7 YouTube channels to learn machine learning

July 31, 2023 Coin Telegraph

YouTube channels, including Sentdex and Data School, offer in-depth data science and machine learning explorations to enhance data-driven decision-making.

Machine learning is a fascinating and rapidly growing field revolutionizing various industries. If you’re interested in diving into the world of machine learning and developing your skills, YouTube can be an excellent platform to start your learning journey.

Numerous YouTube channels are dedicated to teaching machine learning concepts, algorithms and practical applications. This article will explore seven top YouTube channels that offer high-quality content to help you grasp the fundamentals and advance your machine-learning expertise.

3Blue1Brown

Grant Sanderson’s YouTube channel, 3Blue1Brown, has gained fame for its exceptional ability to elucidate intricate mathematical and machine learning concepts using captivating, intuitive animations.

In math, you can sometimes prove a claim before fully understanding it.

But you cannot fully understand a claim without also being able to prove it.
— Grant Sanderson (@3blue1brown) May 11, 2023

Catering to a wide audience, the channel is widely recognized as a leading resource for mathematics, data science and machine learning topics. Its unique approach to presenting complex subjects has earned it a reputation as one of the finest educational channels in these fields.

Sentdex

Harrison Kinsley’s company, Sentdex, provides a vast library of lessons and guidance on machine learning. The channel focuses on Python programming for machine learning, including subjects like data analysis, deep learning, gaming, finance and natural language processing.

Sentdex is an excellent resource for anyone trying to advance their machine learning knowledge using Python, with clear explanations and useful examples.

Corey Schafer

Although not exclusively devoted to machine learning, Corey Schafer’s YouTube channel includes several great videos on data science and Python programming. His machine learning lessons cover a range of topics, including model training, model evaluation and data pre-processing. Learners can better comprehend the fundamental ideas and practical features of machine learning algorithms thanks to Schafer’s in-depth lectures and coding demonstrations.

Siraj Raval

The YouTube channel of Siraj Raval is well known for making difficult machine learning concepts understandable. His enthusiastic and upbeat teaching style makes learning fun and interesting. The channel offers a variety of content, such as walkthroughs of projects, tutorials and discussions on the most recent artificial intelligence (AI) research.

Raval’s channel is ideal for both beginning and seasoned learners wishing to advance their skills because it heavily emphasizes hands-on projects.

Can a customized GPT make profitable sports bets? I built a NodeJS web-app that uses a combination of ChatGPT and Bing to analyze:
1. Historical sports datasets
2. Twitter Sentiment
3. Live betting odds from the Web

Process, code, & results in the video!https://t.co/tdMjv6eklL
— Siraj Raval (@sirajraval) May 16, 2023

StatQuest with Josh Starmer

StatQuest is an exceptional channel for understanding the statistical concepts behind machine learning algorithms. Hosted by Josh Starmer, former assistant professor at the University of North Carolina at Chapel Hill, the channel uses visual explanations and analogies to simplify complex statistical ideas.

By gaining a solid understanding of statistics, viewers can better grasp the working principles of various machine learning models.

Data School

Kevin Markham’s data science and machine learning tutorials using Python and well-known tools like Scikit-Learn and Pandas are the main focus of Data School. The channel provides extensive playlists that cover machine learning algorithms, data visualization and actual data projects. Learners with little to no prior machine learning experience will benefit from Markham’s well-structured and beginner-friendly teaching style.

DeepLearningAI

Andrew Ng, a renowned AI researcher who established Google Brain, is the founder of DeepLearningAI. The platform has gained immense global popularity through his deep learning specialization on Coursera.

A classic.

Original: Data Science memes on Reddit pic.twitter.com/vdc21pHjLw
— DeepLearning.AI (@DeepLearningAI_) July 24, 2023

The DeepLearningAI channel provides a diverse range of educational content, including video lectures, tutorials, interviews with industry experts, and interactive live Q&A sessions. In addition to being an invaluable learning resource, DeepLearningAI keeps its viewers well-informed about the latest trends in machine learning and deep learning.

What is DALL-E, and how does it work?

June 27, 2023 Coin Telegraph

autoencoder

Discover the process of text-to-image synthesis using DALL-E’s autoencoder architecture and learn how it can transform textual prompts into images.

OpenAI created the ground-breaking generative artificial intelligence (AI) model known as DALL-E, which excels at creating distinctive, incredibly detailed visuals from textual descriptions. DALL-E, in contrast to conventional picture creation models, can produce original images in response to given text prompts, demonstrating its capacity to comprehend and transform verbal concepts into visual representations.

During training, DALL-E makes use of a sizable collection of text-image pairs. It learns to associate visual cues with the semantic meaning of text instructions. DALL-E creates an image from a sample of its learned probability distribution of images in response to a text prompt.

The model creates a visually consistent and contextually relevant image that corresponds with the supplied prompt by fusing the textual input with the latent space representation. As a result, DALL-E is able to produce a wide range of creative pictures from textual descriptions, pushing the limits of generative AI in the area of image synthesis.

How does DALL-E work?

The generative AI model DALL-E can produce incredibly detailed visuals from verbal descriptions. To attain this capability, it incorporates ideas from both language and image processing. Here is a description of how DALL-E works:

Training data

A sizable data set made up of pairs of photos and their related text descriptions is used to train DALL-E. The link between visual information and written representation is taught to the model using these image-text pairs.

Autoencoder architecture

DALL-E is built using an autoencoder architecture, which is made up of two primary parts: an encoder and a decoder. The encoder receives an image and reduces its dimensions to create a representation called latent space. The decoder then uses this representation of latent space to create an image.

Conditioning on text prompts

DALL-E adds a conditioning mechanism to the conventional autoencoder architecture. This indicates that DALL-E subjects its decoder to text-based instructions or explanations while creating images. The text prompts have an impact on the appearance and content of the created image.

Latent space representation

DALL-E learns to map both visual cues and written prompts into a common latent space using the latent space representation technique. The representation of latent space serves as a link between the visual and verbal worlds. DALL-E can create visuals that correspond with the provided textual descriptions by conditioning the decoder on particular text prompts.

Sampling from the latent space

DALL-E selects points from the learned latent space distribution to produce images from text prompts. The decoder’s starting point is these sampled points. DALL-E produces visuals that correlate to the given text prompts by modifying the sampled points and decoding them.

Training and fine-tuning

DALL-E goes through a thorough training procedure utilizing cutting-edge optimization methods. The model is taught to precisely recreate the original images and discover the relationships between visual and textual cues. The model’s performance is improved through fine-tuning, which also makes it possible for it to produce a variety of high-quality images based on various text inputs.

Use cases and applications of DALL-E

DALL-E has a wide range of fascinating use cases and applications thanks to its exceptional capacity to produce unique, finely detailed visuals based on text inputs. Some notable examples include:

Creative design and art: DALL-E can help designers and artists come up with concepts and ideas visually. It can produce appropriate visuals from textual descriptions of desired visual elements or styles, inspiring and facilitating the creative process.
Marketing and advertising: DALL-E can be used to design distinctive visuals for promotional initiatives. Advertisers can provide text descriptions of the desired objects, settings or aesthetics for their brands, and DALL-E can create custom photographs that are consistent with the campaign’s narrative and visual identity.
Interpretability and control: DALL-E has the capacity to produce visual material for a range of media, including books, periodicals, websites and social media. It can convert text into images that go with it, resulting in aesthetically appealing and interesting multimedia experiences.
Product prototyping: By creating visual representations based on verbal descriptions, DALL-E can help in the early stages of product design. The ability of designers and engineers to quickly explore many concepts and variations facilitates the prototyping and iteration processes.
Gaming and virtual worlds: DALL-E’s picture production skills can help with game design and virtual world development. It enables the creation of enormous and immersive virtual environments by producing realistically rendered landscapes, characters, objects and textures.
Visual aids and accessibility: DALL-E can assist with accessibility initiatives by producing visual representations of text content, such as visualizing textual descriptions for people with visual impairments or developing alternate visual presentations for educational resources.
Limited understanding of real-world constraints: DALL-E can help in the creation of illustrations or other visual components for the narrative. Authors can provide textual descriptions of objects or people, and DALL-E can produce related images to bolster the narrative and capture the reader’s imagination.

ChatGPT vs. DALL-E

ChatGPT is a language model designed for conversational tasks, while DALL-E is an image generation model capable of creating unique images from textual descriptions. Here's a comparison table highlighting the differences between ChatGPT and DALL-E:

Limitations of DALL-E

DALL-E has constraints to take into account despite its capabilities in producing graphics from text prompts. The model might reinforce prejudices seen in the training data, possibly perpetuating stereotypes or biases within society. Beyond the supplied prompt, it struggles with subtle nuances and abstract explanations because it lacks contextual awareness.

The complexity of the model can make interpretation and control difficult. DALL-E often creates very distinct visuals, but it could have trouble coming up with other versions or catching all of the potential outcomes. It can take a lot of effort and processing to produce high-quality photographs.

Additionally, the model might provide absurd but visually appealing results that ignore limitations in the real world. To responsibly manage expectations and ensure the intelligent use of DALL-E’s capabilities, it is imperative to be aware of these restrictions. These restrictions are being addressed in ongoing research in order to enhance generative AI.

5 emerging trends in deep learning and artificial intelligence

April 22, 2023 Coin Telegraph

Coin Telegraph

Explore five emerging trends in deep learning and artificial intelligence: federated learning, GANs, XAI, reinforcement learning and transfer learning.

Deep learning and artificial intelligence (AI) are rapidly evolving fields with new technologies emerging constantly. Five of the most promising emerging trends in this area include federated learning, GANs, XAI, reinforcement learning and transfer learning.

These technologies have the potential to revolutionize various applications of machine learning, from image recognition to game playing, and offer exciting new opportunities for researchers and developers alike.

Federated learning

Federated learning is a machine learning approach that allows multiple devices to collaborate on a single model without sharing their data with a central server. This approach is particularly useful in situations where data privacy is a concern.

For example, Google has used federated learning to improve the accuracy of its predictive text keyboard without compromising users’ privacy. Machine learning models are typically developed using centralized data sources, which necessitates user data sharing with a central server. Although users could feel uneasy with their data being collected and stored on a single server, this strategy can generate privacy problems.

Federated learning solves this problem by preventing data from ever being sent to a central server by training models on data that stays on users’ devices. Also, since the training data remained on users’ devices, there was no need to send huge volumes of data to a centralized server, which decreased the system’s computing and storage needs.

Generative adversarial networks (GANs)

Generated adversarial networks are a type of neural network that can be used to generate new, realistic data based on existing data. For example, GANs have been used to generate realistic images of people, animals and even landscapes. GANs work by pitting two neural networks against each other, with one network generating fake data and the other network trying to detect whether the data is real or fake.

Generative Adversarial Networks, or GANs for short, have rapidly emerged as a leading technology for generating realistic synthetic data. GANs are a type of neural network architecture that consists of two networks: a g... https://t.co/moU2Dls8Gk pic.twitter.com/0ZSRkeZe3z
— phill.ai (@phill_ai) April 20, 2023

Explainable AI (XAI)

An approach to AI known as explainable AI aims to increase the transparency and comprehension of machine learning models. XAI is crucial because it can guarantee that AI systems make impartial, fair decisions. Here’s an example of how XAI could be used:

Consider a scenario in which a financial organization uses machine learning algorithms to forecast the likelihood that a loan applicant will default on their loan. In the case of conventional black-box algorithms, the bank would not have knowledge of the algorithm’s decision-making process and might not be able to explain it to the loan applicant.

Using XAI, however, the algorithm could explain its choice, enabling the bank to confirm that it was based on reasonable considerations rather than inaccurate or discriminating information. The algorithm might specify, for instance, that it calculated a risk score based on the applicant’s credit score, income and employment history. This level of transparency and explainability can help increase trust in AI systems, improve accountability and ultimately lead to better decision-making.

Reinforcement learning

A type of machine learning called reinforcement learning includes teaching agents to learn via criticism and incentives. Many applications, including robotics, gaming and even banking, have made use of this strategy. For instance, DeepMind’s AlphaGo used this approach to continually improve its gameplay and eventually defeat top human Go players, demonstrating the effectiveness of reinforcement learning in complex decision-making tasks.

AI can be an agent for our improvement. The most interesting paper I've seen on this is a study of professional GO player performance before vs. after the introduction of Leela, the open-source version of DeepMind''s AlphaGo. Player performance improved. pic.twitter.com/Tk6qxwOftz
— Miles Grimshaw (@milesgrimshaw) January 15, 2023

Transfer learning

A machine learning strategy called transfer learning involves applying previously trained models to address brand-new issues. When there is little data available for a new problem, this method is especially helpful.

For instance, researchers have used transfer learning to adapt image recognition models developed for a particular type of picture (such as faces) to a different sort of image — e.g., animals.

This approach allows for the reuse of the learned features, weights, and biases of the pre-trained model in the new task, which can significantly improve the performance of the model and reduce the amount of data needed for training.

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A brief history of artificial intelligence

April 2, 2023 Coin Telegraph

AI history

AI has evolved from the Turing machine to modern deep learning and natural language processing applications.

Multiple factors have driven the development of artificial intelligence (AI) over the years. The ability to swiftly and effectively collect and analyze enormous amounts of data has been made possible by computing technology advancements, which have been a significant contributing factor.

Another factor is the demand for automated systems that can complete activities that are too risky, challenging or time-consuming for humans. Also, there are now more opportunities for AI to solve real-world issues, thanks to the development of the internet and the accessibility of enormous amounts of digital data.

Moreover, societal and cultural issues have influenced AI. For instance, discussions concerning the ethics and the ramifications of AI have arisen in response to worries about job losses and automation.

Concerns have also been raised about the possibility of AI being employed for evil intent, such as malicious cyberattacks or disinformation campaigns. As a result, many researchers and decision-makers are attempting to ensure that AI is created and applied ethically and responsibly.

After +1000 tech workers urged pause in the training of the most powerful #AI systems, @UNESCO calls on countries to immediately implement its Recommendation on the Ethics of AI - the 1st global framework of this kind & adopted by 193 Member Stateshttps://t.co/BbA00ecihO pic.twitter.com/GowBq0jKbi
— Eliot Minchenberg (@E_Minchenberg) March 30, 2023

AI has come a long way since its inception in the mid-20th century. Here’s a brief history of artificial intelligence.

Mid-20th century

The origins of artificial intelligence may be dated to the middle of the 20th century, when computer scientists started to create algorithms and software that could carry out tasks that ordinarily need human intelligence, like problem-solving, pattern recognition and judgment.

One of the earliest pioneers of AI was Alan Turing, who proposed the concept of a machine that could simulate any human intelligence task, which is now known as the Turing Test.

1956 Dartmouth conference

The 1956 Dartmouth conference gathered academics from various professions to examine the prospect of constructing robots that can “think.” The conference officially introduced the field of artificial intelligence. During this time, rule-based systems and symbolic thinking were the main topics of AI study.

1960s and 1970s

In the 1960s and 1970s, the focus of AI research shifted to developing expert systems designed to mimic the decisions made by human specialists in specific fields. These methods were frequently employed in industries such as engineering, finance and medicine.

1980s

However, when the drawbacks of rule-based systems became evident in the 1980s, AI research began to focus on machine learning , which is a branch of the discipline that employs statistical methods to let computers learn from data. As a result, neural networks were created and modeled after the human brain’s structure and operation.

1990s and 2000s

AI research made substantial strides in the 1990s in robotics, computer vision and natural language processing. In the early 2000s, advances in speech recognition, image recognition and natural language processing were made possible by the advent of deep learning — a branch of machine learning that uses deep neural networks.

The first neural language model, that's Yoshua Bengio, one of the "Godfathers of Deep Learning"! He is widely regarded as one of the most impactful people in natural language processing and unsupervised learning.

Learn something new in https://t.co/8mUYA31M9R… pic.twitter.com/4f2DUE5awF
— Damien Benveniste (@DamiBenveniste) March 27, 2023

Modern-day AI

Virtual assistants, self-driving cars, medical diagnostics and financial analysis are just a few of the modern-day uses for AI. Artificial intelligence is developing quickly, with researchers looking at novel ideas like reinforcement learning, quantum computing and neuromorphic computing.

Another important trend in modern-day AI is the shift toward more human-like interactions, with voice assistants like Siri and Alexa leading the way. Natural language processing has also made significant progress, enabling machines to understand and respond to human speech with increasing accuracy. ChatGPT — a large language model trained by OpenAI, based on the GPT-3.5 architecture — is an example of the “talk of the town” AI that can understand natural language and generate human-like responses to a wide range of queries and prompts.

The future of AI

Looking to the future, AI is likely to play an increasingly important role in solving some of the biggest challenges facing society, such as climate change, healthcare and cybersecurity. However, there are concerns about AI’s ethical and social implications, particularly as the technology becomes more advanced and autonomous.

Ethics in AI should be taught in every school.
— Julien Barbier ❤️‍☠️ 七転び八起き (@julienbarbier42) March 30, 2023

Moreover, as AI continues to evolve, it will likely profoundly impact virtually every aspect of our lives, from how we work and communicate, to how we learn and make decisions.

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