Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.

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Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.

Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.

One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.

The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.

Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/

Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.

Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.

Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:

“He turned around a team otherwise known for overall bad temperament”

In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
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

Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well. Enjoy!

Code for this video:
https://github.com/llSourcell/pytorch_geometric

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http://geometricdeeplearning.com/
https://arxiv.org/abs/1611.08097
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Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
ICML 2020 Tutorial

Bayesian inference is especially compelling for deep neural networks. The key distinguishing property of a Bayesian approach is marginalization instead of optimization. Neural networks are typically underspecified by the data, and can represent many different but high performing models corresponding to different settings of parameters, which is exactly when marginalization will make the biggest difference for accuracy and calibration.

The tutorial has four parts:

Part 1: Introduction to Bayesian modelling and overview (Foundations, overview, Bayesian model averaging in deep learning, epistemic uncertainty, examples)

Part 2: The function-space view (Gaussian processes, infinite neural networks, training a neural network is kernel learning, Bayesian non-parametric deep learning)

Part 3: Practical methods for Bayesian deep learning (Loss landscapes, functional diversity in mode connectivity, SWAG, epistemic uncertainty, calibration, subspace inference, K-FAC Laplace, MC Dropout, stochastic MCMC, Bayes by Backprop, deep ensembles)

Part 4: Bayesian model construction and generalization (Deep ensembles, MultiSWAG, tempering, prior-specification, posterior contraction, re-thinking generalization, double descent, width-depth trade-offs, more!)

Slides: https://cims.nyu.edu/~andrewgw/bayesdlicml2020.pdf
Associated Paper: "Bayesian Deep Learning and a Probabilistic Perspective of Generalization" (NeurIPS 2020)
https://arxiv.org/pdf/2002.08791.pdf


Thanks to Kevin Xia (Columbia) for help in preparing the video.

Loss or a cost function is an important concept we need to understand if you want to grasp how a neural network trains itself. We will go over various loss functions in this video such as mean absolute error (a.k.a MAE), mean squared error (a.k.a MSE), log loss or binary cross entropy. After going through theory we will implement these loss functions in python. It is important to go through this implementation as it might be useful during your interviews (if you are targeting a role of a data scientist or a machine learning engineer)

Code: https://github.com/codebasics/....deep-learning-keras-
Exercise: Go at the end of the above notebook to see the exercise

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Why not MSE for logistic regression:
https://towardsdatascience.com..../why-not-mse-as-a-lo

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Prerequisites for this series:   
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2: Pandas tutorials(first 8 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u
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Deep Learning using Matlab - In this lesson, we will learn how to train a deep neural network using Matlab. It is divided into three sections -

1) Challenges of Deep Learning (continuation of previous lecture)

2) Designing the Architecture - https://youtu.be/HSDyiCuViWg?t=371

3) Writing the code in Matlab (Implementation) - https://youtu.be/HSDyiCuViWg?t=678

You can copy the code from here - https://www.nzfaruqui.com/deep....-learning-using-matl

In the first section, the challenges of Deep Learning have been explained briefly. In the second section, the architecture of neural network has been explained. It also covered selection criteria of input nodes and output nodes, weight matrices and training data. In the third section, the implementation has been demonstrated.

In the previous lecture, we have learned details about Neural Network. That is why I didn't recap those things and directly started with the challenges of deep neural network.

Almost every new learners find it difficult to understand few things such as - how to select number of input nodes, number of output nodes, weight matrices, who data are passed into the network, how to calculate error and so on. I have explained these concept using animations and graphics.

Third section, the final section is the implementation part. Here I have implemented the concept I explained in the lecture. First I coded 'DeepLearning' function. The necessary functions - ReLU and Softmax function have been implemented as well. Then I have shown how to train a deep neural network by calling the 'DeepLearning' function. Finally I have tested the deep neural network. All of these has been shown in Matlab code.

I hope this lesson will help you to understand how to train a deep neural network in Matlab. And after completing this lecture, you will be able to train your own deep neural network to accomplish what ever you want.

This is a clip from a conversation with Gary Marcus from Oct 2019. New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days. You can watch the full conversation here: https://www.youtube.com/watch?v=vNOTDn3D_RI
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Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. For now, I post a few clips every Tue & Fri. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel. I hear the 6% and partially agree, so am torn about the whole thing. I tried creating a separate clips channel but the YouTube algorithm makes it very difficult for that channel to grow unless the main channel is already very popular. So for a little while, I'll keep posting clips on the main channel. I ask for your patience and to see these clips as supporting the dissemination of knowledge contained in nuanced discussion. If you enjoy it, consider subscribing, sharing, and commenting.

Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence.

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Checking out a fully custom water-cooled build with 4 A100 40GB cards. An insane powerhouse in a tiny form factor.

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#comino #watercooling #deeplearning

In this video I am going to discuss about the complete road map to prepare for deep learning which will be definitely helpful for preparing for interviews
Complete DL Playlist :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only
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Docker End To End Implementation: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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Machine Learning Pipelines: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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Feature Engineering :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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Kaggle competition :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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MySQL With Python :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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#MachineLearning #DeepLearning

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Grokking Deep Learning is a great introduction to Deep Learning.

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Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are "black boxes", but that's not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.

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0:00 Awesome song and introduction
2:01 A simple dataset and problem
3:37 Description of Neural Networks
7:54 Creating a squiggle from curved lines
15:25 Using the Neural Network to make a prediction
16:38 Some more Neural Network terminology

#StatQuest #NeuralNetworks

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Dato GraphLab is a good software platform for Deep Learning projects that require graph analytics and other important algorithms. It provides two deep nets, sophisticated data munging, an intuitive UI, and built-in enhancements for handling big data.

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Dato GraphLab currently offers a vanilla MLP and a convolutional net. An important feature of the platform is the Graph Analytics toolset, which can be run alongside the deep learning models. Other provided tools include text analytics, a recommender, classification, regression, and clustering. You can also point GraphLab at multiple data sources in order to train data loads.

The platform has an intuitive UI along with an extension called the GraphLab Canvas. This extension offers highly sophisticated visualizations of your models.

Even though GraphLab needs to be deployed and maintained on your own hardware, the platform comes with many performance enhancements that speed up training on big data sets.
GraphLab offers three different types of built-in storage – tabular, columnar, and graph. In addition, the platform provides built-in GPU support which is extremely beneficial for training. You can also set up each type of model as a service that can be accessed programmatically through an API.

Under what circumstances would you use a graph in your deep learning projects? Please comment and share your thoughts.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
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https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
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https://ca.linkedin.com/in/jagannathrajagopal

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In this deep learning interview questions and answers you will learn the latest and top questions asked by companies for deep learning interview. This deep learning interview questions & answers video covers all kinds of questions starting from basic to advanced questions so that you can get benefited.
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📝Following questions are covered in this deep learning video:
00:00 - Deep Learning Interview Questions And Answers
00:59 - What is the Difference between Machine Learning and Deep Learning?
02:16 - What is Perceptron?
03:26 - How is Deep Learning better than Machine Learning?
04:29 - What are some of the most used applications of Deep Learning?
05:27 - What is the meaning of Over fitting?
06:47 - What are Activation functions?
08:00 - Why is Fourier transform used in Deep Learning?
08:55 - What are the steps involved in training a perceptron in Deep learning?
09:47 - What is the use of the loss function?
10:30 - What are some of the Deep Learning Frameworks or tools that you have used?
11:49 - What is the use of the swish function?
12:38 - What are auto encoders?
13:41 - What are the steps to be followed to use the gradient descent algorithm?
14:57 - Differentiate between a single layer perceptron and a multi-layer perceptron
16:00 - What is data normalization in Deep Learning?
16:54 - What is forward propagation?
17:44 - What is back propagation?
18:40 - What are Hyper parameters in Deep Learning?
19:19 - How can hyper parameters be trained in neural networks?
21:38 - What is the meaning of dropout in Deep Learning?
22:42 - What are Tensors?
23:44 - What is the meaning of model capacity in Deep Learning?
24:33 - What is Boltzmann Machine?
25:25 - What are some of the advantages of using TensorFlow?
26:27 - What is the computational graph in Deep Learning?
27:40 - What is a CNN?
28:25 - What are the various layers present in a CNN?
30:19 - What is an RNN in Deep Learning?
31:15 - What is a Vanishing gradient when using RNNs?
32:11 - What is exploding gradient descent in Deep Learning?
33:10 - What is the use of LSTM?
34:04 - Where are autoencoders used?
35:05 - What are the types of auto encoders?
35:35 - What is a restricted Boltzmann Machine?
36:30 - What are some of the limitations of Deep Learning?
38:04 - What are the variants of gradient descent?
39:33 - Why is mini-batch gradient descent so popular?
40:35 - What are deep autoencoders?
41:47 - Why is the leaky ReLu function used in Deep Learning?
42:35 - What are some of the examples if the supervised learning algorithms in Deep Learning?
43:25 - What are some of the examples of unsupervised learning algorithms in Deep Learning?
43:56 - Can we initialize the weights of a network to start from zero?
45:00 - What is the meaning of valid padding and same padding in CNN?
46:16 - What are some of the applications of transfer learning in Deep Learning?
47:25 - How is the transformer architecture better than RNNs in Deep Learning?
48:41 - What are the steps involved in the working of an LSTM network?
50:07 - What are the elements in TensorFlow that are programmable?
50:43 - What is the meaning of bagging and boosting in Deep Learning?
51:52 - What are generative adversarial networks (GANs)?
53:00 - Have you earned any sort of Certification to improve your learning and implementation process?
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