AI Tools Presentation - 2023-04-24 15.22.06

[Audio] By Adam Trending AI Tools in today's world.

[Audio] Hey there! Let's take a look at how AI can help us out. AI is about simulating human intelligence in machines, helping them think and learn like us. It's revolutionizing the way we interact with technology, across many industries including healthcare, finance, and marketing. In this presentation, we'll check out some of the best free AI tools that can give your work a huge boost! Introduction To AI Tools.

[Audio] TensorFlow is an open-source platform that helps people use computers to do cool things, like identify pictures and understand what people say. You can use it to build a program that can tell what a picture is showing! To create a program using TensorFlow, you would need to walk through a series of steps that help teach the computer how to recognize different images. People use TensorFlow to make money by helping others learn how to use the platform and by building programs that solve realworld problems. TensorFlow.

[Audio] PyTorch PyTorch is a popular open-source machine learning framework used for developing and training AI models. It was developed by Facebook's AI research team and has gained a large community of users due to its flexibility and ease of use.PyTorch is widely used for computer vision, natural language processing, and speech recognition applications. It offers a dynamic computational graph and supports both CPU and GPU acceleration, making it ideal for large-scale data processing.Real-time exercise: To demonstrate PyTorch's capabilities, we can assist an 8 year old build something, such as a birdhouse or a model car.a Simpler!neural network that recognizes handwritten digits. First, we can load the MNIST dataset, which consists of thousands of images of handwritten digits. Next, we can define a neural network architecture using PyTorch's high-level API. Finally, we can train the model and test its accuracy on a validation dataset..

[Audio] GPT-3 is an amazing AI model that has the power to create text, like stories and poems, just like a real human. It can even help with making translations, developing chatbots, and generating all kinds of content. It's really cool and can do a lot of exciting things! GPT-3.

[Audio] Keras is an awesome AI tool that makes building and training AI models super easy! You don't need to be an expert to use it – with its simple and intuitive API and built-in tools for data preprocessing and augmentation, anyone can create powerful deep learning models with Keras! Plus, it has support for different backends like TensorFlow, Microsoft Cognitive Toolkit, and Theano. In no time you can create custom loss functions, activation functions, and optimization algorithms too! Keras.

[Audio] Real-time exercise: Let's demonstrate how to use Keras to build a simple image recognition model. We will use the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes.Import the necessary libraries and load the data: arduinoCopy code import keras from keras.datasets import cifar10 (x_train, y_train), (x_test, y_test) = cifar10.load_data() Keras.

[Audio] Keras Preprocess the data by normalizing pixel values to be between 0 and 1: makefileCopy code x_train = x_train / 255.0 x_test = x_test / 255.0 Define the model architecture using Keras' Sequential API: scssCopy code from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten model = Sequential([ Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)), MaxPooling2D((2, 2)), Conv2D(64, (3, 3), activation='relu', padding='same'), MaxPooling2D((2, 2)), Conv2D(128, (3, 3), activation='relu', padding='same'), MaxPooling2D((2, 2)), Flatten(), Dense(10, activation='softmax') ]).

[Audio] Keras Compile the model with a loss function, optimizer, and evaluation metric: pythonCopy code model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) Train the model on the training data: scssCopy code model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test)) Evaluate the model on the test data: scssCopy code test_loss, test_acc = model.evaluate(x_test, y_test) print('Test accuracy:', test_acc) Monetization: Keras is a free and open-source library, but it can be used to build models that can be sold or licensed. Keras can also be used as part of a larger AI solution that is sold or licensed to customers. Additionally, Keras can be used to develop custom AI models for clients, which can be a lucrative business opportunity for AI developers..

[Audio] OpenAI OpenAI is an amazing research lab that helps people use AI (Artificial Intelligence) to do cool things. They have made many discoveries that help us understand how computers can understand language, and make robots and computer games smarter. They have also made GPT-3, which is a really smart computer program that can create new stories and sentences that sound like they were written by a real person. Using OpenAI's GPT-3, you can make really cool stories, or even create virtual assistants that can help customers get what they need. It's really fun to use OpenAI's GPT-3 to create stories, and you can even make money with it by offering content creation services to businesses!.

[Audio] Monetization: There are various ways to monetize the use of OpenAI's GPT-3 model, such as: Offering content creation services to businesses and individuals who need high-quality written content for their websites, blogs, and social media channels.Developing AI-powered chatbots and virtual assistants that can provide personalized assistance and support to customers.Integrating GPT-3 into other AI-powered products and services, such as recommendation engines and predictive analytics tools. OpenAI.

[Audio] Hugging Face is an AI platform that can help you build natural language processing models and share them with others. It has lots of ready-made models and tools that you can use to make your own custom models. To make a sentiment analysis model, you can use Hugging Face by first installing the library with the 'pip install transformers' command. Then, you can choose a pre-trained sentiment analysis model such as BERT, RoBERTa, or DistilBERT. After that, you can fine-tune the model by feeding it your data so it can learn signals from your particular sentiment analysis project. Finally, use the model to make predictions on new text data that you have. Hugging Face.

[Audio] Monetizing AI There are many ways to monetize AI, from developing custom models for clients to selling AI-powered products and services. Some popular approaches include: 1-Offering AI consulting services: Many businesses require AI experts to help them develop AI strategies and implement AI solutions. You can monetize your AI skills by offering consulting services to such businesses. 2-Developing AI-powered chatbots and voice assistants: Chatbots and voice assistants are becoming increasingly popular in customer service and support. You can develop AI-powered chatbots and voice assistants and sell them to businesses to improve their customer engagement and support. 3-Creating AI-powered content: You can use AI tools to create high-quality content quickly and efficiently. You can then monetize this content by selling it to businesses or by creating your own content-based products. Real-world examples:Grammarly: Grammarly is a writing assistant that uses AI to provide suggestions and corrections for grammar, spelling, and punctuation. The company has over 20 million users and generates revenue by selling premium subscriptions to its service..

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