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Devoxx 2018 - From 0 to Deep Learning in 3 hours
Neural Networks, Deep Learning: there's not a week without a thunderous announcement claiming that Artificial Intelligence limits are once again pushed further.
Doesn't that make you wonder what's actually behind all that? Would you like to know how this all works and how we'll end up having all of our jobs stolen by soulless machines?
Sure, but you've heard that this implies tons of matrix calculus, algebra that doesn't always seem to be that linear, partial derivatives and, come on, if we chose programming rather than pursuing a PhD in Mathematics, we knew what we were doin'!
Okay, give me 3 hours and I will tell you, in very simple words, without any prior knowledge required, step by step baby, how neural networks work in real life, how we get them to learn very useful tricks such as how to differentiate (pun intended) cats from dogs or recognize traffic signs in real time, and how we use these networks in practice.
And we'll see that, thanks to libraries such as TensorFlow or Keras, a few lines of code are enough to do wonders...
Ready?
www.youtube.com/watch?v=gy6cLz4ra8E
( Devoxx 2018
Tous les slides sont proprietes de leurs auteurs.
All slides are properties of their authors. )
This video depicts how the Realtime fingerprint Recognition System using Multiclass SVM in MATLAB work. To know more: bit.ly/2jUgUUI.
Jeff Dean sat for this portrait in Google’s Building 43 on an August afternoon in 2025. The room was quiet, the light falling in a way that made his eyes appear both steady and amused. He has the look of someone who has been through countless problem-solving sessions but still finds joy in the process.
Dean’s story begins in Minnesota, where he studied computer science before heading west for graduate school at the University of Washington. There he focused on compilers, writing work that was highly technical but deeply practical. It was less about abstract beauty and more about making computers run faster, squeezing efficiency out of systems that always seemed too limited. That mindset would shape the rest of his career.
His first real laboratory was Digital Equipment Corporation’s Western Research Lab in Palo Alto. At DEC he was surrounded by engineers who cared about elegance but never at the expense of reliability. Those years gave him a lasting respect for craftsmanship in software. He has spoken of how formative it was to be in a place where even the smallest decisions about performance and structure mattered.
When he arrived at Google in 1999, the company had fewer than a hundred employees. Search was already straining the capacity of existing systems. Dean and his longtime collaborator Sanjay Ghemawat began sketching solutions on whiteboards, filling them with boxes and arrows that hinted at ways to divide work across thousands of machines. Out of those sessions came MapReduce, which allowed Google to process massive data sets in a fraction of the time. Bigtable followed, giving the company a storage system that could keep pace with its ambitions. Engineers still remember the first time they saw queries finish in hours instead of days.
Stories about Dean inside Google often return to the way he approaches code reviews. He is known to mark up a colleague’s code with detailed comments, sometimes line by line, always pushing for clarity. Yet the tone is never dismissive. Younger engineers recall feeling surprised that someone of his stature took their work so seriously. It gave them confidence to tackle harder problems.
By the middle of the 2010s Dean turned his focus to artificial intelligence. As head of Google Brain, he encouraged the team to take bold steps, even when success was uncertain. TensorFlow, the open-source library they released, was built with that same spirit. It made sophisticated machine learning accessible, and within a few years it was being used by researchers in medicine, climate science, and language technology. Dean liked to point out that the best ideas often came from unexpected places once the tools were in wide circulation.
His colleagues describe a leadership style that is more invitation than command. In meetings he often starts with a quiet question that reframes the problem, steering the discussion toward fundamentals. He is patient with complexity, willing to sit through hours of debate if it means arriving at a solution that will endure. His amusement shows itself in small ways, often in a half-smile when a tough question lands on the table.
Outside recognition has come steadily. Election to the National Academy of Engineering confirmed his standing as one of the country’s most influential technologists. Awards have followed, but inside Google what people talk about most is his constancy. He shows up, he listens, and he remains deeply engaged with the work itself.
To photograph him is to see both sides at once. His arms are folded, his expression serious, yet his eyes suggest he is ready to lean forward into the next idea. The systems he has helped build are vast and nearly invisible, woven into daily life for billions of people. But in person he is disarmingly affable, a reminder that behind the abstractions of code and scale is a human being who still loves to puzzle out how things work.
In this video I'm explaining how 3D capsule-networks GAN Tensorflow works with TensorFlow, you can do many amazing things with this implementation. To know more: bit.ly/2kpO8vu.
AIVaid is an artificial intelligence based health diagnosis system. AIVaid is based on Artificial Intelligence using Machine Learning and Deep learning. Lets learn about it. To know more: bit.ly/2IYAh97
Devoxx 2018 - From 0 to Deep Learning in 3 hours
Neural Networks, Deep Learning: there's not a week without a thunderous announcement claiming that Artificial Intelligence limits are once again pushed further.
Doesn't that make you wonder what's actually behind all that? Would you like to know how this all works and how we'll end up having all of our jobs stolen by soulless machines?
Sure, but you've heard that this implies tons of matrix calculus, algebra that doesn't always seem to be that linear, partial derivatives and, come on, if we chose programming rather than pursuing a PhD in Mathematics, we knew what we were doin'!
Okay, give me 3 hours and I will tell you, in very simple words, without any prior knowledge required, step by step baby, how neural networks work in real life, how we get them to learn very useful tricks such as how to differentiate (pun intended) cats from dogs or recognize traffic signs in real time, and how we use these networks in practice.
And we'll see that, thanks to libraries such as TensorFlow or Keras, a few lines of code are enough to do wonders...
Ready?
www.youtube.com/watch?v=gy6cLz4ra8E
( Devoxx 2018
Tous les slides sont proprietes de leurs auteurs.
All slides are properties of their authors. )
Devoxx 2018 - From 0 to Deep Learning in 3 hours
Neural Networks, Deep Learning: there's not a week without a thunderous announcement claiming that Artificial Intelligence limits are once again pushed further.
Doesn't that make you wonder what's actually behind all that? Would you like to know how this all works and how we'll end up having all of our jobs stolen by soulless machines?
Sure, but you've heard that this implies tons of matrix calculus, algebra that doesn't always seem to be that linear, partial derivatives and, come on, if we chose programming rather than pursuing a PhD in Mathematics, we knew what we were doin'!
Okay, give me 3 hours and I will tell you, in very simple words, without any prior knowledge required, step by step baby, how neural networks work in real life, how we get them to learn very useful tricks such as how to differentiate (pun intended) cats from dogs or recognize traffic signs in real time, and how we use these networks in practice.
And we'll see that, thanks to libraries such as TensorFlow or Keras, a few lines of code are enough to do wonders...
Ready?
www.youtube.com/watch?v=gy6cLz4ra8E
( Devoxx 2018
Tous les slides sont proprietes de leurs auteurs.
All slides are properties of their authors. )
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
Devoxx 2018 - From 0 to Deep Learning in 3 hours
Neural Networks, Deep Learning: there's not a week without a thunderous announcement claiming that Artificial Intelligence limits are once again pushed further.
Doesn't that make you wonder what's actually behind all that? Would you like to know how this all works and how we'll end up having all of our jobs stolen by soulless machines?
Sure, but you've heard that this implies tons of matrix calculus, algebra that doesn't always seem to be that linear, partial derivatives and, come on, if we chose programming rather than pursuing a PhD in Mathematics, we knew what we were doin'!
Okay, give me 3 hours and I will tell you, in very simple words, without any prior knowledge required, step by step baby, how neural networks work in real life, how we get them to learn very useful tricks such as how to differentiate (pun intended) cats from dogs or recognize traffic signs in real time, and how we use these networks in practice.
And we'll see that, thanks to libraries such as TensorFlow or Keras, a few lines of code are enough to do wonders...
Ready?
www.youtube.com/watch?v=gy6cLz4ra8E
( Devoxx 2018
Tous les slides sont proprietes de leurs auteurs.
All slides are properties of their authors. )
Learn Deep Learning from top-rated instructors. Join Analytixlabs today. This Deep learning course offers practical and task-oriented training using TensorFlow and Keras on Python platform. Learn more at www.analytixlabs.co.in/ai-deep-learning-training-with-python
Devoxx 2018 - From 0 to Deep Learning in 3 hours
Neural Networks, Deep Learning: there's not a week without a thunderous announcement claiming that Artificial Intelligence limits are once again pushed further.
Doesn't that make you wonder what's actually behind all that? Would you like to know how this all works and how we'll end up having all of our jobs stolen by soulless machines?
Sure, but you've heard that this implies tons of matrix calculus, algebra that doesn't always seem to be that linear, partial derivatives and, come on, if we chose programming rather than pursuing a PhD in Mathematics, we knew what we were doin'!
Okay, give me 3 hours and I will tell you, in very simple words, without any prior knowledge required, step by step baby, how neural networks work in real life, how we get them to learn very useful tricks such as how to differentiate (pun intended) cats from dogs or recognize traffic signs in real time, and how we use these networks in practice.
And we'll see that, thanks to libraries such as TensorFlow or Keras, a few lines of code are enough to do wonders...
Ready?
www.youtube.com/watch?v=gy6cLz4ra8E
( Devoxx 2018
Tous les slides sont proprietes de leurs auteurs.
All slides are properties of their authors. )
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
Devoxx 2018 - From 0 to Deep Learning in 3 hours
Neural Networks, Deep Learning: there's not a week without a thunderous announcement claiming that Artificial Intelligence limits are once again pushed further.
Doesn't that make you wonder what's actually behind all that? Would you like to know how this all works and how we'll end up having all of our jobs stolen by soulless machines?
Sure, but you've heard that this implies tons of matrix calculus, algebra that doesn't always seem to be that linear, partial derivatives and, come on, if we chose programming rather than pursuing a PhD in Mathematics, we knew what we were doin'!
Okay, give me 3 hours and I will tell you, in very simple words, without any prior knowledge required, step by step baby, how neural networks work in real life, how we get them to learn very useful tricks such as how to differentiate (pun intended) cats from dogs or recognize traffic signs in real time, and how we use these networks in practice.
And we'll see that, thanks to libraries such as TensorFlow or Keras, a few lines of code are enough to do wonders...
Ready?
www.youtube.com/watch?v=gy6cLz4ra8E
( Devoxx 2018
Tous les slides sont proprietes de leurs auteurs.
All slides are properties of their authors. )
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
Devoxx 2018 - From 0 to Deep Learning in 3 hours
Neural Networks, Deep Learning: there's not a week without a thunderous announcement claiming that Artificial Intelligence limits are once again pushed further.
Doesn't that make you wonder what's actually behind all that? Would you like to know how this all works and how we'll end up having all of our jobs stolen by soulless machines?
Sure, but you've heard that this implies tons of matrix calculus, algebra that doesn't always seem to be that linear, partial derivatives and, come on, if we chose programming rather than pursuing a PhD in Mathematics, we knew what we were doin'!
Okay, give me 3 hours and I will tell you, in very simple words, without any prior knowledge required, step by step baby, how neural networks work in real life, how we get them to learn very useful tricks such as how to differentiate (pun intended) cats from dogs or recognize traffic signs in real time, and how we use these networks in practice.
And we'll see that, thanks to libraries such as TensorFlow or Keras, a few lines of code are enough to do wonders...
Ready?
www.youtube.com/watch?v=gy6cLz4ra8E
( Devoxx 2018
Tous les slides sont proprietes de leurs auteurs.
All slides are properties of their authors. )
Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.