MIT 6.7960 Deep Learning, Fall 2024
Instructor: Sara Beery
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This video provides a course overview and introduces deep neural networks, covering their fundamental concepts and basic building blocks. It sets the stage for understanding how these models work and what components they are built from.

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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

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Extended motivational speech to study deep learning mathematically.

I gave this talk at an NSF Town Hall where the goal was to discuss successes of deep learning especially in light of more traditional fields (other talks can be found here: https://www.nsf.gov/events/event_summ.jsp?cntn_id=304013&org=CISE).

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🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐫𝐨𝐠𝐫𝐚𝐦: https://www.edureka.co/masters....-program/machine-lea (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs.
00:00 Introduction
00:18 Agenda
02:35 Introduction to NLP
04:08 Ways to process Text Data
05:55 Recurrent Nuetral Networks
16:06 Long Short-term Memory
17:17 LSTM Use Cases
18:15 Name Entity Recognition
20:55 Sentiment Analysis
22:50 Machine Translation
24:19 Hands-on
📝Feel free to comment your doubts in the comment section below, and we will be happy to answer📝

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⏩𝐓𝐨𝐩 𝟏𝟎 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟮: https://bit.ly/3yhEs7S
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About the Machine Learning with Python Training
Edureka’s Machine Learning Certification Training using Python is designed to make you grab the concepts of Machine Learning. This Machine Learning Certification will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language. Furthermore, in this Machine Learning with Python Training, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of this Machine Learning online course, we will be discussing various practical use cases of Machine Learning with Python programming language to enhance your learning experience. Edureka offers the best Machine Learning Training online for those who want to be the best in Python. Enroll now with Edureka's online Machine Learning Certification to get trained with industrial experts.

What are the pre-requisites for this Machine Learning Certification Training ?
The pre-requisites for the Machine Learning Training include development experience with Python. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous.

Github repo: http://www.github.com/luisguis....errano/singular_valu

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In this video, we learn a very useful matrix trick called singular value decomposition (SVD), in which we express a matrix as a product of two rotation matrices and one scaling matrix.
We also show a very interesting application to image compression.

Similar videos:
Principal component analysis (PCA): https://www.youtube.com/watch?v=g-Hb26agBFg
Matrix factorization and Netflix recommendations: https://www.youtube.com/watch?v=ZspR5PZemcs

Introduction: (0:00)
Transformations: (0:50)
A puzzle: (1:27)
A harder puzzle: (2:21)
Linear transformations: (3:50)
Dimensionality reduction: (10:50)
Image compression: (23:57)