that
Or you can blow with us
You can blow with this or you can blow with that
You can blow with this or you can blow with that
Or you can blow with
Walk without rhythm, it won't attract the worm
Walk without rhythm and it won't attract the worm
Walk without rhythm and it won't attract the worm
If you walk without rhythm, you never learn, yeah
Don't be shocked by the tone of my voice
Check out my new weapon, weapon of choice
Don't be shocked now by the tone of my voice
Check out my new weapon, weapon of choice
Before they catch ya chainsmokin'
(We can't forget to load it)
Before they catch ya chainsmokin'
Before they catch ya chainsmokin'
(We can't forget to load it)
Before they catch ya chainsmokin'
You can blow with this or you can blow with that
You can blow with this or you can blow with that
Or you can blow with this or you can blow with that
Or you can blow with us
You can blow with this or you can blow with that
You can blow with this or you can blow with that
Or you can blow with
Organically grown through the hemisphere I roam
To make love to the angels of light, yeah
And my girl, I guess you just don't understand
It's gone beyond bein' a man
As I drift off into the night, I'm in flight
She's a boy scout, no doubt
But I'm gonna hold my cool 'cause the music rules, yeah
So groove on, check it out
Halfway between the gutter and the stars, yeah
Halfway between the gutter and the stars, yeah
You can blow with this or you can blow with that
You can blow with this or you can blow with that
Or you can blow with this or you can blow with that
Or you can blow with us
You can blow with this or you can blow with that
You can blow with this or you can blow with that
Or you can blow with
Break, eject, eject, eject
Break, eject, eject, eject
Break, eject, eject, eject
Eject, eject, eject, eject
'For they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya chainsmokin', word
Come before they catch ya
(Break, eject, eject, eject)
Come before they catch ya
(Break, eject, eject, eject)
Break, eject, eject, eject
Break, eject, eject, eject


#FatboySlim #WeaponOfChoice #20thAnniversary

Writing blog posts and emails can be tough at the best of times.

TBH, some days just writing anything can be a struggle

I mean, right now, I'm struggling to write this description 😅

There's got to be a better way right?

Well, there is. Using the amazing AI power of GPT2 and Python you can generate your own blog posts using a technique called Text Generation. This can be extended out to a whole heap of different use cases, it could be used to write emails, poems, code. You name it, you could probably do it.

In this case, we're focused on blog posts though. You'll be able to pass through a simple sentence and have a whole chunk of text output that you can then use on your blog!

In this video, you'll learn how to:
1. Setting up Hugging Face Transformers to use GPT2-Large
2. Loading the GPT2 Model and Tokenizer
3. Encoding text into token format
4. Generating text using the GPT2 Model
5. Decoding output to generate blog posts

Get the code: https://github.com/nicknochnac....k/Generating-Blog-Po

Chapters:
0:00 - Start
3:34 - Installing Hugging Face Transformers with Python
4:03 - Importing GPT2
5:23 - Loading the GPT2-Large Model and Tokenizer
8:39 - Tokenizing Sentences for AI Text Generation
10:57 - Generating Text using GPT2-Large
11:50 - Decoding Generated Text
14:13 - Outputting Results to .txt files
16:11 - Generating Longer Blog Posts

Oh, and don't forget to connect with me!
LinkedIn: https://www.linkedin.com/in/nicholasrenotte
Facebook: https://www.facebook.com/nickrenotte/
GitHub: https://github.com/nicknochnack
Patreon: https://www.patreon.com/nicholasrenotte
Join the Discussion on Discord: https://discord.gg/mtTTwYkB29

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!

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Our previous discussions of deep net applications were limited to static patterns, but how can a net decipher and label patterns that change with time? For example, could a net be used to scan traffic footage and immediately flag a collision? Through the use of a recurrent net, these real-time interactions are now possible.

Deep Learning TV on
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Twitter: https://twitter.com/deeplearningtv

The Recurrent Neural Net (RNN) is the brainchild of Juergen Schmidhuber and Sepp Hochreiter. The three deep nets we’ve seen thus far – MLP, DBN, and CNN – are known as feedforward networks since a signal moves in only one direction across the layers. In contrast, RNNs have a feedback loop where the net’s output is fed back into the net along with the next input. Since RNNs have just one layer of neurons, they are structurally one of the simplest types of nets.

Like other nets, RNNs receive an input and produce an output. Unlike other nets, the inputs and outputs can come in a sequence. Here are some sample applications for different input-output scenarios:
- Single input, sequence of outputs: image captioning
- Sequence of inputs, single output: document classification
- Sequence of inputs, sequence of outputs: video processing by frame, statistical forecasting of demand in Supply Chain Planning

Have you ever used an RNN in one of your projects before? If so, please comment and tell us about your experience.

RNNs are trained using backpropagation through time, which reintroduces the vanishing gradient problem. In fact, the problem is worse with an RNN because each time step is the equivalent of a layer in a feedforward net. Thus if the net is trained for 1000 time steps, the gradient will vanish exponentially as it would in a 1000-layer MLP.

There are different approaches to address this problem, the most popular of which is gating. Gating takes the output of any time step and the next input, and performs a transformation before feeding the result back into the RNN. There are several types of gates, the LSTM being the most popular. Other approaches to address this problem include gradient clipping, steeper gates, and better optimizers.

GPUs are an essential tool for training an RNN. A team at Indico compared the speed boost from using a GPU over a CPU, and found a 250-fold increase. That’s the difference between 1 day and over 8 months!

A recurrent net has one additional capability – it can predict the next item in a sequence, essentially acting as a forecasting engine.

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