TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow
This Edureka video provides you with a basic introduction to TensorFlow:
The fantastic deep learning framework by Google. Below are the topics covered in this video:
- What is TensorFlow?
- Companies using TensorFlow
- Features of TensorFlow
- What are Tensors?
- What are Neural Networks?
- TensorFlow Open Source Community
Complete Tensorflow Playlist: https://www.youtube.com/playlist?list…
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What Is TensorFlow?
TensorFlow is a popular open-source framework for machine learning and deep learning. It was developed by Google and released in 2015. TensorFlow allows users to create, train, and deploy neural networks and other models using a variety of programming languages, such as Python, C++, Java, and Go. TensorFlow also provides a high-level API called Keras, which simplifies the process of building and running models.
Deep Learning: A Professional Guide
Deep learning is a branch of machine learning that uses artificial neural networks to learn from large amounts of data and perform complex tasks. Deep learning has been applied to various domains, such as computer vision, natural language processing, speech recognition, etc.
What is Deep Learning?
Deep learning is a subset of machine learning, a field of artificial intelligence that aims to create systems that can learn from data and make decisions or predictions. Machine learning algorithms can be divided into two categories: supervised and unsupervised. Supervised learning algorithms learn from labeled data, meaning that the data has some desired output or target value. For example, a supervised learning algorithm can learn to classify images of cats and dogs by using a dataset of pictures that are labeled as either cat or dog. Unsupervised learning algorithms learn from unlabeled data, meaning the data has no predefined output or target value. For example, an unsupervised learning algorithm can learn to cluster similar images together by using a dataset of pictures that are not labeled.
Deep learning is supervised learning that uses artificial neural networks to learn from data. Artificial neural networks are composed of layers of interconnected nodes called neurons, inspired by the brain’s biological neurons. Each neuron receives input from the previous layer, performs some computation, and produces some output to the next layer. The input layer receives the raw data, such as an image or a text, and the output layer has the final prediction or decision, such as a label or a score. The layers between the input and output layers are called hidden layers, and they are responsible for extracting features and patterns from the data.
How Does Deep Learning Work?
Profound learning works by adjusting the weights and biases of the neurons in the network based on the error between the predicted output and the actual output. The error is calculated using a loss function, which measures how well the network performs on a given task. The process of minimizing the loss function is called optimization, which is done using an algorithm called gradient descent. Gradient descent updates the weights and biases of the network by moving them in the opposite direction of the gradient of the loss function, which is the direction that leads to the steepest decrease in the loss function.
The network learns from data using a technique called backpropagation, which propagates the error from the output layer to the input layer and updates the weights and biases accordingly. Backpropagation consists of two steps: forward propagation and backward propagation. In forward propagation, the network computes the output for a given input by passing it through each layer of neurons. In backpropagation, the network calculates the error for each neuron by comparing the work with the actual output and then adjusts the weights and biases using gradient descent.
What are Some Applications of Deep Learning?
Deep learning has been used for various applications in different domains, such as:
- Computer vision: Deep learning can perform tasks such as face recognition, object detection, scene segmentation, image generation, and more. For example, deep learning can recognize faces in photos or videos using a convolutional neural network (CNN). This neural network can process images efficiently using filters that detect edges, shapes, colors, and other features.
- Natural language processing: Deep learning can perform tasks such as text classification, sentiment analysis, machine translation, text summarization, question answering, and more. For example, deep learning can translate text from one language to another using a recurrent neural network (RNN), which can process sequential data such as text or speech using loops that can store information from previous inputs.
- Speech recognition: Deep learning can perform speech recognition, speech synthesis, speech enhancement, speaker identification, and more. For example, deep learning can recognize speech in audio or video by using a long short-term memory (LSTM) network, a type of RNN that can handle long-term dependencies and avoid forgetting important information.
Deep learning is a powerful and versatile technique that can learn from large amounts of data and perform complex tasks. Deep learning uses artificial neural networks to learn from data and produce predictions or decisions. Deep learning works by adjusting the weights and biases of the neurons in the network based on the error between the predicted output and the actual output. Deep learning has been applied to various domains, such as computer vision, natural language processing, speech recognition, etc.