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In this video we go through how to code a simple rnn, gru and lstm example. Focus is on the architecture itself rather than the data etc. and we use the simple MNIST dataset for this example.

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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We'll be using the numpy module to convert data to numpy arrays, which is what Scikit-learn wants. We will talk more on preprocessing and cross_validation when we get to them in the code, but preprocessing is the module used to do some cleaning/scaling of data prior to machine learning, and cross_ alidation is used in the testing stages. Finally, we're also importing the LinearRegression algorithm as well as svm from Scikit-learn, which we'll be using as our machine learning algorithms to demonstrate results.

At this point, we've got data that we think is useful. How does the actual machine learning thing work? With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast. Another common example with regression might be to try to predict the dollar value of an insurance policy premium for someone. The company may collect your age, past driving infractions, public criminal record, and your credit score for example. The company will use past customers, taking this data, and feeding in the amount of the "ideal premium" that they think should have been given to that customer, or they will use the one they actually used if they thought it was a profitable amount.

Thus, for training the machine learning classifier, the features are customer attributes, the label is the premium associated with those attributes.

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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.

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This Edureka "Spring Interview Questions and Answers" tutorial video will help you to prepare yourself for Spring Framework Interviews. This tutorial is ideal for freshers as well as experienced also. Learn about the most important Spring Framework interview questions and answers and know what will set you apart in the interview process. This video helps you to learn following topics:
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This video, "Image Classification using CNN," is a project-based video by Simplilearn where you'll get to know how to create machine learning projects in python. In addition, you will also see how different python libraries like NumPy, TensorFlow, seaborn, matplotlib, and many more are used to create amazing projects for your portfolio. Below are the topics we are going to discuss in this machine-learning tutorial.

✅ 00:00 Image Classification using CNN
✅ 01:08 What is Image Classification?
✅ 01:40 What is CNN?
✅ 03:40 Hands-on Lab Demo

What is image classification?

The process of classifying an entire image is known as image classification. Images are anticipated to have just one class per image. Models for image classification take an image as input and produce a prediction of the class to which the image belongs.

We can utilize image classification models when we are not interested in individual instances of items with position information or their shape.

What is CNN?

Machine learning includes convolutional neural networks, also known as convnets or CNNs. It is a subset of the several artificial neural network models that are employed for diverse purposes and data sets. A CNN is a particular type of network design for deep learning algorithms that is utilized for tasks like image recognition and pixel data processing.

Numpy:

NumPy is a Python library used for working with arrays. It also has functions for working in the domain of linear algebra and matrices. It is an open-source project and you can use it freely. NumPy stands for Numerical Python.

Seaborn:

An open-source Python library based on matplotlib is called Seaborn. It is utilized for data exploration and data visualization. With data frames and the Pandas library, Seaborn functions with ease.

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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Cfhyya

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Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/....cs224n/index.html#sc

0:00 Introduction
0:27 Announcements
1:09 Overview
2:47 Natural Language Generation (NLG)
5:00 Recap: training a (conditional) RNN-LM
6:21 Recap: decoding algorithms
6:47 Recap: greedy decoding
7:32 Recap: beam search decoding
8:57 Aside: Do the hosts in Westworld use beam search?
10:07 What's the effect of changing beam size k?
13:16 Effect of beam size in chitchat dialogue
15:58 Sampling-based decoding
18:22 Softmax temperature
21:03 Decoding algorithms: in summary
22:55 Summarization: task definition
27:04 Summarization: two main strategies
28:20 Pre-neural summarization
31:00 Summarization evaluation: ROUGE
35:20 Neural summarization (2015-present)
38:53 Neural summarization: copy mechanisms
42:58 Neural summarization: better content selection
43:47 Bottom-up summarization
45:46 Neural summarization via Reinforcement Learning
49:36 Pre- and post-neural dialogue
50:56 Seq2seq-based dialogue
52:36 Irrelevant response problem
54:19 Genericness / boring response problem
56:38 Repetition problem
59:12 Storytelling
59:35 Generating a story from an image

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#Nvidia #softbank #youtube
Jefferies Managing Director Atul Goyal joins Yahoo Finance Live anchors Julie Hyman and Brad Smith to discuss Softbank's announcement of its generative AI collaboration with Nvidia, Softbank's upcoming IPO, and whether its subsidiary Arm Ltd. could be the next big AI play.
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Train ChatGPT & GPT-4 For PERFECT Midjourney V5 Images! In this video, I'm gonna show you how you can craft amazing and super creative prompts for Midjourney using ChatGPT and GPT-4! Unleash the full potential of Ai art!

What did you think of this trick? Let me know in the comments!
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SOCIAL MEDIA LINKS!
✨ Support my work on Patreon: https://www.patreon.com/aitrepreneur
⚔️ Join the Discord server: https://discord.gg/3ErYSdyUPt
🧠 My Second Channel THE MAKER LAIR: https://bit.ly/themakerlair
📧 Business Contact: [email protected]
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Runpod: https://bit.ly/runpodAi

ChatGPT: https://chat.openai.com/

Midjourney: https://www.midjourney.com/app/

Full prompt: https://pastebin.com/8zeBDedK

Previous GPT-4 Secret Prompts video: https://youtu.be/OHdB5sqZMTs


All ControlNet Videos: https://www.youtube.com/playli....st?list=PLkIRB85csS_

My previous ControlNet video: https://youtu.be/PbDdtPTYm_4

3D POSE & HANDS INSIDE Stable Diffusion! Posex & Depth Map: https://youtu.be/EwWkLMhR23I

Multiple Characters With LATENT COUPLE: https://youtu.be/uR89wZMXiJ8

GET PERFECT HANDS With MULTI-CONTROLNET & 3D BLENDER: https://youtu.be/ptEZQrKgHAg

NEXT-GEN MULTI-CONTROLNET INPAINTING: https://youtu.be/MDHC7E6G1RA

CHARACTER TURNAROUND In Stable Diffusion: https://youtu.be/xMmCZ1EqMVA

EASY POSING FOR CONTROLNET : https://youtu.be/vvrH1Lcx-jo

3D Posing With ControlNet: https://youtu.be/5z71oxf8kh4

My first ControlNet video: https://youtu.be/OxFcIv8Gq8o

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Special thanks to Royal Emperor:
- Totoro

Thank you so much for your support on Patreon! You are truly a glory to behold! Your generosity is immense, and it means the world to me. Thank you for helping me keep the lights on and the content flowing. Thank you very much!

#GPT4 #GPT3 #ChatGPT #midjourney
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WATCH MY MOST POPULAR VIDEOS:
RECOMMENDED WATCHING - My "Stable Diffusion" Playlist:
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RECOMMENDED WATCHING - My "Tutorial" Playlist:
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Disclosure: Bear in mind that some of the links in this post are affiliate links and if you go through them to make a purchase I will earn a commission. Keep in mind that I link these companies and their products because of their quality and not because of the commission I receive from your purchases. The decision is yours, and whether or not you decide to buy something is completely up to you.

In this video I use the new ChatGPT API and Whisper API's to have a conversation. I use my voice as input and ChatGPT speaks back to me using my computer's audio.

0:00 - Demo (What We're Building)
1:10 - High Level Walkthrough / Discussion
5:02 - Gradio User Interface (Microphone Recording)
9:07 - OpenAI Whisper API (Speech to Text)
11:34 - ChatGPT API (Chat Completion)
21:00 - Making OSX Talk
22:06 - Jay-Z Edition (Rapping Therapist)

If you've been enjoying the AI content, I am starting a spinoff channel this year focused on AI in music, gaming, and design at https://youtube.com/@parttimeai
Source Code: https://github.com/hackingthem....arkets/chatgpt-api-w
Twitter: https://twitter.com/parttimelarry
Buy Me a Drank: https://www.buymeacoffee.com/parttimelarry