Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/mla-3

Comparison of different data storage options when working with your ML models.

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/mla-2

Some numerical data nitty-gritty in Python.

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/mla-1

Reboot on the MLG episode, with more confident recommends.

Try a walking desk to stay healthy while you study or work!

Introduction to reinforcement learning concepts. ocdevel.com/mlg/29 for notes and resources.

Try a walking desk to stay healthy while you study or work!

Hyperparameters part 2: hyper-search, regularization, SGD optimizers, scaling. ocdevel.com/mlg/28 for notes and resources

Try a walking desk to stay healthy while you study or work!

Hyperparameters part 1: network architecture. ocdevel.com/mlg/27 for notes and resources

Try a walking desk to stay healthy while you study or work!

Community project & intro to Bitcoin/crypto + trading. ocdevel.com/mlg/26 for notes and resources

Try a walking desk to stay healthy while you study or work!

Notes and resources at  ocdevel.com/mlg/25 

• Filters and Feature Maps: Filters are small matrices used to detect visual features from an input image by applying them to local pixel patches, creating a 3D output called a feature map. Each filter is tasked with recognizing a specific pattern (e.g., edges, textures) in the input images.

• Convolutional Layers: The filter is applied across the image to produce an output which is the feature map. A convolutional layer is composed of several feature maps, with depth corresponding to the number of filters applied.

• Image Compression Techniques:

  • Window and Stride: The window is the size of the pixel patch examined by the filter, and stride determines how much the window moves over the image. Together, they allow compression of images by reducing the number of windows examined, effectively downsampling the image.
  • Padding: Padding allows the filter to account for border pixels that do not fit perfectly within the window size. 'Same' padding adds zero-padding to ensure all pixels are included, while 'valid' padding ignores excess pixels around the borders.

• Max Pooling: Max pooling is a downsampling technique used to reduce the spatial dimensions of feature maps by taking the maximum value over a defined window, further compressing and reducing computational load.

• Predefined Architectures: There are well-established predefined architectures like LeNet, AlexNet, and ResNet, which have been fine-tuned through competitions such as the ImageNet Challenge, and can be used directly or adapted for specific tasks in computer vision.

Try a walking desk to stay healthy while you study or work!

Notes and resources at  ocdevel.com/mlg/24 

Desktop if you're stationary, as you'll get the best performance bang-for-buck and improved longevity; laptop if you're mobile.

Desktops. Build your own PC, better value than pre-built. See PC Part Picker, make sure to use an Nvidia graphics card. Generally shoot for 2nd-best of CPUs/GPUs. Eg, RTX 4070 currently (2024-01); better value-to-price than 4080+.

For laptops, see this post (updated).

Use Linux (I prefer Ubuntu), or Windows, WSL2, and Docker. See mla/12 for details.

Deep-learning frameworks. You'll use both TF & PT eventually, so don't get hung up. mlg/9 for details.

1. Tensorflow (and/or Keras)
2. PyTorch (and/or Lightning)

Shallow-learning / utilities: ScikitLearn, Pandas, Numpy

Cloud-hosting: AWS / GCP / Azure. mla/13 for details.

The episode discusses setting up a tech stack tailored for machine learning, emphasizing the necessity of choosing a primary programming language and framework, which, in this case, are Python and TensorFlow. The decision is supported by the ongoing popularity and community support for these tools. This preference is further influenced by the necessity for GPU optimization, which TensorFlow provides, allowing for enhanced performance through utilizing Nvidia's CUDA technology.

A notable change in the landscape is the decline of certain deep learning frameworks such as Theano, and the rise of competitors like PyTorch, which is gaining traction due to its ease of use in comparison to TensorFlow. The author emphasizes the importance of selecting frameworks with robust community support and resources, highlighting TensorFlow's lead in the market in this respect.

For hardware, the suggestion is a custom-built PC with a powerful Nvidia GPU, such as the 1080 TI, running Ubuntu Linux for best compatibility. However, for those who favor cloud services, Amazon Web Services (AWS) and Google Cloud Platform (GCP) are viable options, with a preference for GCP due to cost and performance benefits, particularly with the upcoming Tensor Processing Units (TPUs).

On the software side, the use of Pandas for data manipulation, NumPy for mathematical operations, and Scikit-Learn for shallow learning tasks provides a comprehensive toolkit for machine learning development. Additionally, the use of abstraction libraries such as Keras for simplifying TensorFlow syntax and TensorForce for reinforcement learning are recommended.

The episode further explores system architectures, suggesting a separation of concerns between a web app server and a machine learning (job) server. Communication between these components can be efficiently managed using a message queuing system like RabbitMQ, with Celery as a potential abstraction layer.

To support developers in implementing their machine learning pipelines, the recommendation extends to leveraging existing datasets, using Scikit-Learn for convenient access, and standardizing data for effective training results. The author points to several books and resources to assist in understanding and applying these technologies effectively, ending with your own workstation recommendations and building TensorFlow from source for performance gains as a potential advanced optimization step.

Try a walking desk to stay healthy while you study or work!

Notes and resources at  ocdevel.com/mlg/23 

• Vanilla Neural Networks (Feedforward Networks):

  • Used for general classification or regression tasks.
  • Examples include predicting housing costs or classifying images as cat, dog, or tree.

• Convolutional Neural Networks (CNNs):

  • Primarily used for image-related tasks.

• Recurrent Neural Networks (RNNs):

  • Used for sequence-based tasks such as weather predictions, stock market predictions, and natural language processing.
  • Differ from feedforward networks as they loop back onto previous steps to handle sequences over time.

• Supervised vs Reinforcement Learning:

  • Supervised learning involves training models using labeled data to learn patterns and create labels autonomously.
  • Reinforcement learning focuses on learning actions to maximize a reward function over time, suitable for tasks like gaming AI but less so for tasks like NLP.

• Encoder-Decoder Models:

  • These models process entire input sequences before producing output, crucial for tasks like machine translation, where full context is needed before output generation.
  • Transforms sequences to a vector space (encoding) and reconstructs it to another sequence (decoding).

• Gradient Problems & Solutions:

  • Vanishing and Exploding Gradient Problems occur during training due to backpropagation over time steps, causing information loss or overflow, notably in longer sequences.
  • Long Short-Term Memory (LSTM) Cells solve these by allowing RNNs to retain important information over longer time sequences, effectively mitigating gradient issues.

• An LSTM cell replaces traditional neurons in an RNN with complex machinery that regulates information flow.
• Components within an LSTM cell:
  • Forget Gate: Decides which information to discard from the cell state.
  • Input Gate: Determines which information to update.
  • Output Gate: Controls the output from the cell.