What is Transfer Learning? It is a way to transfer knowledge and learning from one neural network to nets in other domains. indico a group of Deep Learning innovators from Boston, MA has used Transfer Learning to build a demo on fashion-matching. This video is in two parts - Part 1 showcases Transfer Learning.

Online clothing stores typically recommend products by simply analyzing the past purchasing behaviors of their customers. Deep learning is currently revolutionizing this paradigm by using algorithms to determine which fashion items are likely to match from a human’s point of view. The demo as you'll see in Part 2 of this video, is built using their Custom Collections API. The API works on the concept of Transfer Learning which allows deep nets trained on one task, to be tweaked to work for other tasks using about 100x data than starting from scratch.

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Indico Links
Post on Transfer Learning - https://indico.io/blog/deep-le....arning-fashion-trans
Tutorial - Site - https://indico.io/blog/fashion-matching-tutorial/
Fashion Matching Demo - https://indico.io/demos/clothing-matching
Blog (the blog has great content) - https://indico.io/blog/
Site - https://indico.io/

Credits
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Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

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.

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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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Out of all the current Deep Learning applications, machine vision remains one of the most popular. Since Convolutional Neural Nets (CNN) are one of the best available tools for machine vision, these nets have helped Deep Learning become one of the hottest topics in AI.

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CNNs are deep nets that are used for image, object, and even speech recognition. Pioneered by Yann Lecun at New York University, these nets are currently utilized in the tech industry, such as with Facebook for facial recognition. If you start reading about CNNs you will quickly discover the ImageNet challenge, a project that was started to showcase the state of the art and to help researchers access high-quality image data. Every top Deep Learning team in the world joins the competition, but each time it’s a CNN that ends up taking first place.

A CNN tends to be a difficult concept to grasp. If you’ve ever struggled while trying to learn about these nets, please comment and share your experiences.

CNNs have multiple types of layers, the first of which is the convolutional layer. To visualize this layer, imagine a set of evenly spaced flashlights all shining directly at a wall. Every flashlight is looking for the exact same pattern through a process called convolution. A flashlight’s area of search is fixed in place, and it is bounded by the individual circle of light cast on the wall. The entire set of flashlights forms one filter, which is able to output location data of the given pattern. A CNN typically uses multiple filters in parallel, each scanning for a different pattern in the image. Thus the entire convolutional layer is a 3-dimensional grid of these flashlights.

Connecting some dots
- A series of filters forms layer one, called the convolutional layer. The weights and biases in this layer determine the effectiveness of the filtering process.

- Each flashlight represents a single neuron. Typically, neurons in a layer activate or fire. On the other hand, in the convolutional layer, neurons search for patterns through convolution. Neurons from different filters search for different patterns, and thus they will process the input differently.

- Unlike the nets we've seen thus far where every neuron in a layer is connected to every neuron in the adjacent layers, a CNN has the flashlight effect. A convolutional neuron will only connect to the input neurons that it “shines” upon.

The convoluted input is then sent to the next layer for activation. CNNs use backprop for training, but because a special engine called RELU is used for activation, the nets don’t suffer from the vanishing gradient problem.

In real world applications, image convolution results in 100s of millions of weights and biases, which has an adverse effect on performance. Thus after RELU, the activations are typically pooled in an adjacent layer to reduce dimensionality. Afterwards, there is usually a fully connected layer that acts as a classifier.

CNNs that are in use typically have an architecture with repeated sets of layers. Set 1 is a convolutional layer followed by a RELU. This set can be repeated a few times, and the repeated structure is followed by a pooling layer. This resulting combination forms set 2, which is also repeated a few more times. The final resulting structure is then attached to a fully connected layer at the end. This architecture allows the net to continuously build complex patterns from simple ones, all while lowering computing costs with dimensionality reduction.

CNNs are a powerful tool, but there is one drawback – they require 10s of millions of labelled data points for training. They also must be trained with GPUs for the process to be completed in a reasonable amount of time.

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Yuvaraju Telugu Full Movie HD ft Mahesh Babu, Simran and Sakshi Shivanand. Music composed by Ramana Gogula. Subscribe to Telugu Cinema for more Super Hit Telugu Full Movies : http://bit.ly/1XkIOBD

Yuvaraju movie directed by YVS Chowdary, produced by Burugapally Siva Rama Krishna. The movie also stars Brahmanandam, Ali, MS Narayana, Tanikella Bharani, Chandra Mohan and Sivaji among others.

Synopsis:

Srinivas (Mahesh Babu) just returns to India and joins a college in Hyderabad. There he meets his classmate Srivalli (Saakshi Sivanand), a young beautiful girl with whom he falls in love. Sakshi Sivanand has a childhood friend, Vamsi (Venkat) in abroad whom she regards as her best friend and keeps him close to her heart as when a plane crashed 20 years back these two are the only survivors of the tragedy.

Finally Mahesh and Sakshi come to know that they are in love with each other. They break the news followed by a lavish engagement ceremony. Srilatha (Simran) who arrives in nick of time to attend the engagement. Sakshi Sivanand comes to know that these guys know each other in abroad from the brief observation at her engagement ceremony.

The rest of the story is about who is Simran and what happens to Mahesh Babu's love.

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A comedy of errors takes place when Sanjay and Anjali send their friends Raj and Anjali respectively to spy on each other. Raj and Anjali fall in love with each other but back off assuming the other to be married to their friend. When Raj realises that his love interest Anjali is not his friend's wife he tries to meet her and tell her his feelings, but is shocked to find Anjali engaged to someone else. Will Raj be able to stop Anjali's marriage and tell her the truth?

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Transformers Rise Of The Dark Spark Full Movie Cinematic (2022) 4K ULTRA HD Action

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Direction By Yatrik
Story By Nihar Ranjan Gupta
Art Direction By Subodh Lal Das
Editing By Amiya Mukhopadhyay
Sound Designing By Satyen Chatterjee
Dialogues & Screenplay By Umanath Bhattacharya

Music By Nachiketa Ghosh
Lyrics By Pulak Bandopadhyay & Shibdas Bandopadhyay
Playback Singing By Arati Mukherjee, Banashree Sengupta and Dinen Chowdhury

Leading Starcast By Uttam Kumar (Double Role), Madhabi Chakraborty, Supriya Chowdhury, Dilip Mukherjee, Kamal Mitra, Asit Baran, Jahar Roy and others.

Synopsis:
An Award Winning film by Yatrik, Chinnapatra has Uttam Kumar in a double role. One is the unruly, rich and spolit brat who's killed on the roadside by his enemies while the other, a good guy, reaches the dead man's house and is compelled to take his place. Interesting twists and turns take place as he tries to fit into the dead man's shoes.

* Yatrik was the screen-name of the trio of directors Tarun Majumdar, Sachin Mukherjee, and Dilip Mukherjee.

* Winner of Bengal Film Journalist Association (BFJA) Award For Best Supporting Actress - Supriya Chowdhury

Released In Theatres In The Year 1972

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Hungama (HD) | Hindi Movies 2016 Full Movie | Akshaye Khanna Movies | Bollywood Comedy Movies

Movie : Hungama
Director : Priyadarshan
Producer : Ganesh Jain
Writer : Priyadarshan
Starcast : Akshaye Khanna, Aftab Shivdasani, Rimi Sen, Paresh Rawal, Rajpal Yadav, Shoma Anand, Shakti Kapoor & Others

Synopsis:

The story of a bunch of misfits whose misconception about each others backgrounds end up in a series of chaotic, yet comic outcomes. Aftab Shivdasani (Nandu) and Rimi Sen (Anjali) play two strangers who have to pretend that are a married couple in order to get a place to live. Paresh Rawal (Radheysham Tiwari) plays a rich, yet ground to earth businessman whose business is named after his wife (Shoma Anand) Anjali. Akshaye Khanna (Jeetu) plays a young man starting a new business in electronic ware. Things get complicated when Rimi Sen goes to Paresh Rawal's house in search of a job & meets Akshaye Khanna who falls in love with her thinking she is Paresh Rawal's daughter. Paresh Rawal's wife thinks that he is having an affair with Rimi Sen & while Paresh Rawal thinks his wife is having an affair with Akshaye Khanna due to Rimi Sen and her having the same name. Then comes in Shakti Kapoor (Tejabhai alias Kachara Seth) whose daughter falls in love with a guy pretending to be Paresh Rawal's son and soon everything gets out of control.


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Aadama Jaichomada (transl. We won without playing) is a 2014 Tamil language black comedy film directed by Badri. Produced by B&C films and Upshot Films, co-produced by Skilite Creations, the film stars Karunakaran, Bobby Simha and Vijayalakshmi in prominent roles, while Balaji Venugopal, Aadukalam Naren, K. S. Ravikumar amongst others appear in supporting roles. The dialogues were written by actor Shiva.[1] The film deals with corruption involved in cricket.[2] Aadama Jaichomada was released on 19 September 2014.

Cast
Karunakaran as Panneer
Bobby Simha as Inspector Bhoominathan
Vijayalakshmi as Rama
Balaji Venugopal as Dhayalan
K. S. Ravikumar as Police Commissioner
Aadukalam Naren as Film Producer
Radha Ravi as Producer
Chetan as Constable Mariadas
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Pammanne The Great is a Tulu movie starring Aravind Bolar, Pruthvi Ambar and Shilpa Suvarna in prominent roles. It is a comedy drama genre movie directed by Ismail Moodushadde.
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Director: Ismail Moodushedde
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A local gangster Tedha Singh is recruited for a by RAW, whose main aim is to catch Bob Morley, a dreaded international mafia leader. In the midst of flirting with a bar dance, who turns out to be an ACP and wooing Harika in the neighbourhood, Tedha Singh also has a violent brush with a Minister's hoodlums. Harika is searching for her missing sister Sarika. But it is not hunky dory as Tedha Singh has a secret flashback, which plays out in the backdrop of Portugal. What is his mission, how did he meet Sarika and what happened to her is the rest of the story.

Movie:- Paisa Vasool
Starcast:- Nandamuri Balakrishna, Shriya Saran, Vikramjeet Virk, Musskan Sethi, Kabir Bedi, Kyra Dutt, Ali
Directed by:- Puri Jagannadh
Music by:- Anup Rubens

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Krishna, who is a milk supplier, falls in love with Shamitha, who is already in love with another person
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Producer: Santhosh Shetty Kumble
Production company: Anugraha Films
Director :- Vaibhav Prashanth
Story, Screenplay, Lyrics, Dialogue :- Suresh Anchan
Music Director :- RD Verman , Sandesh Babu
DOP :- K.M.Vishnuvardhan
Starring:-Arvind Bolar, Navin D Padil,Bhoajarajvamanjoor,Sathish Bandale,Umesh Mijar,suresh Anchan, Rashmika, Priya Hegde, Vignesh, Neema Ray,
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Movie Starts At 02:35

Destiny throws aspiring fashion designer Sia (Neha Dhupia) and television producer Raj (Sonu Sood) together during a chance encounter. He boosts Sia's career using his many connections. While Sia's designs get international attention, the pair start an intense romance, but not without their share of complications and misunderstandings. Time will tell if they can withstand what's in store for them.

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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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