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In this guided project, we will embark on an exciting journey through the MNIST digits dataset , one of the most influential benchmarks in the field of machine learning. Our goal is to develop a comprehensive understanding of image classification and master the art of recognizing handwritten digits from images. By utilizing popular deep learning framework TensorFlow, we will build and train convolutional neural networks (CNNs) to achieve high accuracy in classifying the dig

In today's data-driven world, extracting meaningful insights from the vast amount of information available is crucial for making informed decisions. When it comes to the financial domain, understanding sentiment in news articles can provide valuable insights for investors, analysts, and decision-makers. In this blog post, we delve into our latest guided project, where we leverage the power of Deep Learning and TensorFlow to build a sentiment classification model for financial

Zipped TensorFlow Sequential Animal Prediction Model Zipped Folder of Zipped Train and Zipped test Images used in Video Expanding our convolution network in this multi-class classification guided project with the Sequential model In TensorFlow with Python. Although you can get more complex with the Sequential model , training a model of this complexity takes a long long time, as in 4 hours with a GPU and those unfortunately those aren't free. Without a GPU it took me rough

Follow in this free-to-use Python TensorFlow guided project and build a deep learning model with the functional API in TensorFlow . Over the sequential model, the functional model in TensorFlow allows you to have multiple inputs and multiple outputs. This gives us a powerful technique in our deep learning architecture and is necessary for more advanced deep learning architectures. Here we will use the functional API in TensorFlow to see if we can get a better accuracy scor

TensorFlow is a power tool for building neural networks in many data science problems from tabular data and spreadsheets to NLP or text data and even images and photographs. TensorFlow has 3 available APIs to build your neural network Sequential API , Functional API , and the Subclassing Model API . These 3 API offer different levels of flexibility and ease of writing code. The Sequential API is the easiest to use and a great place to start for some new to TensorFlow. It'

create sequences for each next character Follow along with this Free Guided TensorFlow project. In this free Python Project, we will...

Download the folder for the files of the images of dessert used in the deep learning guided project. Zipped Folder with Images (202MB) Full Data Set of Food Images at Kaggle In this Simple Computer Vision guided project in TensorFlow using Python we will build a deep learning model to classify whether a dessert is an Apple Pie, French Toast, Donuts, or Cheesecake. In the near future, a company is building a machine to automate serving food to customers. The problem is that

In this simple TensorFlow multiclass prediction problem in Python we will be an NLP model and attempt to classify poems as Affection,...

In this simple TensorFlow Computer Vision Guided Project in Python we a convolution neural network or deep learning model to predict a binary classification problem and predict with an X-ray image containing a chest X-ray image with or without pneumonia. Medical Image classification is a common and important type of computer vision problem In future projects we will explore the many various types of image preprocessing available on our images to enhance our predictions. In t

In this Deep Learning TensorFlow Guided Project, we will use the LSTM recurrent layer along with an embedding layer and dense fully connected layer in Python to predict if the news Headlines are sarcastic or not. Follow along with me in this beginner simple TensorFlow deep learning project in Python. Learn to preprocess your data for NLP with TensorFlow. We will learn how to turn text into sequences and then how to pad these sequences so they are all the same size. After w

Continuing our LSTM stock prediction Python guided deep learning project. In our part 4 part of our Tensorflow Stock prediction project, we start with the functional API in Tensorflow . The functional API will allow us to ingest 3 different sources of data and extract higher-level features from each dataset before combining it with the next channel inside your neural network. We set up the workflow to allow for experiments and for you to further refine the architecture. I

Follow along with me and code your own Python Tensorfow Project. In this beginner deep learning classification project with Tensorflow's Sequential model. When we start designing the architecture for a deep learning model it is a good idea to set up a base model so we can evaluate our progress. Sklearn's RandomForestClassifier or GradientBoostingClassifier with the default setting work well as base models. Random Forest just gives good predictions in a few lines of cod

This Python computer vision project is a minimal level of difficulty to get computer vision to work well. Overfitting in computer vision, in part, has to do with our deep learning models learning the a shape it's recognized is in a certain position in the picture. To solve this we can use Tensorflow's ImageDataGenerator . This will require us to work with a Tensorflow dataset which can be a little tricky at first as it works differently than a Pandas DataFrame. The benefi

In our second part of the LSTM stock prediction using Tensorflow. Now that we have our deep learning model with LSTM recurrent layer...

In this Python deep learning project, we set up our LSTM neural network with Tensorflow in an attempt to get rich and predict the stock...

Follow along with me and code in Python using Pandas and Tensorflow. We will predict the TripAdvisor star rating of hotels from the description left in the review. In this project, the recurrent layer SimpleRNN was used but the LSTM or GRU layer can be used as well. We will use the text from the review to predict which star rating the left.

in the 3rd part of our stock market prediction using the LSTM layer to predict google stock. use TensorFlow and build a neural network to predict. in the project, we pick up where we left of and include economic data with our times series. we also focus on finding the right architecture for your neural network with TensorFlow in python. data science teacher brandyn YouTube Follow data science teacher brandyn facebook Template workbook Solution workbook One on one ti