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. In this video, I have explained what is meant by Deep Learning, Artificial Neural Networks and Applications of Deep Learning.

All presentation files for the Machine Learning course as PDF for as low as ₹200 (INR): Drop a mail to siddhardhans2317@gmail.com

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Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision.
I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence.

Hello everyone! I am setting up a donation campaign for my YouTube Channel. If you like my videos and wish to support me financially, you can donate through the following means:

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Let's build a Community of Machine Learning experts! Kindly Subscribe here👉 https://tinyurl.com/md0gjbis

I am making a "Hands-on Machine Learning Course with Python" in YouTube. I'll be posting 3 videos per week. 2 videos on Machine Learning basics (Monday & Wednesday Evening). 1 video on a Machine Learning project (Friday Evening).

Download the Course Curriculum File from here: https://drive.google.com/file/....d/17i0c6SmncNuwSgr9W

LinkedIn: https://www.linkedin.com/in/si....ddhardhan-s-74165220

Telegram Group: https://t.me/siddhardhan

Facebook group: https://www.facebook.com/group....s/490857825649006/?r Instagram: https://www.instagram.com/siddhardhan23

Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations.

✏️ Course created by Tatev Karen Aslanyan.

✏️ Expanded course with 100 questions: https://academy.lunartech.ai/p....roduct/deep-learning

⭐️ Contents ⭐️
⌨️ 0:00:00 Introduction
⌨️ 0:08:20 Question 1: What is Deep Learning?
⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning?
⌨️ 0:15:25 Question 3: What is a Neural Network?
⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning
⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way
⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network?
⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them
⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network?
⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works
⌨️ 0:53:45 Question 10: What is Gradient Descent?
⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning?
⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning?
⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent?
⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN
⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN
⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason?
⌨️ 1:37:50 Question 17: What do you understand by a computational graph?
⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning?
⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry?
⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems?
⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks?
⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima?
⌨️ 2:03:38 Question 23: How is GD different from SGD?
⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term?
⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent.
⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)?
⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model?
⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages?
⌨️ 2:34:12 Question 29: What is RMSProp and how does it work?
⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods
⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs?
⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam?
⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN?
⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN?
⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN?
⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN?
⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN?
⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients?
⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients?
⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights?
⌨️ 3:29:18 Question 41: What is Dropout and how does it work?
⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN?
⌨️ 3:35:06 Question 43: Is Dropout like Random Forest?
⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing?
⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN?
⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN?
⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN?
⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI?
⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality?
⌨️ 3:56:47 Question 50: What are Generative Models, give examples?

PyTorch is a deep learning framework for used to build artificial intelligence software with Python. Learn how to build a basic neural network from scratch with PyTorch 2.

#ai #python #100SecondsOfCode

💬 Chat with Me on Discord

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🔗 Resources

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Tensorflow in 100 Seconds
Python in 100 Seconds https://youtu.be/x7X9w_GIm1s

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- What is PyTorch?
- PyTorch vs Tensorflow
- Build a basic neural network with PyTorch
- PyTorch 2 basics tutorial
- What is a tensor?
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How does AI learn? Is AI conscious & sentient? Can AI break encryption? How does GPT & image generation work? What's a neural network?
#ai #agi #qstar #singularity #gpt #imagegeneration #stablediffusion #humanoid #neuralnetworks #deeplearning

I used this to create neural nets:
https://alexlenail.me/NN-SVG/index.html

More info on neural networks
https://youtu.be/aircAruvnKk?si=Go9XAXR8TqmhX-5m

How stable diffusion works
https://youtu.be/sFztPP9qPRc?si=aF-doepEiaiDrG6z

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MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
*New 2024 Edition*

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Lecture Outline
0:00​ - Introduction
6:10- Why care about generative models?
8:16​ - Latent variable models
10:50​ - Autoencoders
17:02​ - Variational autoencoders
23:25 - Priors on the latent distribution
32:31​ - Reparameterization trick
34:36​ - Latent perturbation and disentanglement
37:40 - Debiasing with VAEs
39:37​ - Generative adversarial networks
42:09​ - Intuitions behind GANs
44:57 - Training GANs
48:28 - GANs: Recent advances
50:57 - CycleGAN of unpaired translation
55:03 - Diffusion Model sneak peak

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2024 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com

Lecture Outline
0:00​ - Introduction
3:42​ - Sequence modeling
5:30​ - Neurons with recurrence
12:20 - Recurrent neural networks
14:08 - RNN intuition
17:14​ - Unfolding RNNs
19:54 - RNNs from scratch
22:41 - Design criteria for sequential modeling
24:24 - Word prediction example
31:50​ - Backpropagation through time
33:40 - Gradient issues
37:15​ - Long short term memory (LSTM)
40:00​ - RNN applications
44:00- Attention fundamentals
46:46 - Intuition of attention
49:13 - Attention and search relationship
51:22 - Learning attention with neural networks
57:45 - Scaling attention and applications
1:00:08 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Shortform link:
https://shortform.com/artem

In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles.

OUTLINE:
00:00 Introduction
01:28 Historical background
02:50 Curve Fitting problem
06:26 Random vs guided adjustments
09:43 Derivatives
14:34 Gradient Descent
16:23 Higher dimensions
21:36 Chain Rule Intuition
27:01 Computational Graph and Autodiff
36:24 Summary
38:16 Shortform
39:20 Outro

USEFUL RESOURCES:
Andrej Karpathy's playlist: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E

Jürgen Schmidhuber's blog on the history of backprop:
https://people.idsia.ch/~juerg....en/who-invented-back


CREDITS:
Icons by https://www.freepik.com/

El Machine Learning y el Deep Learning son clave para la evolución de la Inteligencia Artificial porque permiten que las máquinas puedan aprender y mejorar por sí mismas, logrando que estas máquinas puedan hacer tareas complejas y resolver problemas de manera autónoma. Pero, ¿qué es Machine Learning y Deep Learning exactamente? Te lo explicaremos en este nuevo video de EDteam.

Y si no lo sabías #LoAprendisteEnEDteam

🎉 ¡CURSOS NUEVOS DE LA SEMANA!
1. Curso: Android desde cero https://edy.to/android-yt
2. Curso: Desarrollo de apps con Vue 3 y TypeScript (Pre-venta) https://edy.to/vue-typescript-yt

Si quieres que EDteam de una conferencia gratis en tu institución, contáctanos aquí 👉 https://ed.team/conferencias

💻 Cursos recomendados:
- Carrera: Machine Learning e Inteligencia Artificial https://ed.team/especialidades/machine-learning
- Curso: Introducción al Machine Learning https://edy.to/ML-yt
- Curso: Introducción a las redes neuronales artificiales https://edy.to/redes-neuronales-yt
- Curso: Redes neuronales con TensorFlow https://edy.to/tensorflow-yt


📺 Videos recomendados
- ¿Qué es la Inteligencia Artificial y cómo está cambiando al mundo? https://www.youtube.com/watch?v=98-SzelNMzU
- ¿Qué es el Big Data? https://www.youtube.com/watch?v=M26iIqmqWkI&t=6s
- ¡Los peores errores de programación de la historia! https://www.youtube.com/watch?v=qi9nFs2caj4

⌚ Timeline:
00:00 - Introducción
00:59 - Que es Machine Learning
04:50 - Tipos de MAchine Learning
08:00 - Las Redes Neuronales
11:40 - El Deep Learning


- 🧑‍🎓👩‍🎓 ¿Eres estudiante? Postula a las becas de EDteam: https://edy.to/estudiantes-yt
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- ⭐ Sube a premium y accede a cientos de cursos: https://edy.to/premium-yt
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This deep learning course is designed to take you from beginner to proficient in deep learning. You will learn the fundamental concepts, architectures, and applications of deep learning in a clear and practical way. So get ready to build, train, and deploy models that can tackle real-world problems across various industries.

Course created by @AyushSinghSh

GitHub: https://github.com/ayush714/co....re-deep-learning-cou

⭐️ Contents ⭐️
0:00:00 Intro
0:03:07 Getting started
0:05:07 Vectors
0:21:51 Operation on vectors
0:38:52 Matrices
0:52:02 Operation on Matrices
0:52:27 Matrix Scalar Multiplication
0:55:47 Addition of Matrices
0:59:27 Properties of Matrix addition
1:03:07 Matrix Multiplication
1:08:02 Properties of Matrix Multiplication
1:18:32 Linear Combination Concept
1:36:20 Span
1:50:57 Linear Transformation
2:05:30 Transpose
2:14:02 Properties of Transpose
2:19:52 Dot Product
2:25:22 Geometric Meaning of Dot Product
2:34:32 Types of Matrices
3:04:22 Determinant
3:11:17 Geometric Meaning of Determinant
3:15:42 Calculating Determinant
3:23:37 Properties of Determinant
3:27:22 Rule of Sarus
3:48:42 Minor
3:56:49 Cofactor of a Matrix
4:00:42 Steps to calculate Cofactor of a Matrix
4:03:17 Adjoint of a Matrix
4:18:47 Trace of a Matrix
4:17:22 Properties of Trace
4:38:17 System of Equations
5:03:07 Example
5:17:42 Determinant
5:57:47 Single Variable Calculus
6:02:48 What is Calculus?
6:11:07 Ideas in Calculus
6:11:33 Differentiation
6:18:38 Integration
6:22:07 Precalculus Functions
6:43:52 Single Variable Calculus (Trigonometry Review)
6:45:02 Trigonometry functions
7:12:02 Unit Circle
7:24:32 Limit Concept
7:51:47 Definition of a limit
7:53:27 Continuity
8:00:17 Evaluating Limits
8:17:12 Sandwich Theorem
8:21:12 Differentiation
8:45:42 Differentiation as rate of Change
8:52:37 Differentiation in terms of Limit
9:04:51 Example
9:09:54 Important Differentiation Rules
9:53:12 Rule Chain Rule
10:17:27 What is Deep Learning
10:18:27 What is Machine Learning
10:36:37 Definition of Deep Learning
10:43:07 Applications
10:47:19 Introduction to Neural Networks
10:51:17 Artificial Neural Networks
11:08:31 The Perceptron
11:19:57 Linear Neural Network
11:21:32 Intuition Behind Activation function and Backpropagation Algorithm



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All Machine Learning algorithms intuitively explained in 17 min

In this video I will go through all machine learning algorithms in less than 17 minutes to get you an intuitive understanding of how they work and how they relate to each other as well as help you decide how to pick the right one for your problem. Going all the way from Linear Regression to Neural Networks / Deep Learning and Unsupervised Learning.

Also Watch: How to Learn Machine Learning in 2024 (7 step roadmap) https://youtu.be/jwTaBztqTZ0

Chapters:

00:00 - Intro: What is Machine Learning?
00:59 - Supervised Learning
01:37 - Unsupervised Learning
02:20 - Linear Regression
04:04 - Logistic Regression
04:53 - K Nearest Neighbors (KNN)
06:10 - Support Vector Machine (SVM)
07:51 - Naive Bayes Classifier
08:37 - Decision Trees
09:11 - Ensemble Algorithms
09:24 - Bagging & Random Forests
09:53 - Boosting & Strong Learners
10:26 - Neural Networks / Deep Learning
12:42 - Unsupervised Learning (again)
12:57 - Clustering / K-means
14:35 - Dimensionality Reduction
15:15 - Principal Component Analysis (PCA)

Qual a diferença entre Inteligência Artificial, Machine Learning e Deep Learning?

Neste vídeo trato das diferenças que existem entre as tecnologias de Inteligência Artificial, Aprendizado de Máquina e Aprendizado Profundo, além de abordar as aplicações de cada tecnologia.

Leia também: O que é Machine Learning? http://www.bosontreinamentos.c....om.br/inteligencia-a

Ajude o canal adquirindo meus cursos na Udemy:
Bancos de Dados com MySQL Básico: https://bit.ly/35QdWE4
Lógica de Programação com Português Estruturado: https://bit.ly/3QKPn22
Programação em Python do Zero: https://bit.ly/python-boson

Adquira também livros e outros itens na loja da Bóson Treinamentos na Amazon e ajude o canal a se manter e crescer:
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Seja membro deste canal e ganhe benefícios:
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Contribua com a Bóson Treinamentos!:
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Outros projetos do autor:
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#bosontreinamentos #inteligenciaartificial #machinelearning

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
This Edureka "Convolutional Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow.

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU

🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

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Go from zero to a machine learning engineer in 12 months. This step-by-step roadmap covers the essential skills you must learn to become a machine learning engineer in 2024.

Download the FREE roadmap PDF here: https://mosh.link/machine-learning-roadmap

✋ Stay connected

- Complete courses: https://codewithmosh.com
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🔗 Other roadmaps

https://youtu.be/Tef1e9FiSR0?si=QpVnZ_o9-DAXzT71
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📖 Chapters

00:00 - Introduction
00:20 - Programming Languages
00:42 - Version Control
01:03 - Data Structures & Algorithms
01:35 - SQL
01:55 - The Complete Roadmap PDF
02:19 - Mathematics & Statistics
02:40 - Data Handling
03:15 - Machine Learning Fundamentals
03:57 - Advanced Topics
04:28 - Model Deployment

#machinelearning #ai #datascience #coding #programming

TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models.

#programming #deeplearning #100secondsofcode

🔗 Resources

TensorFlow Docs https://www.tensorflow.org/
Fashion MNIST Tutorial https://www.tensorflow.org/tutorials/keras/classification
Neural Networks Overview for Data Scientists https://www.ibm.com/cloud/learn/neural-networks
Machine Learning in 100 Seconds https://youtu.be/PeMlggyqz0Y

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In this comprehensive exploration of the field of deep learning with Professor Simon Prince who has just authored an entire text book on Deep Learning, we investigate the technical underpinnings that contribute to the field's unexpected success and confront the enduring conundrums that still perplex AI researchers.

Understanding Deep Learning - Prof. SIMON PRINCE [STAFF FAVOURITE]

Watch behind the scenes, get early access and join private Discord by supporting us on Patreon:
https://patreon.com/mlst
https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk

Key points discussed include the surprising efficiency of deep learning models, where high-dimensional loss functions are optimized in ways which defy traditional statistical expectations. Professor Prince provides an exposition on the choice of activation functions, architecture design considerations, and overparameterization. We scrutinize the generalization capabilities of neural networks, addressing the seeming paradox of well-performing overparameterized models. Professor Prince challenges popular misconceptions, shedding light on the manifold hypothesis and the role of data geometry in informing the training process. Professor Prince speaks about how layers within neural networks collaborate, recursively reconfiguring instance representations that contribute to both the stability of learning and the emergence of hierarchical feature representations. In addition to the primary discussion on technical elements and learning dynamics, the conversation briefly diverts to audit the implications of AI advancements with ethical concerns.

Pod version (with no music or sound effects): https://podcasters.spotify.com..../pod/show/machinelea

Follow Prof. Prince:
https://twitter.com/SimonPrinceAI
https://www.linkedin.com/in/si....mon-prince-615bb9165

Get the book now!
https://mitpress.mit.edu/97802....62048644/understandi
https://udlbook.github.io/udlbook/

Panel: Dr. Tim Scarfe -
https://www.linkedin.com/in/ecsquizor/
https://twitter.com/ecsquendor

TOC:
[00:00:00] Introduction
[00:11:03] General Book Discussion
[00:15:30] The Neural Metaphor
[00:17:56] Back to Book Discussion
[00:18:33] Emergence and the Mind
[00:29:10] Computation in Transformers
[00:31:12] Studio Interview with Prof. Simon Prince
[00:31:46] Why Deep Neural Networks Work: Spline Theory
[00:40:29] Overparameterization in Deep Learning
[00:43:42] Inductive Priors and the Manifold Hypothesis
[00:49:31] Universal Function Approximation and Deep Networks
[00:59:25] Training vs Inference: Model Bias
[01:03:43] Model Generalization Challenges
[01:11:47] Purple Segment: Unknown Topic
[01:12:45] Visualizations in Deep Learning
[01:18:03] Deep Learning Theories Overview
[01:24:29] Tricks in Neural Networks
[01:30:37] Critiques of ChatGPT
[01:42:45] Ethical Considerations in AI

References:

#61: Prof. YANN LECUN: Interpolation, Extrapolation and Linearisation (w/ Dr. Randall Balestriero)
https://youtube.com/watch?v=86ib0sfdFtw

Scaling down Deep Learning [Sam Greydanus]
https://arxiv.org/abs/2011.14439

"Broken Code" a book about Facebook's internal engineering and algorithmic governance [Jeff Horwitz]
https://www.penguinrandomhouse.....com/books/712678/br

Literature on neural tangent kernels as a lens into the training dynamics of neural networks.
https://en.wikipedia.org/wiki/....Neural_tangent_kerne

Zhang, C. et al. "Understanding deep learning requires rethinking generalization." ICLR, 2017.
https://arxiv.org/abs/1611.03530

Computer Vision: Models, Learning, and Inference, by Simon J.D. Prince
https://www.amazon.co.uk/Compu....ter-Vision-Models-Le

Deep Learning Book, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
https://www.deeplearningbook.org/

Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
https://arxiv.org/abs/2210.00881

Computer Vision: Algorithms and Applications, 2nd ed. [Szeliski]
https://szeliski.org/Book/

A Spline Theory of Deep Networks [Randall Balestriero]
https://proceedings.mlr.press/....v80/balestriero18b/b

DEEP NEURAL NETWORKS AS GAUSSIAN PROCESSES [Jaehoon Lee]
https://arxiv.org/abs/1711.00165

Do Transformer Modifications Transfer Across Implementations and Applications [Narang]
https://arxiv.org/abs/2102.11972

ConvNets Match Vision Transformers at Scale [Smith]
https://arxiv.org/abs/2310.16764

Dr Travis LaCroix (Wrote Ethics chapter with Simon)
https://travislacroix.github.io/