https://view-awesome-table.com/-OM59PSExHseaHudfQYS/view

https://www.google.com/maps/d/edit?mid=1TlP4_1N1q6-qzChOPCC-DP5F_1kNfm0&usp=sharing

• Here is insights on our instance on Google Colab

https://colab.research.google.com/drive/1r642Knz3hICyYa4GXoNygle0gFvPG_dN?usp=sharing

We Unfolds Machine Intelligence And Now Computes Tensor Arrays in Machine Learning

• What We Only Did Earlier Runned out to be indentified information
• What We Now Envolve into is Calculation and Productions of Machine Intelligence Arrays and Machine Learning Algortihms with tensor arrays

ml.ipynb

Prepairing Calendar times with machine learning training dates

https://docs.google.com/spreadsheets/d/1Fqp3XOA2Y2OcaXUAr9h6At2Itd2rs6jeYdxTvNWNCB8/edit?usp=sharing

Google Colaboratory, or Colab, is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. It's a powerful tool for machine learning, data analysis, and education, offering access to free GPUs and TPUs.

• Free Cloud Environment: Run your code on Google's powerful servers without any setup.
• Jupyter Notebook Interface: Familiar and interactive coding environment.
• Free GPU and TPU Access: Accelerate your machine learning workloads.
• Easy Sharing: Share your notebooks with others via Google Drive.
• Pre-installed Libraries: Popular libraries like TensorFlow, PyTorch, Pandas, and NumPy are readily available.
• Integration with Google Drive: Seamlessly load and save data and notebooks from Google Drive.
• Command-Line Access: Execute shell commands directly within the notebook.
• Collaboration: Real-time collaboration similar to Google Docs.

• Machine Learning: Train and deploy machine learning models with GPU/TPU acceleration.
• Data Analysis: Perform data cleaning, transformation, and visualization.
• Education: Teach and learn programming and data science.
• Prototyping: Quickly test and iterate on code.
• Research: Conduct reproducible research and share results.

1. Access Colab: Go to colab.research.google.com.
2. Create a New Notebook: Click "New Notebook" to create a new notebook.
3. Write and Run Code: Write Python code in cells and run them.
4. Connect to a Runtime: To use GPU or TPU, go to "Runtime" > "Change runtime type" and select the desired hardware accelerator.
5. Install Libraries: Use !pip install library_name to install additional libraries.
6. Load Data: Load data from Google Drive, local files, or URLs.
7. Save Your Notebook: Your notebooks are automatically saved to Google Drive.

import tensorflow as tf

# Check for GPU availability
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

# Example TensorFlow code
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

predictions = model(x_train[:1]).numpy()
tf.nn.softmax(predictions).numpy()

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_fn(y_train[:1], predictions).numpy()

model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test,  y_test, verbose=2)

• Keyboard Shortcuts: Learn Colab's keyboard shortcuts for faster navigation and coding.
• Magic Commands: Use magic commands like %timeit for performance analysis.
• File Upload: Upload local files using the file browser on the left sidebar.
• Google Drive Integration: Mount Google Drive using from google.colab import drive; drive.mount('/content/drive').
• TensorBoard: Use TensorBoard for visualizing machine learning experiments.
• Formulas: Use Latex formulas inside of text cells.

• Google Colab Website: colab.research.google.com
• Colab FAQ: https://research.google.com/colaboratory/faq.html
• Colab GitHub: https://github.com/googlecolab/colabtools